AI infrastructure for government

Public sector AI carries requirements most software doesn't: data has to be isolated between departments and cases, every decision an agent makes has to be accountable, and the documents involved, forms, filings, public records, arrive at enormous volume in every format. If you're building tools for government agencies, citizen-facing or internal, the model is rarely the constraint. The infrastructure underneath is: extraction at scale, departmental isolation that holds, and audit trails that are there by default.

This page is for teams building public sector tools. Exabase gives you document processing through Extract, departmental isolation through Bases, and audit trails built into how Memory is stored. Each department or case gets its own isolated environment, and what an agent did and knew is reconstructable, the foundation public sector AI needs to be accountable.

What you can build

Government AI tools tend to be one of a few shapes, each on infrastructure that already exists.

A citizen-facing assistant that answers from public records and an applicant's own submissions, isolated per case, with every interaction on the record, a per-case RAG pipeline with the audit trail pattern.

A forms and records processor that extracts structured data from the flood of forms, filings, and public records an agency handles, document extraction at scale.

An internal agency tool that gives staff AI over the department's own knowledge, with departmental isolation, the internal tools pattern with Bases per department.

An accountable decision-support agent whose every action is reconstructable from a timestamped record, the compliance and audit trails and decision trace patterns.

Government problems, solved

The problems public sector builders run into are specific, and each has an answer.

Departmental and case isolation. Data has to stay separated between departments, agencies, and cases, and the separation has to be structural rather than a filter that might fail. Bases make isolation a property of the architecture: each department or case gets a sealed environment, and operations scoped to it can only see that environment. It's the multi-tenant memory pattern where the requirement is regulatory.

Audit trails on every decision. Public sector AI must be accountable. Every memory carries creation and modification timestamps, so what an agent knew and did at the time of an action is reconstructable, without a separate logging system, the compliance and audit trails pattern. For capturing the reasoning behind decisions specifically, decision trace infrastructure covers that.

Forms, filings, and records at volume. Agencies process enormous volumes of paper in every format. Extract turns forms, filings, and records into clean, structured text through one API, including scans, at scale, and Workers handle continuous intake on a schedule.

Public records that need to be searchable. Deep Search finds by meaning across processed records, so staff and citizen-facing tools can find the relevant document regardless of phrasing, and it holds quality at the scale public archives reach.

The infrastructure underneath

Four primitives carry most government tools. Extract processes forms, filings, and records at scale. Bases enforce departmental and per-case isolation from a single API call. Memory carries context with the timestamps that make it auditable. Deep Search makes records findable by meaning. One API key, rather than assembling and isolating the pieces yourself. Exabase is HIPAA compliant and has passed CASA Tier 2 review, with AES-256 encryption at rest and structural data isolation between tenants, though whether a deployment meets your specific public sector and regulatory obligations is a determination for your own compliance review.

Isolated and accountable as you scale

A government tool on this foundation keeps its critical properties as it grows. Adding a department or a case is one API call to create its isolated environment, so the separation holds whether an agency runs a handful of departments or processes millions of cases, and it doesn't weaken as features are added, because individual operations can't breach a structural boundary. The audit trail builds itself as agents work, so accountability is inherent rather than retrofitted, and there's no growing backlog of un-logged actions. Extraction and scheduled processing absorb the document volume the public sector generates. The properties that matter most for public sector AI, isolation and auditability, are exactly the ones that hold steady at scale rather than degrading when maintained by hand.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: multi-tenant memory for SaaS for departmental isolation, compliance and audit trails for AI for accountability, document extraction at scale for records processing, and decision trace infrastructure for capturing reasoning. There's a free tier to build against, and any public sector deployment should go through your own compliance review before production.

FAQs

How is data isolated between departments and cases?

Each department or case gets its own Base, a structurally isolated environment, so operations scoped to it can only see that environment and cross-contamination is prevented by the architecture rather than by filtering. It's the multi-tenant SaaS pattern.

Is every agent decision auditable?

Every memory carries creation and modification timestamps, so what an agent knew and did at the time of an action is reconstructable without a separate logging system. For the reasoning behind decisions specifically, decision trace infrastructure captures that as searchable memory. It's the compliance and audit trails pattern.

Can it process forms, filings, and public records at scale?

Yes. Extract turns forms, filings, and records into clean, structured text through one API, handling scans as well as native formats, and Workers process continuous intake on a schedule.

Is it secure and compliant enough for public sector use?

Exabase is HIPAA compliant and has passed CASA Tier 2 review, with security and privacy practices including AES-256 encryption at rest and structural data isolation between tenants. Whether a given deployment meets your specific public sector and regulatory obligations remains a determination for your own compliance review.

Can records be searched by meaning?

Yes. Deep Search finds by meaning across processed records, so the relevant document surfaces regardless of phrasing, and it holds quality at the scale public archives reach.

Is this a finished government product or something I build on?

Something you build on. Exabase is the infrastructure, isolation, extraction, auditable memory, and search, and you build the citizen-facing or internal agency tool on top.