# AcademicFellows is becoming part of Antlect
Source: https://docs.antlect.com/academicfellows
Why AcademicFellows is becoming part of Antlect, and what that means for researcher and capability discovery.
We started AcademicFellows with a simple question: how do we make research expertise and capability easier to discover?
The question turned out to be the surface of a much older one.
Knowledge is the one asset that compounds. Every discovery is built on the ones before it, and every decision about where to direct effort, money and people shapes what becomes possible next. Yet those decisions are made with almost no view of the system they are shaping.
We fund without seeing what already exists. We build capability without knowing what capability is already there. We set direction for decades using evidence assembled in weeks.
This is not a failure of intelligence or intent. It is that the knowledge required to decide well has never been assembled into a form anyone can reason with.
That is the problem Antlect exists to solve.
## What Antlect is
Antlect is a decision intelligence platform for research and innovation. Our vision is to connect the whole innovation ecosystem into a single structure: research, funding, organisations, publications, infrastructure, patents, clinical trials, spinouts, technology startups and policy.
Connected, it becomes a feedback loop. The people building the future and shaping society can see the system they are shaping.
## What happens to AcademicFellows
AcademicFellows remains part of this as the [research collaboration](/academicfellows/research-collaboration) experience, built on the Antlect foundation.
## What this means in practice
The four layers Antlect is built on, and what each one contributes to an answer.
What sits in the Trusted Data Foundation today.
The company behind the platform is now **Antlect Limited**, formerly AcademicFellows Limited. Our [Privacy Policy](/legal/privacy-policy), [Terms of Service](/legal/terms-of-service) and [Trust Centre](/trust-centre) name the new entity; nothing about how data is handled changes with it.
# AcademicFellows Access
Source: https://docs.antlect.com/academicfellows/access
How to access AcademicFellows freely through your own institutional identity, and why it matters
Trust and identity are central to research collaboration, and so is
seamless integration with the access systems institutions already run.
AcademicFellows uses that same identity. There is no second account to
create, no separate password to remember, and no approval queue to sit
in.
AcademicFellows is a service provider under REANNZ's federated identity
system.
Here is how REANNZ explains this service and why it matters for
research: "Tuakiri allows an end-user to consume services, access
resources, and otherwise collaborate within New Zealand while using
their home institution's identity. This means that the end-user does
not require a unique identity with every remote organisation they are
working with, simplifying the workflow of both the end-user, and the IT
staff as the service being accessed gets trusted identity that has been
verified by the home organisation."
## Sign in where you already do
When you click Sign in, you will be asked to choose your institution.
This takes you to your institution's own login page for authentication,
where you consent to sharing your name, email address, and institution.
Your password credentials never reach AcademicFellows at all, and that
is the whole point: it is what makes the system secure and trusted.
## Why built on REANNZ's federated identity system
Under Antlect, AcademicFellows is a research collaboration platform, and
access is managed as a service provider under REANNZ, the organisation
that runs the identity infrastructure connecting universities, Crown
Research Institutes, and research organisations across the country. It
is the same infrastructure those institutions rely on to protect their
own systems, and AcademicFellows sits inside that same circle of trust
rather than beside it.
Your affiliation on AcademicFellows is not something you typed into a
form, and it is not something we assign. It was asserted by your own
primary institution, and AcademicFellows grants access on that basis.
## Why it matters
* **No one can claim to be you.** An identity can only be maintained by
the identity provider, which is your primary institution, so
impersonation has no route in.
* **No fake or automated accounts sit alongside real ones.** There is no
open signup for them to come from.
* **Your standing here stays accurate.** When someone's institutional
relationship ends, so does their access, without AcademicFellows having
to track it separately.
Reliable identity is what makes a research network worth trusting in the
first place, and REANNZ's infrastructure is what makes that possible
here without asking you to do anything extra. It is the gold standard
for accessing research infrastructure.
## Who can use it
Anyone with access to a participating New Zealand university, Crown
Research Institute, or research organisation:
* Academic staff and faculty
* PhD candidates and postdoctoral researchers
* Research fellows and scientists
* Alumni and researchers who retain institutional credentials
Access is free for individual researchers.
## What happens on first sign-in
You will be asked to consent to sharing your name, email address, and
institution. Your public research profile is then created from your
public research record on Antlect's data foundation: the concepts you
work on, your affiliations, collaborations, and outputs become part of
your profile. Nothing is published that was not already in the public
domain.
If you have any issue connecting through your institutional ID via
Tuakiri REANNZ, write to
[support@academicfellows.com](mailto:support@academicfellows.com) and
the team will talk through the options.
## Coverage
AcademicFellows is a service provider under REANNZ's Tuakiri, and it
currently supports New Zealand institutions. We are working to extend
access to the Australian research ecosystem.
Institutional sign-in, plus access routes for organisations without an
academic affiliation.
# Research Collaboration
Source: https://docs.antlect.com/academicfellows/research-collaboration
AcademicFellows, the research collaboration platform built on Antlect's Trusted Data Foundation: what it makes visible, and who it is for.
AcademicFellows is a research collaboration platform built on Antlect's Trusted Data Foundation, the governed body of research and innovation data behind the Antlect platform. AcademicFellows draws on a focused subset of it: the slice needed to make research capability and collaboration visible.
It answers one question well: who is doing what research, where, and with whom.
## What it makes visible
Research capability is spread across institutions that each hold only their own part of it. AcademicFellows maps the connections that cross those boundaries:
* **Researchers**, and the concepts they actually work on
* **Research groups and laboratories**, the working units where research happens
* **Departments and institutions**, and how a capability is distributed across them
* **Collaborations**, including the ones that cross institutional lines
* **Outputs and funding**, connected to the people and groups behind them
The result is not a list of records. It is a map of who is working on what, and who they are working with.
## Knowledge profiles
Every researcher has a knowledge profile, built from their public research record. It reflects the concepts they work on, their affiliations, collaborations, outputs, and funding.
A profile shows not only what someone has published, but what they work on, who they work with, and how their expertise connects to the wider research community. Nothing in a profile was not already public.
## Depth rather than breadth
AcademicFellows follows a regional-first approach: complete coverage of an ecosystem before expanding into a new one.
Across Australasia that means every university, research institute, department, research group, and laboratory, together with the funding programmes and registered intellectual property connected to them. Not a sample, and not a proxy.
That depth is not a limitation. For anyone outside the region, it offers a close view of one of the world's more distinctive research ecosystems.
## How the data is built
AcademicFellows is not a repository or a data aggregator. The records behind it are processed rather than stored in raw form.
Data arrives from open repositories, shared registries, and curated public sources, where it is fragmented and inconsistent. Antlect's [Trusted Data Foundation](/platform/overview) resolves it: records are reconciled into consistent entities through entity resolution, concepts and relationships are extracted, and everything is placed against Antlect's [semantic ontology](/platform/semantic-ontology-layer) so that a concept carries the same meaning across disciplines.
AcademicFellows draws the research relationship layer from that foundation. The wider innovation ecosystem, and the decision intelligence built on it, sit with the [Antlect platform](/platform/overview).
## Who it is for
* **Researchers** looking for collaborators, adjacent expertise, or relevant funding
* **Research offices** that need a view of capability across their own institution and beyond it
* **Industry partners** seeking research collaborators for a specific problem
Access is free for individual researchers.
Sign in with your institutional identity through REANNZ's federated identity system, and why access is built that way.
The platform itself.
# Requesting access
Source: https://docs.antlect.com/account/access-requests
Antlect is available to approved organisations. Here's how to get in.
Access to Antlect is granted per organisation, reviewed by an administrator.
## Submitting a request
1. On the Antlect landing page, choose **Book a Demo** / request access.
2. Fill in your name, work email, and organisation, and briefly describe your use case.
3. Submit. Your request enters the review queue.
## What happens next
* An administrator reviews the request, typically within a few business days.
* **Approved**: you receive an email confirming your account is active, and you can [sign in](/account/authentication) immediately.
* **More information needed**: the team may reach out to your work email first.
## While you wait
Attempting to sign in before approval will not create an account. If it's been more than a few business days, [contact support](/account/support) referencing the email address you applied with.
# Authentication
Source: https://docs.antlect.com/account/authentication
How signing in to Antlect works.
Antlect uses secure single sign-on for all access. There are no passwords stored by Antlect itself.
## Signing in
1. Click **Login** on the Antlect landing page.
2. You're redirected to the secure login screen to authenticate.
3. On success you're returned to your workspace, signed in.
Sessions are token-based and expire automatically; you'll simply be asked to sign in again.
## Accounts and roles
Every account has a role:
| Role | Capabilities |
| ----- | ----------------------------------------------------------------------------------------------------------------------- |
| User | Full platform access: projects, queries, reports, library |
| Admin | Everything a user can do, plus the [admin dashboard](/admin/admin-dashboard): access-request review and user management |
## Signing out
Sign out from the account menu in the sidebar. This ends your session everywhere the token is used.
If your organisation hasn't been granted access yet, see [Requesting access](/account/access-requests).
# Support
Source: https://docs.antlect.com/account/support
Get help from the Antlect team.
## In-app support
The fastest route is the **Help** screen inside the app. Describe the issue and submit, and your message goes straight to the Antlect team by email, tagged with your account, and replies come back to your work email.
When reporting a problem, include:
* What you were doing (the query or report involved)
* What you expected, and what happened instead
* Roughly when it happened
## Email
You can also reach the team directly at **[support@antlect.com](mailto:support@antlect.com)**.
## Guides and updates
* The **Guide** section inside the app covers the same workflows as these docs, in context.
* The [Changelog](/changelog) lists every notable change to the platform.
# Admin dashboard
Source: https://docs.antlect.com/admin/admin-dashboard
Review access requests and manage users. Admin role required.
The admin dashboard is available to accounts with the **admin** role. It's where organisation access is granted and users are managed.
## Access requests
New access requests from the public landing page arrive here for review. For each request you can see the applicant's name, work email, organisation, and stated use case.
* **Approve**: the applicant's account is activated and they can sign in immediately.
* **Reject**: the request is closed; no account is created.
Requests are timestamped and retain their review outcome, so there's an auditable record of who was granted access and when.
## User management
The dashboard lists all users with their role and status. Admins can:
* Promote a user to **admin** or return an admin to **user**.
* See when each account was created and linked identity details.
Admin actions take effect immediately. Demoting the only remaining admin would lock the dashboard, so keep at least two admin accounts.
# Changelog
Source: https://docs.antlect.com/changelog
What's new in Antlect: the capabilities we've shipped, in the order we shipped them.
Antlect was built in layers, and this page is the record of it. The research graph was laid down first, in February 2026. Publications, patents, funding, and clinical trials were connected to it through the first half of the year. The query engine, the workspace, and research briefs were built on that foundation once it could carry them.
Read from the bottom for how the platform was assembled, or from the top for what changed most recently. For how the layers fit together, see the [platform architecture](/platform/overview).
## Platform security audit
A dedicated security review across the entire API surface. Every route now enforces request-level authentication and verified workspace isolation, rate limiting applies platform-wide with stricter limits on public-facing endpoints, all rich-text input is sanitised before storage, and error handling was tightened so internal detail never reaches a client.
[Read more →](/trust-centre)
## Open and forthcoming funding calls
Funding intelligence now extends beyond the historical record: Antlect tracks live Horizon Europe funding calls, refreshed from the official EU source. The results panel gained **Open Calls** and **Forthcoming** tabs, so current and upcoming opportunities sit alongside the research landscape they relate to.
[Read more →](/guides/data-coverage)
## Brief drafts
Generate a focused brief draft from any item: an artifact, a document, or a mind map. Drafts are generated for you, tied to the item they came from, and kept in the Library's Drafts tab to refine and reuse.
[Read more →](/guides/reports)
## Mind maps live in your workspace
Mind maps are now first-class workspace content. Maps save as editable data (reopen one and continue where you left off, rather than looking at a static image) with autosave while you work, a full-map PNG download for sharing outside Antlect, and a dedicated **Mind Maps** tab in the Library.
[Read more →](/guides/history-library)
## A mind map editor designed for Antlect
The mind map editor received its own interface, tuned for structuring an investigation quickly: per-branch colours with recolouring from the context menu, three layout directions that persist across sessions, a floating title, and unobtrusive zoom controls that stay out of the map's way.
## Account settings and your personal audit trail
Settings is organised into **Profile**, **Security**, and **Preferences**. Manage your profile, choose where you land after sign-in, and, under Security, review your account's audit trail: key content actions and password reset requests, logged with what happened and when.
[Read more →](/trust-centre)
## See the network behind every answer
Results gained a **Graph mode**: an interactive, force-directed network of the people, universities, faculties, and research institutes behind your answer. Nodes are sized and coloured by entity type; search highlights matches while the rest fades back, a type filter narrows the view, and clicking a node opens its detail panel. Pop the graph out into a floating panel to keep it visible while you work, or expand a node to fetch its wider neighbourhood on demand.
[Read more →](/guides/network-graph)
## Switch chart types in place
Chart blocks in the report editor are fully switchable: pick from five types (horizontal bar, vertical bar, line, area, and pie) and when a dataset has multiple numeric fields, choose which one the chart plots. Switching is instant and presentational only; the underlying data is untouched.
[Read more →](/guides/charts)
## Antlect goes public
A public landing page introduces the platform, with sign-in and demo booking. Existing users go straight to **Login** from the top navigation.
## Mind maps arrive
A new way to think inside Antlect: sketch the structure of an investigation as a mind map (branching out entities, questions, and findings) alongside your threads and reports, in the same project.
## Collections in the Library
Group related artifacts into named collections, so the findings for one line of enquiry stay together and retrievable. Library search now spans every tab: artifacts, files, drafts, and mind maps.
[Read more →](/guides/history-library)
## Workspace overhaul
* **Inline query runs**: queries execute directly in the thread panel with a live step trace and timing.
* **Global search** across the project (threads, reports, and tags) from the search pill at the top of the workspace.
* **Tag management**: rename tags in place and reassign an item's tag on hover.
* **Artifact selection**: tick artifacts in results and act on the whole selection from the selection bar.
* **Redesigned sidebar**: a compact collapsible rail that keeps more room for content.
Projects, threads, reports, drafts, files, collections, and history are all persisted server-side through a full workspace API.
[Read more →](/guides/workspace)
## The research brief editor
Research briefs became a full rich-text environment: headings, tables, task lists, images, links, highlights, character count, and autosave, with PDF export rendered server-side, so the document you deliver looks the way you wrote it.
[Read more →](/guides/reports)
## Ask questions across the graph
The query engine arrived, and with it the Discovery Terminal: pose a research question in plain language and tune how it's answered: the **dimension** the answer is organised around (people, institutions, funding bodies) and the **metrics** it's ranked by (publication output, citation impact, funding volume, collaboration breadth). Completed queries return structured result sets with **Summary**, **Publications**, and **Patents** views, and every run keeps an inspectable step-by-step trace in its thread.
[Read more →](/guides/running-queries)
## Secure access, roles, and support
The platform's access model shipped end to end: secure single sign-on with no passwords held by Antlect, role-based access separating users from administrators, access requests reviewed individually before an account is activated, and support requests raised from inside the app and delivered directly to the team.
[Read more →](/account/authentication)
## Clinical trial coverage
Clinical trial activity joined the graph, connected to the researchers and institutions running it. Trials are the clearest translational signal, where research is moving towards practice, and they now inform the same queries and results as the rest of the graph.
[Read more →](/guides/data-coverage)
## Funding coverage
Funding bodies and grant records were connected to the researchers and institutions they back. Funding volume became a rankable metric in queries, and an entity's funding history now appears alongside its publication and patent record.
[Read more →](/guides/data-coverage)
## Patent coverage
Patent activity joined the graph: filings, assignees, and inventors, linked to the researchers and institutions behind them. Patents reveal commercialisation intent that publication data alone misses, and they surface both in the Patents view of results and as signals in the Summary.
[Read more →](/guides/data-coverage)
## Publication coverage
Scholarly publications were linked into the graph: papers, authors, venues, and dates, connected to the people and institutions behind them. Publication output and citation impact became rankable metrics, and every answer can be verified against its underlying publication record.
[Read more →](/guides/data-coverage)
## The research graph
The foundation of Antlect, and the beginning of the Descriptive Digital Twin: a connected graph of researchers, universities, faculties, and research institutes across New Zealand and Australia. People and institutions are first-class entities with real relationships between them, not rows in separate datasets, and every capability that followed is built on this graph.
[Read more →](/guides/data-coverage)
# Core concepts
Source: https://docs.antlect.com/concepts
Projects, queries, threads, research briefs, and artifacts: how Antlect organises your work, and how they map to the platform layers.
Antlect is organised around a small number of concepts that compose together. Understanding them makes everything else in the platform predictable.
These are the concepts you work with inside the [Decision Intelligence Terminal](/platform/decision-intelligence-terminal), the top layer of the [platform architecture](/platform/overview). If you have read about Antlect on the website, this table connects the two vocabularies:
| On antlect.com | In the app, and in these docs |
| ------------------------ | ------------------------------------------------ |
| Discovery Terminal | Running a query in a thread |
| Knowledge Management | History and Library |
| Research Brief | A report, written in the report editor |
| Descriptive Digital Twin | Graph mode, and the entities behind every result |
| Trusted Data Foundation | The data coverage behind an answer |
## Projects
A **project** is the top-level container for an investigation. It holds every query you run, every thread of analysis, and every report you write on a topic. Projects are private to you and appear in your sidebar for quick switching.
Use one project per line of enquiry: for example, one project for a sector scan and a separate one for a due-diligence deep dive.
## Queries
A **query** is a research question posed in plain language. When you run a query, Antlect analyses it across its knowledge of researchers, institutions, funding, publications, patents, and clinical trials.
Queries can be **tuned** before running:
* **Dimension**: the primary entity type you want the answer organised around (people, institutions, funding bodies).
* **Metrics**: how results are ranked and measured (publication output, citation impact, funding volume, collaboration breadth).
## Threads
Every query runs inside a **thread**: a live, inspectable trace of the analysis. The thread panel shows each step as it executes, with timing, and keeps the full history of runs in the project so you can revisit how an answer was produced.
## Results
A completed query produces a structured **result set** with multiple views:
| View | What it shows |
| ------------ | -------------------------------------------------------------------------- |
| Summary | A synthesised answer: the key entities, relationships, and findings |
| Publications | The publication record underlying the answer |
| Patents | Related patent activity |
| Graph mode | The same entities as an interactive [network graph](/guides/network-graph) |
## Artifacts
Individual findings inside a result (a ranked table, a chart, an entity profile) are **artifacts**. Artifacts can be selected and saved to the [Library](/guides/history-library) or placed directly into a report, so a finding you generated once stays reusable.
## Mind maps
A **mind map** structures an investigation visually, branching entities, questions, and findings alongside your threads and briefs in the same project. Maps save as editable data, so you reopen one and keep working rather than looking at a static picture.
## Research briefs
A **research brief** is the deliverable, and what the app calls a report. The editor is a full rich-text environment with headings, tables, task lists, images, links, and [interactive chart blocks](/guides/charts). Briefs live inside their project, autosave as you write, and export to PDF.
## How they fit together
```
Project
├── Threads (live query runs, step-by-step traces)
│ └── Queries → Results (Summary / Publications / Patents / Graph)
│ └── Artifacts (saved findings)
└── Research briefs (rich-text documents with charts, exported to PDF)
```
A good rhythm: run queries in threads until the picture is clear, save the artifacts that matter, then assemble them into a brief at the end.
# Working with charts
Source: https://docs.antlect.com/guides/charts
Five chart types, switchable in place, with your choice of data field.
Chart blocks in reports are live visualisations, not images. You control how each chart displays its data directly from the block itself.
## Chart types
Every chart block supports five display types:
| Type | Best for |
| ---------------- | --------------------------------------------------------- |
| Bar (horizontal) | Ranked comparisons with long labels: institutions, people |
| Bar (vertical) | Compact category comparisons |
| Line | Trends over time |
| Area | Trends where cumulative volume matters |
| Pie | Share-of-total breakdowns with few categories |
## Switching chart type
1. Hover over a chart block. A grip handle appears at its left edge.
2. Click the handle to open the chart options popover.
3. Pick a chart type: the block re-renders instantly in place.
The switch is purely presentational: the underlying data is untouched, so you can flip between types freely while deciding what reads best.
## Choosing the data field
When a dataset has more than one numeric field, the popover also shows a **data field selector**. Pick which field the chart plots: for example, switching a funding chart between *total awarded* and *grant count*. With a single numeric field, the selector stays out of the way.
## Charts in exports
Charts render into the PDF export exactly as configured in the editor: the type and data field you chose are what readers see.
Horizontal bars are the safest default for entity rankings: names stay readable at full length instead of being rotated or truncated under a vertical axis.
# Data coverage
Source: https://docs.antlect.com/guides/data-coverage
What sits in Antlect's Trusted Data Foundation: researchers, institutions, publications, patents, clinical trials, and funding, connected as a single research graph.
Antlect's answers are produced from the **Trusted Data Foundation**, the first layer of the [platform architecture](/platform/overview). It is a connected research graph holding more than 100 million data points, not separate datasets reconciled at the moment a question is asked.
Six entity types live in the graph, each linked to the others, so a question about any one of them draws on all of them. Records are integrated, validated, and resolved continuously, which is why a researcher is a single entity across their publications, patents, trials, and grants rather than a separate record in each.
Coverage is deepest across **New Zealand and Australia**, where the objective is a picture complete enough to make decisions on rather than a thin global index. Live funding calls are tracked from the European source directly.
## Researchers
The people behind the work, with their titles, departments, and institutional affiliations. Researchers are connected to everything they produce: publications, patents, trials, and the funding behind them. In queries, people are a primary **dimension**, and metrics like publication output, citation impact, and collaboration breadth rank them.
## Institutions
Universities, faculties, and research institutes, modelled as real organisational structures rather than flat name strings. Institutional queries answer questions like where a capability is concentrated, which faculties collaborate across organisations, and how a research strength is distributed across the region.
## Publications
The scholarly record: papers, authors, venues, and dates, linked to the people and institutions behind them. Publications underpin the **Publications** view of every result set, and they're the evidence trail: any claim in a Summary can be verified against the record that produced it.
## Patents
Patent activity (filings, assignees, and inventors) connected to the researchers and institutions behind them. Patents reveal commercialisation intent that publication data alone misses, and they surface both in the **Patents** view of results and as signals in the Summary.
## Clinical trials
Clinical trial activity, connected to the researchers and institutions running it. Trials are the clearest translational signal in the graph, where research is moving from the lab towards practice, and they carry weight in questions about applied capability.
## Funding
Two layers of funding intelligence:
* **The funding record**: funding bodies and grant records connected to the researchers and institutions they back, making funding volume a rankable metric in queries.
* **Live funding calls**: open and forthcoming Horizon Europe calls, refreshed from the official EU source and shown in the **Open Calls** and **Forthcoming** tabs of the results panel.
## How coverage shows up in your work
| You're looking at | The graph behind it |
| ------------------------ | ----------------------------------------------------------- |
| A Summary's key entities | Researchers and institutions, ranked by your chosen metrics |
| The Publications tab | The publication record underlying the answer |
| The Patents tab | Related patent families, assignees, and inventors |
| Graph mode | All connected entities, as an interactive network |
| Open Calls / Forthcoming | Live funding call tracking |
The connections matter more than the counts. Because every entity type links to the others, a single query can move from a capability, to the people who hold it, to who funds them, to whether it's heading towards market, without changing tools.
# History & Library
Source: https://docs.antlect.com/guides/history-library
Every query you've run, and every artifact you've kept.
Two views keep your past work retrievable: **History** for queries, **Library** for saved artifacts. Together they are Antlect's **Knowledge Management**, the reason a finding produced once stays available to the next decision rather than being rediscovered.
## History
History lists every query you've run across all projects, most recent first. Each entry shows the query text, the project it ran in, and when it ran. Click an entry to reopen its results.
Use History when you remember asking something but not where: it's the cross-project index of your questions.
## Library
The Library holds the artifacts you've deliberately saved (ranked tables, charts, entity profiles) independent of the reports they may appear in.
* **Search** the library by name or content using the search bar.
* **Open** any artifact to see it in full, with the link back to the query that produced it.
* **Reuse** artifacts in new reports without re-running the original query.
Saving to the Library is how you make a finding durable. Results views are regenerated per query; the Library is your permanent shelf.
# Network graph
Source: https://docs.antlect.com/guides/network-graph
Explore any result set as an interactive map of researchers, institutions, and their connections.
Graph mode turns a result set into a live, force-directed network of the entities behind your answer: the people, universities, faculties, and research institutes, connected by affiliation and collaboration.
It is where the [Descriptive Digital Twin](/platform/overview) becomes visible. Every node and edge resolves to a record in the data beneath it, so the structure you are reading is the ecosystem as recorded, not a rendering of it.
## Opening the graph
From any results view, click the **Graph mode** toggle at the right end of the tab bar. The graph replaces the tab content in place; clicking any tab returns you to the standard views without losing the graph's state for that query.
## Reading the graph
Each node is an entity from your result. Nodes are sized and coloured by type, with the entity's initials inside the circle and its name below:
| Node type | Meaning |
| ------------------ | ----------------------------------------------- |
| Person | An individual researcher |
| University | A university |
| Faculty | A faculty or school within a university |
| Research Institute | An independent or affiliated research institute |
| CoRE | A Centre of Research Excellence |
Edges show relationships: **affiliation** (a person belongs to an organisation) and **collaboration** (entities that work together).
The layout is physical: tightly connected clusters pull together, bridges between communities stretch out. You can drag any node to reposition it, scroll to zoom, and drag the background to pan.
## Controls
The control bar above the graph gives you:
* **Search**: type a name or keyword; matching nodes stay at full strength while the rest of the graph fades back, so matches stand out without losing context.
* **Type filter**: restrict the view to one entity type (for example, only People) to simplify a dense graph.
* **Reset**: clears search, filter, and zoom back to the default view.
* **Pop out**: detaches the graph into a floating panel you can drag anywhere in the window, useful for keeping the graph visible while you read the tab views or write a report.
## Node details
Click a node to select it. A detail panel opens below the graph with the entity's profile (title, department, and institution for a person; keywords and type for an organisation) so you can identify an entity without leaving the graph.
Selected nodes get a highlight ring; hovering previews a node the same way.
## Expanding the graph
Some nodes can be **expanded** to pull in their wider neighbourhood: for example, expanding a research institute fetches the people affiliated with it that weren't part of the original answer. An expanding node shows a loading ring while its connections are fetched.
Use search and the type filter together: filter to People, then search a surname to find one researcher inside a large collaboration cluster instantly.
# Building research briefs
Source: https://docs.antlect.com/guides/reports
Turn findings into a research brief: a polished, shareable document with live charts and PDF export.
A **research brief** is where an investigation becomes a deliverable, and what the app calls a report. Each project can hold several, and every one autosaves as you write.
For how a brief fits into the wider arc from an open question to a grounded position, see the [Decision Intelligence Terminal](/platform/decision-intelligence-terminal).
## Creating a report
From the project workspace, open the **Reports** tab and create a new report. Reports open in a full-page editor with the document on a clean stage and formatting controls at hand.
## The editor
The report editor is a complete rich-text environment:
* **Structure**: headings, paragraphs, bulleted and numbered lists, task lists with checkboxes.
* **Tables**: full table support with header rows, and per-cell editing.
* **Media**: images and links inline in the document.
* **Emphasis**: bold, italic, underline, highlight, and text alignment.
* **Character count**: visible as you write, useful for length-constrained deliverables.
Everything autosaves; there is no save button to remember.
## Chart blocks
Reports can embed **live charts** rendered from your data, not screenshots. Charts are interactive in the editor and render crisply in the exported PDF.
Chart types, switching between them, and choosing the data field.
## Bringing in artifacts
Artifacts saved from [results](/guides/results) (tables, charts, entity summaries) can be placed into a report, keeping the link back to the query that produced them. This is the fastest way to assemble a report: run queries, save artifacts, arrange them into a narrative.
## Exporting to PDF
When the report is ready, export it to PDF. Antlect renders the document server-side, including charts and tables, into a print-quality PDF, stored with the project so past versions remain retrievable.
Draft the narrative in the Summary language of your queries: it's written to be lifted. Then add chart blocks where a number needs to be seen rather than read.
# Reading results
Source: https://docs.antlect.com/guides/results
The Summary, Publications, and Patents views of a completed query, and how to work with them.
When a query finishes, its results open in a structured view with a tab bar across the top: **Summary**, **Publications**, and **Patents**, plus a **Graph mode** toggle at the right end of the bar.
## Summary
The Summary is the synthesised answer to your question. It presents:
* **Key entities**: the researchers, institutions, and funders that matter for your question, with the context that makes them relevant.
* **Findings**: the relationships and signals the analysis surfaced, written as prose you can lift directly into a report.
* **Ranked tables and charts**: where the question implies a ranking or a trend, the Summary includes it as an artifact.
## Publications
The Publications tab shows the publication record underlying the answer: papers, authors, venues, and dates. Use it to verify a claim from the Summary or to drill into a specific researcher's output.
## Patents
The Patents tab shows related patent activity (filings, assignees, and inventors), which often reveals commercialisation intent that publication data alone misses.
## Graph mode
At the right end of the tab bar is the **Graph mode** toggle. It switches the same result set into an interactive network graph of the entities and their connections, in the same space the tabs occupy. The tab views and the graph are two lenses on one result, and switching between them never loses your place.
Full guide to navigating, filtering, and expanding the graph.
## Selecting artifacts
Findings inside results (tables, charts, entity profiles) are selectable **artifacts**. Tick the artifacts you want to keep and use the selection bar to save them to the [Library](/guides/history-library) or send them into a [report](/guides/reports).
Save artifacts as you read rather than at the end. The selection bar keeps a running count, and anything saved stays attached to the query that produced it.
# Running queries
Source: https://docs.antlect.com/guides/running-queries
Compose a research question, tune its dimensions and metrics, and watch it run live.
Queries are how you ask Antlect questions, and the working surface of the **Discovery Terminal**. This guide covers composing a query, tuning it, and following its execution in the thread panel.
Questions are interpreted through the [Semantic Ontology Layer](/platform/semantic-ontology-layer), which is why plain language works and why results include records that never used your wording.
## Composing a query
Start a new query from the thread panel inside any project. Write the question the way you'd ask a colleague:
> Which research institutes in Australia have growing patent activity in battery chemistry, and who are their key people?
You don't need special syntax: Antlect interprets the entities, geography, and intent from natural language.
### What makes a good query
* **Name the domain**: "photonics", "battery chemistry", "mRNA delivery", rather than a broad field like "science".
* **Say what you want ranked**: people, institutions, funders, so the answer is organised the way you'll use it.
* **Include geography if it matters.** Antlect's coverage is deepest for New Zealand and Australia.
## Tuning a query
Before running, you can open the tuning controls to shape the analysis:
The primary entity type the answer is organised around. Choose **People** for a who's-who, **Institutions** for a capability map, or **Funding** to follow the money.
How results are ranked: publication output, citation impact, funding volume, collaboration breadth. You can combine metrics; the Summary view explains how each entity scored.
## Watching a query run
Queries execute inside the thread panel, in place: you never leave the workspace. As the analysis proceeds you'll see:
1. **Step trace**: each stage of the analysis appears as it starts, with elapsed timing.
2. **Expandable steps**: click a step to see what that stage examined.
3. **Completion**: the thread card links to the full results view when done.
Past runs stay in the thread, so a project accumulates a visible history of how its answers were produced.
## Re-running and iterating
Queries are cheap to iterate. If the Summary shows the question was too broad, refine the wording or narrow the dimension and run again: each run is kept as its own entry in the thread, and nothing is overwritten.
Treat threads like a lab notebook: keep the exploratory runs. When a result is worth keeping, save its artifacts to the Library or a report rather than trying to reproduce it later.
## Next steps
Navigate the Summary, Publications, and Patents views.
See the same answer as a map of people and institutions.
# The Antlect Spinout Tracker
Source: https://docs.antlect.com/guides/spinout-tracker
A record of spinouts from research institutions, beginning with Australia and New Zealand, each resolved against the core technology it is built on.
The Antlect Spinout Tracker records spinouts from research institutions, beginning with Australia and New Zealand, together with the core technology behind each one.
Spinout formation sits along the knowledge translation spectrum, at the point where research moves into a business and produces economic or social outcomes. What makes it worth examining systematically is that formation is unevenly distributed. A small number of institutions account for a disproportionate share of it, and that concentration is not explained by research volume alone. Accounting for it requires knowing not only what has formed, but what it formed from.
The tracker holds both. Every spinout is recorded with the core technology it is built on, resolved against the [Antlect Technology Matrix](/guides/technology-matrix), which places spinout formation on the same classification as the research that precedes it. Formation can then be read against the research base it emerged from, across institutions and across technology areas, rather than as a count held separately by each institution.
## What the tracker holds
For each spinout: the originating institution, the core technology it is built on, the research base it was formed on, and the formation route through which it emerged. Where a spinout draws on more than one institution or spans more than one technology area, both are recorded rather than reduced to a single attribution.
Spinouts are identified from institutional and commercialisation records, company registers, and investment and funding records, and each is verified against the research base it is attributed to. The tracker is maintained continuously, since company status changes and a record of formation is only useful if it reflects the present state.
## How the tracker is used
The tracker operates as context. It can be applied to a specific query to return formation activity for an institution or technology area, or applied across the system to examine activity system-wide.
Because spinouts are classified on the same basis as publications, patents, clinical trials and funding, the research underlying formation in a technology area can be examined directly, as can the areas where research capability exists and formation has occurred elsewhere.
The tracker forms part of the [Antlect ontology](/platform/semantic-ontology-layer) and is available as context that can be applied to any query on the platform.
# The Antlect Technology Matrix
Source: https://docs.antlect.com/guides/technology-matrix
A classification of the 100 technologies that define the leading edge of the knowledge economy, structured into 13 categories and applied across publications, patents, clinical trials and funding records.
Economic advantage has concentrated. Across advanced economies, a limited set of technologies now accounts for a disproportionate share of productivity growth, industrial capability and strategic leverage, and governments have responded by identifying and monitoring them directly. The United States, the European Union and comparable jurisdictions all maintain critical technology frameworks on this basis. The premise is common to all of them: position in these areas compounds, and the window in which position can be established is finite.
Acting on that premise requires knowing where capability sits and how it is forming. That is where the instrumentation fails. Research is recorded through disciplinary classifications built to describe how knowledge is produced, not where it leads. Those classifications are stable by design and technologies are not, so the categories through which research activity is observed bear no reliable relation to the categories in which its consequences arrive. The result is that technology questions are answered by proxy, inconsistently, and at a level of resolution too coarse to support decisions of consequence.
The Antlect Technology Matrix resolves this. It is a classification of the 100 technologies that define the leading edge of the knowledge economy, structured into 13 categories, with each technology defined in its own right and characterised by the vocabulary through which it is expressed in research. Applied across publications, patents, clinical trials and funding records, it renders research and innovation activity observable at the level of the technology itself: what is being pursued, by whom, at what depth, and along what trajectory.
This turns the critical technology approach from a policy instrument into an analytical one. Where existing frameworks identify which technologies matter, the matrix establishes what is actually happening within them.
## What the matrix defines
The matrix consists of 100 technologies arranged under 13 categories, spanning artificial intelligence, quantum technologies, biotechnology, medical technologies, advanced materials, semiconductors, energy and climate, space, autonomous systems, advanced sensing, communications, computing and cybersecurity.
Each technology is defined in its own right rather than by reference to the disciplines it draws on. A definition states what the technology is, what falls within it, and where it stands in relation to established fields. Definitions are written to a common standard and to a consistent level of resolution, so that no technology is defined so broadly that it absorbs its neighbours, or so narrowly that it captures only part of the activity that belongs to it. They carry no sector-specific framing, which allows the same classification to serve research, innovation and policy use without adjustment.
Each technology additionally carries the vocabulary through which it is expressed in published research, and a mapping to the ANZSRC 2020 Fields of Research classification. The mapping is an attribute of the technology, not the basis on which it is defined. It exists so that the matrix can be applied to data already recorded under disciplinary schemes.
## How the matrix was built
Construction proceeded in three stages, each addressing a distinct requirement.
**Selection.** Technologies were identified against the criteria that govern critical technology frameworks internationally: anticipated economic and strategic impact, dependence on specialised capability that cannot be quickly acquired, and relevance across multiple sectors rather than a single application. Selection is forward-looking by construction. An area qualifies on the consequence it is expected to carry, not on the volume of research presently conducted in it, which is why the matrix includes areas that remain small in output terms and excludes established fields with substantial activity but limited strategic leverage.
**Definition and structure.** The 100 technologies were structured into 13 categories, each defined so that its members are related by underlying capability rather than by administrative convenience. Every technology was then defined to the common standard described above. Boundaries between adjacent technologies were set deliberately, particularly in dense areas such as artificial intelligence and quantum technologies, where imprecise definition would render neighbouring areas indistinguishable in practice.
**Mapping and verification.** Each technology was mapped to the ANZSRC 2020 Fields of Research classification with a primary code and, where the technology genuinely spans more than one field, a secondary code. Every mapping was verified against the published ANZSRC 2020 group definitions rather than assigned by inference. Each carries a confidence value recording how closely the technology aligns to the assigned field. Where no code fits cleanly, the confidence value records the gap rather than concealing it, on the principle that a classification used for decisions should disclose the limits of its own precision.
## How the matrix is used
The matrix operates as context. It can be applied to a specific query to return activity resolved against a given technology area, or applied across the system to examine activity spanning several areas at once. The classification does not sit alongside the analysis as a reference table; it structures the analysis itself.
This supports the questions that funders, agencies and investors put to a research system. Where capability in a technology sits, and which institutions hold it. Whether that capability is deepening or thinning over time. Where activity in one technology is beginning to draw on another. How position in an area compares against comparable systems elsewhere.
Because every technology is defined to the same standard and applied consistently across sources, results hold their meaning across publications, patents, clinical trials and funding records, and remain comparable over time. The matrix forms part of the [Antlect ontology](/platform/semantic-ontology-layer), the layer that gives structure and meaning to the underlying [data foundation](/platform/overview).
The matrix is maintained by Antlect and reviewed as technology areas develop. It remains Antlect property and is published here as a reference for users of the platform.
# Project workspace
Source: https://docs.antlect.com/guides/workspace
Organise threads, research briefs, and tags inside a project.
The workspace is a project's home view, with every thread, brief, and artifact in one place. It is your working surface within the [Decision Intelligence Terminal](/platform/decision-intelligence-terminal).
## Layout
* **Sidebar**: your projects, always available for switching. The sidebar collapses to an icon rail when you want more room.
* **Thread panel**: the running history of queries in the project, with live step traces for in-flight runs.
* **Workspace tabs**: the project's contents, organised into threads, reports, and artifacts.
## Global search
The search pill at the top of the workspace searches across the whole project (thread titles, report names, and tags) so you can jump to anything without scanning lists.
## Tags
Tags keep a busy project navigable:
* **Add tags** to reports to mark status, topic, or audience.
* **Rename a tag** in place: every item carrying it updates.
* **Reassign on hover**: hover an item's tag to swap it without opening the item.
## Threads
Each thread card shows the query, its step trace with timing, and links to results. Active threads are marked with an accent border; click any step to expand what that stage did. All query runs happen inline here: you never lose sight of the project while an analysis runs.
## Reports tab
The Reports tab lists the project's reports with their tags. Click through to open any report in the [editor](/guides/reports).
Keep project scope tight and let tags handle the variety inside it. A project with focused queries produces threads that read like a coherent investigation log.
# Antlect Help Centre
Source: https://docs.antlect.com/index
Documentation for Antlect: the decision intelligence platform for the innovation ecosystem, connecting researchers, institutions, funding, publications, patents, and clinical trials.
Antlect synthesises the forces that shape innovation into a single decision intelligence platform, connecting more than 100 million data points so that research, funding, capability, and intellectual property can be examined as one system rather than six disconnected sources.
These pages cover how the platform is built and how to work within it.
## Understand the platform
The four layers behind every Antlect answer.
How an open strategic question becomes a grounded research brief.
Why an answer no longer depends on the vocabulary you happened to use.
Published research landscapes, and the method behind them.
## Get started
Sign in, create your first project, and run your first query in under five minutes.
Understand projects, queries, threads, research briefs, and artifacts, the building blocks of Antlect.
## Core workflow
The six entity types Antlect connects: researchers, institutions, publications, patents, clinical trials, and funding.
Compose a query, tune its dimensions and metrics, and watch it run live in the thread panel.
Navigate the Summary, Publications, and Patents views of a completed query.
Switch any result set into an interactive graph of people, institutions, and their connections.
Turn findings into polished reports with a full rich-text editor, charts, and PDF export.
Add charts to reports and switch types in place: bar, line, area, and pie.
Organise threads, reports, and tags inside a project, with global search across everything.
Every query you've run, and every artifact, draft, and mind map you've kept.
## Account & access
How signing in works: secure single sign-on, with no passwords stored by Antlect.
Request access from the landing page; an administrator reviews every request.
Get help from the Antlect team directly from inside the app.
See what's new in Antlect: features, improvements, and fixes, in the order we shipped them.
## Administration
Review access requests and manage users from the administrator dashboard.
## Security & Compliance
Encryption, compliance practices, sub-processors, audit logs, and your data rights, all in one place.
# Decision Intelligence Terminal
Source: https://docs.antlect.com/platform/decision-intelligence-terminal
How an open strategic question becomes a structured, grounded research brief inside Antlect.
Most strategic questions do not arrive well formed. They arrive as a direction of travel: where is our capability thinning, what are we not seeing in this technology, is this position defensible.
The difficulty has never been a shortage of information. It is that turning scattered evidence into a position someone will sign off on has historically taken months of manual synthesis, and the result has rarely been auditable once it was written. The reasoning lived in the analyst's head, and the evidence trail dissolved somewhere between the source and the slide.
The Decision Intelligence Terminal is the layer where that work happens instead. It is the fourth layer of the [platform architecture](/platform/overview), and the only one you work in directly.
## The question stays open
You do not need to know the vocabulary of a field before you can ask about it. Questions are posed in plain language and interpreted through the [Semantic Ontology Layer](/platform/semantic-ontology-layer), which reconciles how the question was asked with how the underlying record was written.
This matters more than it first appears. The premise of a question is usually the weakest part of it, because it encodes what you already assume to be true. A question interpreted semantically rather than literally returns the evidence you did not know to ask for, which is the evidence that changes a decision.
## The reasoning stays visible
Every question runs as an inspectable trace. Each stage of the analysis is shown as it executes, and the full history is retained, so the route to an answer can be re-examined long after it was produced.
Findings are returned as structured evidence rather than prose: the entities that matter, the relationships between them, and the publication, patent, funding, and trial records that substantiate each one. Any statement can be followed back to the record that produced it. Explainability here is a property of the architecture, not a feature bolted onto the output.
## The structure emerges
A position rarely arrives fully formed either. Findings worth keeping are retained as artifacts and organised as the picture develops, and a mind map holds the shape of an investigation while it is still forming, branching entities, open questions, and findings as they connect.
This is the part of the work that usually happens on a whiteboard and is lost. Here it is retained, editable, and attached to the evidence beneath it.
## The position is written
A research brief is where the investigation resolves. It carries the findings, the evidence behind them, and the reasoning that connects the two, in a document you control end to end.
The distinction that matters: the brief is authored, not generated. AI assists the drafting, the evidence is assembled for you, and every section remains fully editable. You keep editorial control of what your organisation puts its name to, which is the only arrangement that survives contact with a board, a regulator, or a funding committee.
Briefs are written in a full editing environment, carry live charts built from your own findings, and export to PDF for circulation.
The practical guide to building, formatting, and exporting a research brief.
## Why this holds up under scrutiny
Three properties travel with every output of this layer:
* **Grounded.** Findings rest on the [Trusted Data Foundation](/guides/data-coverage), not on a model's recollection.
* **Explainable.** The route from question to conclusion is retained and can be re-examined.
* **Controlled.** The final document is yours, edited by your people, carrying your judgement.
AI is the interface to evidence in Antlect. It is not the source of it.
# Forward-Deployed Partner
Source: https://docs.antlect.com/platform/forward-deployed-partner
How Antlect analysts work alongside your team on research too consequential to run self-service.
Some questions carry more consequence than a self-service query should. A due diligence position, a capability assessment behind a funding decision, a technology landscape a board will act on.
The Forward-Deployed Partner model embeds experienced Antlect analysts with your team for that work. They work in the same platform you do, bringing the domain judgement and quality control that no platform supplies on its own.
## How the work runs
1. **Framing.** The question is scoped with your team, including what a useful answer would allow you to decide.
2. **Analysis.** Analysts work across the platform, using Antlect to search the connected record and surface relationships that manual research would not reach at comparable speed.
3. **Synthesis.** Findings are assembled into a [research brief](/guides/reports), with the evidence trail intact.
4. **Handover.** The brief is delivered to your team, and the underlying artifacts remain in your workspace, so the work compounds rather than ending with the engagement.
## Where the division of labour sits
The platform handles retrieval and pattern recognition across the connected record. Analysts handle framing, judgement, synthesis, and quality control. Work at this level requires both, which is the reason the model exists.
## What it is used for
* Technology landscapes across a defined domain
* National and sector capability mapping
* Technical due diligence on a research-backed opportunity
* Competitive and intellectual property intelligence
## Getting started
Forward-Deployed Partner engagements are scoped with the Antlect team rather than enabled in the app.
Discuss scoping a piece of work with the team.
# Platform architecture
Source: https://docs.antlect.com/platform/overview
The four layers behind Antlect: the Trusted Data Foundation, the Descriptive Digital Twin, the Semantic Ontology Layer, and the Decision Intelligence Terminal.
Antlect is built as four connected layers. Each depends on the one beneath it, and every answer the platform produces passes through all four. The architecture is the reason an Antlect answer carries context, and the reason it can be traced back to evidence.
## Trusted Data Foundation
The ground truth. Scientific literature, patents, funding, clinical trials, organisations, and researchers are continuously integrated, validated, and reconciled across more than 100 million data points.
The decisive work at this layer is entity resolution. The same researcher appears in a publication record, a patent filing, and a grant award under different name forms, different affiliations, and different identifiers. Resolving those into a single entity is what converts separate datasets into a foundation, and it is why funding, output, and translation can be examined as one system rather than compared as three.
The entity types in the graph, and the regions covered in depth.
## Descriptive Digital Twin
The structural layer. A continuously evolving representation of the innovation ecosystem: organisations, institutes, laboratories, researchers, capabilities, research infrastructure, intellectual property, and the relationships that connect them.
The term descriptive is deliberate. The twin describes the ecosystem as it is and as it changes, rather than simulating what it might do. That restraint is what makes it admissible as evidence: every relationship it holds resolves to a record in the foundation beneath it.
The twin becomes visible in [Graph mode](/guides/network-graph), where the entities behind an answer can be examined as a network.
## Semantic Ontology Layer
The layer that carries meaning. A policy analyst, a corporate technology scout, and a university researcher will describe one capability in three vocabularies, none of which is wrong. This layer reconciles the taxonomies, classifications, and domain ontologies arriving with the source data into a single conceptual model.
Without it, an answer is limited by the questioner's vocabulary. With it, the question reaches the evidence regardless of how that evidence was described.
Why reconciling vocabulary changes what an answer contains.
## Decision Intelligence Terminal
The layer you work in, where evidence becomes a position. Discovery, knowledge management, AI-assisted analysis, and research briefs operate as one environment rather than as separate tools.
How an open strategic question resolves into a structured research brief.
## What the architecture guarantees
A question is interpreted through the ontology, resolved against the twin, and answered from the foundation. Each stage is retained, which is why any conclusion can be followed back to the record that produced it.
Customer data remains private by design and is never used to train third-party models. See the [Trust Centre](/trust-centre).
# Semantic Ontology Layer
Source: https://docs.antlect.com/platform/semantic-ontology-layer
How Antlect reconciles the vocabularies used to describe the same capability, so a question reaches the evidence however it was written.
Every part of the innovation ecosystem describes itself in its own terms. A government paper records a capability as advanced manufacturing. A university files the same work under mechatronics. A patent classifies it by technical function and uses neither phrase.
None of these is incorrect. Each is the natural vocabulary of the institution that produced it. The consequence is that literal search returns whichever fragment matches the words you happened to choose, and the completeness of an answer becomes a function of the questioner's vocabulary.
The Semantic Ontology Layer removes that dependency.
## What the ontology does
An ontology is a shared model of concepts and the relationships between them. Antlect's ontology reconciles the taxonomies and classification schemes arriving with the source data and maps them onto one conceptual model.
* **Terminology resolves.** Different terms for one concept reach the same records, whichever term is used.
* **Hierarchy is understood.** A question about a broad field reaches the specific subfields within it, without each having to be named.
* **Classifications reconcile.** Patent classification codes, funding programme categories, and research subject codes are read against one another rather than in isolation.
## What it changes about an answer
Consider a question about institutional capability in battery chemistry. Matched literally, it returns the records that use that phrase.
Interpreted through the ontology, it also reaches work filed under electrochemical energy storage, patents classified by technical function, and grants awarded under a programme category that never mentions batteries.
The difference is not a longer list of results. It is that the answer no longer depends on the questioner already knowing every phrase the field uses, which is precisely what the question was asked to establish.
## Where it operates
The ontology has no interface of its own. It sits beneath the work:
* **[Running queries](/guides/running-queries)** are interpreted through it, which is why questions in plain language work without special syntax.
* **[Results](/guides/results)** include records that never used your wording.
* **[Graph mode](/guides/network-graph)** groups entities by resolved type rather than by source label.
Write queries in the vocabulary natural to you. Reconciling it with the vocabulary of the record is the ontology's work, not yours.
# Quickstart
Source: https://docs.antlect.com/quickstart
From sign-in to your first result set in under five minutes.
This guide walks you through signing in, creating a project, running your first query, and viewing the results.
## Before you begin
Antlect is available to approved organisations. If you don't have an account yet, [request access](/account/access-requests) from the landing page. An administrator reviews each request and you'll receive an email when your account is activated.
## 1. Sign in
Go to the Antlect app and click **Login**. Antlect uses secure single sign-on, and you'll be redirected to the login page, and back into your workspace once authenticated.
Your session is tied to your organisation's access grant. If your login is rejected, your access request may still be pending review.
## 2. Create a project
Everything in Antlect lives inside a **project**: a container for related queries, threads, and reports.
1. From the sidebar, click the **+** button.
2. Give the project a name that describes the investigation, for example `Quantum computing capability: NZ universities`.
3. The project opens in the workspace view.
## 3. Run your first query
1. Inside the project, start a new query from the thread panel.
2. Type a research question in plain language, for example:
> Who are the leading researchers in photonics across New Zealand universities, and who funds them?
3. Optionally tune the query: choose the **dimension** you care about (people, institutions, funding) and the **metrics** to rank by.
4. Run the query. The thread panel shows each step of the analysis live as it executes.
## 4. Explore the results
When the query completes, the results view opens with three tabs:
* **Summary**: a synthesised answer with the key entities and findings.
* **Publications**: the underlying publication record.
* **Patents**: related patent activity.
Use the **Graph mode** toggle at the right end of the tab bar to switch the same result set into an [interactive network graph](/guides/network-graph).
## 5. Save your findings into a report
Select the artifacts you want to keep and add them to a [report](/guides/reports). The report editor gives you a full rich-text environment (headings, tables, images, and [live charts](/guides/charts)) and exports to PDF when you're ready to share.
## Next steps
Learn how projects, queries, threads, and reports fit together.
Explore relationships between researchers and institutions visually.
# Antlect research
Source: https://docs.antlect.com/research/index
Published research landscapes built on the Antlect platform, and the method behind them.
Antlect publishes research landscape studies built on the same platform our customers use. Each takes a system that matters, a national research effort, a sector, a technology domain, and describes it as a connected whole rather than as separate datasets.
## What a landscape study describes
A landscape study is descriptive. It sets out the structure and activity of a research system: who works in it, what funds it, what it produces, where clinical and commercial capability sits, and how those relate to one another.
It does not rank institutions, assess the quality of individual research programmes, or prescribe where future investment should be directed. Its observations derive from the underlying data, which is what makes them usable in a decision.
## How they are built
Every study runs on Antlect's [Trusted Data Foundation](/guides/data-coverage), which connects researchers, organisations, funding programmes, publications, registered intellectual property, and clinical trials into a single research knowledge graph.
Because those records are connected rather than compared side by side, a study can hold questions that no single dataset answers. How research activity in a disease area relates to its incidence. Where a country hosts clinical trials without leading them. Which capabilities rest on a single institution.
The method is the platform. Anything in a published study can be reproduced by a customer working in their own [workspace](/guides/workspace).
## Published studies
Current studies are listed on the Antlect site, each with its findings and the option to request the full report.
Research landscapes published by Antlect.
## Commissioned research
Studies scoped to your own question are run through the [Forward-Deployed Partner](/platform/forward-deployed-partner) model.
# Trust Centre
Source: https://docs.antlect.com/trust-centre
Security, privacy, and compliance at Antlect. Your research intelligence is protected at every layer.
Antlect is built for organisations that make decisions on research intelligence. Every part of the platform, from sign-in to storage to analysis, is designed with security, privacy, and data isolation as defaults, not afterthoughts.
## Compliance
We are implementing the controls and practices defined by the following security and privacy frameworks. See each framework below for details on how these practices are implemented.
**Practices implemented.** Data subject rights honoured on request (access, correction, deletion, and portability) with a designated privacy contact.
**Practices implemented.** Our security controls follow ISO 27001 principles: access control, encryption, logging, and least privilege. Formal certification is planned.
**Aligned.** Antlect serves Australia and New Zealand first, with practices aligned to the Australian Privacy Principles (APPs) and the NZ Privacy Act 2020.
What we implement today:
* Data subject rights (access, correction, deletion, and portability) honoured on request via [hello@antlect.com](mailto:hello@antlect.com).
* Data minimisation: the personal data we hold is limited to your account profile and the content you create in your workspaces.
* No sale of personal data, and no use of your content to train AI models.
The full GDPR statement (lawful bases, retention schedule, and response SLA) is being finalised and will be published here.
What we implement today:
* Role-based access control enforced at the API layer.
* Encryption in transit on every connection; encrypted managed storage at rest.
* Audit logging of key account and content lifecycle actions.
* Rate limiting, hardened HTTP security headers, and input sanitisation on all user-supplied content.
The full control mapping against ISO 27001 Annex A is being prepared and will be published here, along with our certification timeline.
What we implement today:
* Customer file storage hosted in AWS ap-southeast-2 (Sydney), keeping data close to the Australasian organisations we serve.
* Collection limited to what the platform needs to operate; clear channels for access and correction requests.
Operating legal entity details and the full APP/NZ Privacy Act statement are being finalised and will be published here.
***
## Security overview
All traffic is encrypted in transit with TLS. Data at rest lives in managed database and object storage with provider-managed encryption. No unencrypted paths exist.
Every workspace is scoped to its owner and enforced on every API request. No cross-workspace data access is possible.
Key lifecycle actions (creates, deletes, restores, and password reset requests) are logged with who, what, and when, and are visible in **Settings > Security > Audit logs**.
Sign-in is handled by a dedicated identity provider. Every API request re-verifies the token's signature and expiry server-side. Antlect never stores your password.
Two roles, user and admin, with the hierarchy enforced at the API level. Access to the platform itself is granted through admin-reviewed access requests.
Your content is processed to answer your queries and nothing more. **Your data is never used to train AI models.**
Beyond the fundamentals above, the platform ships with defence-in-depth hardening:
* **Rate limiting** on every route, with stricter per-route limits on public-facing endpoints such as access requests and support.
* **Strict security headers** and CORS locked to the Antlect application origin.
* **Input sanitisation** on all rich-text content before it is stored or rendered.
* **Safe error handling**: internal errors are never leaked to clients in responses.
* **Upload limits** on all file endpoints to prevent abuse.
***
## Compliance & data protection
Full data subject rights (export, deletion, and portability) honoured on request through our designated privacy contact. A formal response SLA will be published with our full privacy policy.
Operated for the Australasian research sector, with customer file storage in AWS ap-southeast-2 (Sydney) and practices aligned to the Australian Privacy Principles (APPs) and the NZ Privacy Act 2020. *Operating entity details to be published.*
The personal data Antlect holds about you is deliberately small: your account profile and the content you create in your workspaces. The research corpus Antlect analyses (publications, patents, funding, and clinical trials) is public scholarly data about research activity, not customer data.
### Your data rights
* **Export**: request a complete copy of your personal data by emailing [hello@antlect.com](mailto:hello@antlect.com). Self-serve export from Settings is on the roadmap.
* **Correction**: your profile fields are editable at any time in **Settings > Profile**.
* **Delete**: request permanent removal of your account and all associated data via [hello@antlect.com](mailto:hello@antlect.com). Self-serve deletion from Settings is on the roadmap.
* **Privacy contact**: reach our privacy team at [hello@antlect.com](mailto:hello@antlect.com). A designated Data Protection Officer will be announced with our full privacy policy.
***
## Infrastructure & sub-processors
Antlect runs on managed, reputable infrastructure. Customer file storage is hosted in **AWS ap-southeast-2 (Sydney)**, keeping data in the region we serve. The following third-party services process data on Antlect's behalf:
| Sub-processor | Purpose | Data processed | Location |
| ------------------- | --------------------------------- | ------------------------------------------------------------------------ | ----------------------- |
| Auth0 (Okta) | Identity & sign-in | Email, name, credentials (held by Auth0, never by Antlect) | *To be published* |
| Amazon Web Services | File & asset storage (S3) | Uploaded files and report assets | ap-southeast-2 (Sydney) |
| Managed PostgreSQL | Primary application database | Workspace content and account data | *To be published* |
| Neo4j Aura | Research graph database | Public research data: researchers, publications, patents, funding | *To be published* |
| Resend | Transactional email | Name, email address, and message content for support and access requests | *To be published* |
| Mintlify | Documentation hosting (this site) | No customer data | United States |
A data processing agreement (DPA) register covering each sub-processor is being prepared and will be published here. Need a DPA in the meantime? Contact [hello@antlect.com](mailto:hello@antlect.com).
***
## Policies & documentation
Encryption, workspace isolation, access controls, deletion, and your data rights, the full technical breakdown.
Complete list of third-party services that process data on behalf of Antlect, with DPA coverage.
What cookies Antlect uses, why, and how to control them.
Track the key actions on your account (who did what, and when) from Settings.
How sign-in works, and how to manage your password and account security settings.
Full legal privacy policy: how we collect, use, store, and protect your personal data.
Terms governing your use of the Antlect platform.
***
## Questions?
If you have security questions, need a DPA, or want to report a vulnerability, contact us:
* **Privacy & data protection:** [hello@antlect.com](mailto:hello@antlect.com)
* **General support:** [support@antlect.com](mailto:support@antlect.com)
We take vulnerability reports seriously. A formal responsible disclosure policy is being prepared and will be published here.