Industry
Government
Policy-constrained AI for public-sector programs where oversight, records obligations, and public trust depend on auditable evidence.
Who in Government benefits from deterministic AI governance — and what they're hearing from skeptics.
Common Objections
Our process already has human approvals, so model output risk is contained. Human approvals help, but they do not guarantee evidentiary support at machine speed. Ontic provides deterministic verification before claims are emitted into the approval chain.
Policy interpretation varies by program, making enforcement brittle. Ontic externalizes policy and evidence rules so they can be versioned, reviewed, and updated per program without changing application code paths.
Audits focus on outcomes, not every intermediate model decision. When outcomes are challenged, intermediate decisions become evidence. Ontic preserves those decisions with source lineage and gate rationale so agencies can answer confidently.
Questions to Consider
- Can your agency prove why a given AI-assisted output was allowed to be emitted?
- How are policy updates propagated and enforced across model-enabled workflows?
- What evidence chain do you provide to inspector, legislative, or public oversight teams?
- Where do you enforce least-privilege and mission-boundary controls for AI output?
- How quickly can you reproduce the exact gate decision path for a challenged case?