Document intelligence, grounded retrieval, and model assurance for organizations whose decisions get audited — government agencies, health plans, insurers, and financial institutions. Evidence-linked outputs, auditable pipelines, deployable inside your boundary.
We go deep on a small number of things and then compose them. Each stands alone as an engagement; together they become the layer a company actually runs its AI on — rather than a dozen disconnected pilots nobody owns.
Classification, extraction, and adjudication support across claims, casefiles, permits, contracts, correspondence, and FOIA queues — including the scanned, handwritten, and decades-old material that defeats commodity OCR.
Question answering over policy manuals, regulations, contract files, and technical archives. Every response carries a citation to the source passage, and the system declines rather than guesses.
Independent evaluation, red-teaming, and bias assessment for AI systems you already own. Model cards, test plans, and inventory artifacts that survive an inspector general, a regulator, or an external auditor.
The AI OS for a regulated company: systems of record connected once, shared institutional knowledge, agents permissioned to do real work, and one evidence trail across all of it. Built in place, on your infrastructure, and handed over.
The work is the same either way: read the evidence, cite the source, let a human hold the decision. What changes is the paperwork around it.
Federal, state, and local agencies, and the primes who hold their contracts. We are set up to sit underneath a prime rather than compete with one.
Health plans and providers, insurers, financial institutions — anywhere a decision has to survive an auditor, a regulator, or a plaintiff’s lawyer.
AI programmes fail when they are bought as a monolith. We size the first increment to fit inside a quarter, and we make the result measurable.
We start from the action a person takes today — the determination, the routing, the review — and work backward to the model.
Current accuracy, cycle time, and cost are measured first. Without a baseline, no claim of improvement is defensible.
Deployed where the data already lives — your cloud, your enclave, your network. No exfiltration to train anything.
Documentation, evaluation harnesses, and runbooks are deliverables. Your team can operate it without us.
An AI system in regulated work has to explain itself to a programme owner, a privacy officer, an auditor or inspector general, and eventually a journalist. We design for that conversation from day one.
Discuss a programOutputs link to the source record. Reviewers verify in one click instead of trusting a score.
We publish where the system is weak, and enforce abstention thresholds rather than degrading silently.
On-premise, private cloud, and air-gapped deployment paths. Your data never leaves your boundary or trains a shared model.
Interfaces meet Section 508 and WCAG 2.1 AA — a requirement, not a retrofit.
Every inference, prompt, version, and override is logged and reproducible for post-hoc review.
We build decision support. Consequential determinations stay with the accountable official.
Commercial buyers can skip this section. It is here because federal and state procurement needs these identifiers before a conversation can start.
Registration is active in SAM.gov. Primes needing anything further ahead of a proposal deadline should reach out directly.
Tell us the problem, the constraints, and the deadline. We will tell you honestly whether we are the right partner for it — and say so plainly if we are not.