Government & Public Sector AI Engineering

Every citizen-facing answer cites its official source, and rights-affecting decisions stay with a person who can explain them. We document how the system works and design it to be reviewable, because public AI that cannot explain itself erodes trust in the institution. Transparency is a requirement of the deployment, not a feature we add if there is time.

We design so it does not make final rights-affecting decisions on its own. AI assists by processing forms, surfacing information, and drafting, while a person decides and remains accountable. Fully automated decisions about benefits, eligibility, or enforcement carry both legal and trust risk that outweighs the time they would save.

Yes, when designed for it. We use secure, FedRAMP-style deployments and zero-data-retention options so citizen data is protected and not exposed to external model vendors. Public records are sensitive and heavily regulated, so security and access control shape the architecture from the start rather than being bolted on.

It must be, and we build to accessibility standards so citizen-facing AI works for people with disabilities and across channels. A service that only some citizens can use is not a public service. Accessibility and plain-language answers are core requirements, because the whole point is reaching everyone the institution serves.

The agency does. Your records, the trained models, and the code remain yours, in your environment under your security controls. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single a citizen service assistant build is a modest, weeks-long project, while a wider rollout across your records and case systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from shorter backlogs, faster citizen answers, and lower cost to serve, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as a citizen service assistant usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are citizen service assistants, case and form automation, public-sector analytics, and anomaly detection. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.