About FieldHash
We build the independent authority and evidence layer for enterprise AI.
FieldHash governs what may shape an AI answer or action, then records the decision for review.
Mission
Keep humans in authority over the AI acting in their name.
Your reviewers, policies, and systems of record establish what an agent may do. FieldHash enforces that authority independently of the model and preserves a packet reviewers can verify under the configured deployment policy. The human remains the author; the model does not police its own boundary.
That is a narrower, more honest promise than “trustworthy AI.”
Vision
When critical decisions shift to machines, teams need to keep hold of what governs them. Without clear visibility, “the AI did it” becomes an answer reviewers are forced to accept.
We are building toward the opposite: a world where every governed consequential AI action traces to its authorization, can be contested, and can be reversed when the underlying system supports reversal.
Delegate, don't abdicate.
Accountable by construction.
From ledger evidence to governed inference
FieldHash grew out of evidence systems reviewers could verify for themselves. We apply that same discipline at the live AI handoff: configured authority determines what may proceed, and the packet preserves why.
What we believe
Govern the path, then keep the proof.
AI needs more than a constitution.
Model self-policing is a soft control. Moving the gate to the handoff enforces configured policy independently of the model, so model choice can turn on capability, latency, and cost without shifting the governance boundary.
Authority is defined, not inferred.
FieldHash does not replace or independently index your systems of record, and it does not guess your rules. You define authority through existing systems and review; FieldHash enforces the resulting signals at the handoff.
Proof matters more than posture.
A narrow claim that can be inspected is more useful than a broad claim the buyer has to trust.
Enterprise AI needs reviewable paths.
When an output is challenged, teams need to know what was allowed in, what stayed out, and why.
What we build
Three surfaces, one governed path.
FieldHash governs configured handoffs across three surfaces: which records may influence an answer, which actions an agent may execute, and which reviewed decisions may govern later cases. FieldHash Ledger preserves the resulting path for review.
Governed Memory
Controls which retrieved records may influence an answer, while retaining stale, rejected, superseded, or rolled-back records for review.
Governed Actions
Controls which tools remain available to the agent, and can require verified human approval before configured consequential actions run.
Governed Precedent
Controls which reviewed decisions may govern future cases: scoped, expiring precedents that suspend on drift and route back to review.
FieldHash Ledger
Records the governed path across all three surfaces: what was allowed, what was blocked, which authority signal applied, and what reviewers can inspect later.
Founder
Aaron Martinez
Aaron founded FieldHash around a narrow operating question: when an AI answer or action is challenged, can the team show which authority was allowed to shape it?
He builds the gate, evidence program, and pilot path around the same rule: publish the boundary, keep the claim narrow, and let reviewers inspect the record.
Write to AaronStart with one workflow.
The fastest evaluation is narrow: one agent or RAG workflow, the authority signals it already depends on, and a shadow-mode comparison before enforcement.
Request pilot review