Documents into structured information
Extract fields, classify records, and summarize long documents. Preserve the source and confidence signals so a reviewer can trace and correct an interpretation before it enters a workflow.
CapabilitiesAI Systems
Intelligence, put to work.
Make information useful. Give people better tools for the decisions that matter.
01 / The operational problem
Begin with the work.
Public institutions hold a great deal of knowledge in documents, records, and disconnected applications. Finding the right information is often harder than acting on it. We design AI around a defined task, an authorized source, and a person who remains responsible for the outcome.
- 01Approved sources
- 02Permission-aware retrieval
- 03AI assistance
- 04Human review
- 05Authorized action
Technical capabilities
From requirements
to working software.
Answers grounded in your knowledge
Connect enterprise search and retrieval-augmented generation to approved material. Carry source references and document permissions into the answer instead of treating a model as a source of record.
Automation with explicit boundaries
Coordinate repetitive steps through constrained tools and explicit approval points. Separate a suggestion from an authorized action, and define what the system should do when information is missing.
Evaluation as part of the system
Build representative test sets, inspect failure modes, and track output quality and cost. Compare model and retrieval changes against the same operational criteria before expanding use.
Representative use cases
Where this can help.
Examples of the work this approach can support. These are application scenarios, not claims of past performance.
Policy and procedure search
Help authorized staff find an answer with links to the relevant policy, version, and supporting passage.
Document intake and review
Prepare structured records from forms and supporting documents, with staff review of uncertain or incomplete fields.
Analyst assistance
Draft summaries and classify incoming information so analysts can focus on exceptions, interpretation, and decisions.
Engineering approach
Make the important decisions explicit.
Define the decision
Identify the user, permitted sources, acceptable error, and consequences of a wrong answer. Establish a non-AI baseline.
Constrain the system
Apply access rules during retrieval, validate outputs, isolate tool permissions, and require approval for consequential actions.
Evaluate and observe
Test representative records, record evidence and decisions, and define escalation and rollback before deployment.
Integration considerations
Part of your environment.
AI should work inside the institution’s existing identity and information boundaries. We plan connectors, document refresh, retention, model hosting, and provider terms together. A provider-neutral interface can make model changes possible without rebuilding the surrounding workflow.
Public-sector requirements
Built around responsibility.
Source-grounded does not mean error-free. Human review, accessible interfaces, logged actions, and explicit data boundaries belong in the acceptance criteria. Sensitive records require an agreed handling and deployment model before they enter any AI system.
Read our engineering principlesConnected capabilities
Let’s build what matters
Bring the operational problem. Let’s define the system.
Start with the work. We’ll help define the right technology.