Benefits navigation & public-facing chat

Conversational systems answering high-stakes questions for the public — where an authoritative wrong answer is indistinguishable, to its victim, from policy.

Use cases

What AI is doing here

Benefits-navigation chatbots

Generative

Conversational guidance about eligibility and process, for applicants directly or for navigators assisting them.

Retrieval-grounded appeal drafting

Generative

RAG systems drafting determinations or appeal responses from policy corpora for adjudicator review.

Case files

What has gone wrong — and right

Documented deployments, presented as stylized model organizations with full citations.

Nava assistive benefits chatbot

United States (pilot deployments)

A retrieval-grounded navigation copilot built on verify-before-use principles — the cautious end of the design spectrum.

System map

Who is in the system, and what pushes on it

Who is in the system

Frontline workers. Caseworkers, screeners, eligibility staff — the operator network whose judgment the system augments or erodes.
Agency leadership. Owns procurement, policy, and the authority map; answers for the system publicly.
Served people & families. Those the decisions land on. Deliberately outside the PAN dynamics — their outcomes are measured, never simulated.
Vendors. Build and update the systems; hold the information asymmetry procurement must govern.
Advocates & community organizations. Surface harms institutions do not see; historically the earliest accurate signal.

Dominant pressures

Caseload surge. Demand outruns staffing; per-case attention shrinks and review becomes triage.
Vendor opacity. The deploying institution cannot inspect the model, data, or update pipeline it is accountable for.
Data & policy drift. The world, the intake process, and the rules change under a system trained on how things used to be.

Governance

Questions leaders should be asking

1. Is the system answering people directly, or assisting a professional who answers?
2. What is the retrieval corpus, who curates it, and how fast does it track policy change?
3. How are confident wrong answers detected — before a journalist detects them?
4. Does the interface honestly convey uncertainty, or does it perform authority?

For the actions behind these questions, see the Practice Library.

Seeing your organization in this domain? Mapping its actual pathways, pressures, and correction capacity is engagement work.

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