AI Intelligence Assistant Research track

Ask in plain language. Get answers you can verify.

The Epinova AI Intelligence Assistant is a research-stage capability that would let authorized users query surveillance and programme data in natural language - with every answer grounded in, and cited to, the underlying sources.

The problem

The answer exists - somewhere in the data.

Decision-makers often need a quick, specific answer: which districts have rising positivity, what the forecast says for next month, what was decided at the last partner meeting. Getting it usually means waiting for an analyst. Generic AI chat tools, meanwhile, cannot be trusted with programme data and may produce confident but unsupported answers.

  • Simple questions require analyst time
  • Generic AI tools are not grounded in authorized data
  • Unsupported answers carry real operational risk

Product overview

Grounded, cited and permission-aware.

The assistant is being researched as a retrieval-grounded interface: it would only answer from data the user is authorized to access, cite the records and documents behind each statement, and say clearly when it does not know.

It is not yet available. We are prioritizing evaluation of factual accuracy, citation quality and refusal behaviour before any release.

Key capabilities

What it is designed to do

Research track

Natural-language questions

Plain-language queries over surveillance, forecast and programme data.

Research track

Citations on every answer

Each statement linked to its source record or document.

Research track

Permission-aware retrieval

Answers limited to data the user is authorized to see.

Research track

Briefing drafts

Draft situation summaries for human review.

Interactive visualization

Explore AI Intelligence Assistant

Illustrative concept - simulated question, data and citations.

Which areas have rising test positivity this month?
Assistant (concept)

Three fictional areas show positivity rising for three or more consecutive weeks: Kestrel Marsh, Granite Bay and Dune Hollow[1]. Kestrel Marsh has the steepest increase and below-target net coverage[2].

  1. Weekly surveillance extract, weeks 22–26 (simulated)
  2. Coverage survey summary, Q2 (simulated)

Research-stage concept - simulated question, answer and sources. Not an available feature.

Example workflow

How the work flows

  1. 01

    Ask

    A user asks a question in plain language.

  2. 02

    Retrieve

    Relevant authorized records, forecasts and documents are retrieved.

  3. 03

    Answer

    A concise answer is drafted with citations to each source.

  4. 04

    Verify

    The user can open every source; uncertain answers are flagged.

Inputs and outputs

What goes in, what comes out

Inputs

  • Authorized surveillance data
  • Forecasts and risk maps
  • Programme documents and decisions

Outputs

  • Cited answers
  • Draft briefings
  • Links to source records

Intended users

Who it is for

Senior decision-makers

Fast, trustworthy answers without a data request.

Analysts

Less time on routine queries.

Integrations

Works with the systems you already use

Integration scope is agreed with each partner; connectors marked “planned” are on the roadmap.

  • All Epinova modules (planned)
  • Document repositories (planned)

Responsible AI and data

Safeguards built in from the start

Grounding required

No answer without a supporting source.

Says “I don’t know”

Designed to decline when evidence is insufficient.

Evaluation before release

Accuracy and safety testing precede any availability.

Interested in shaping the assistant?

We welcome research and programme partners to help define evaluation criteria.