For · Professional Services & Advisory Firms

The firm signs the deliverable.
The evidence underneath it should be inspectable.

AI can accelerate regulatory research, due diligence and policy analysis. It also creates a governance problem: a polished answer can hide weak sourcing, derivative evidence, unreviewed assumptions or a citation that does not support the sentence it is attached to.

Epistamate is designed to make the evidence chain and the human review chain visible before consequential work leaves the team.

See a governed research example →Explore evidence-led advisory →

What partners and practice leads
actually need to know.

Does the citation support the claim?

A real source can still be misused. The relevant control is an inspectable relationship between the proposition and the evidence, not merely a bibliography with valid URLs.

Is the corroboration independent?

Multiple articles or multiple models repeating one underlying result do not create independent evidence. Provenance and source-family relationships matter.

What did the reviewer actually do?

A reviewer should be able to see what AI prioritised, what the analyst inspected, what they accepted or rejected, and what remained unresolved. Passive exposure to an AI result is not the same as review.

What was known at sign-off?

When a position is challenged later, the useful record is the contemporaneous evidence posture: sources, qualifications, contradictions, gaps and explicit human decisions.

An evidence-control layer for
AI-assisted professional work.

01

Regulatory and compliance advisory

Binding instruments, official guidance, proposals, enforcement positions and commentary should not be flattened into equivalent “sources.” The engine is designed to preserve role, authority, jurisdiction, applicability and effective-state distinctions.

Important: domain-specific RegWatch/Gov policy packs are still being calibrated; the architectural separation exists, but this site does not claim full vertical production readiness.
02

Transaction and commercial due diligence

Market, operational and regulatory claims can be linked to the evidence and uncertainty that existed when the view was formed. The objective is reconstructability, not a universal numerical confidence score.

Reviewer value: the file can preserve why the team believed something, what contradicted it and which gaps were knowingly accepted.
03

Policy and public-sector work

Known unknowns should be explicit. A gap documented before a recommendation is different from an unsupported claim smoothed over by confident synthesis.

Fail-closed principle: zero strict findings can be a correct output when the evidence does not warrant a stronger conclusion.
04

Institutional knowledge

The long-term product direction is to let firms reuse prior evidence with provenance, review history, applicability and recency checks rather than retrieving old decks into a new prompt and hoping context does not drift.

Current boundary: same-topic lineage and continuation are substantially implemented; durable cross-topic Knowledge remains under canonical rebuild.

Local-first does not mean
pretending external models are local.

Local research state

Epistamate is a desktop application with local canonical persistence. Research state, review history and evidence artifacts are designed to remain under the researcher's control.

Explicit network permissions

When web acquisition or model reasoning is enabled, external calls go only through configured services and explicit permissions. The product should make those boundaries visible rather than implying that every workflow is zero-network.

Closed-source workflows

Document-led and source-bounded review remain important architectural directions for confidential work. Capability claims should be evaluated against the exact configured workflow rather than inferred from a broad “privacy-first” label.

Governance principle: privacy, evidence quality and human review are separate controls. None should be inferred from the presence of the others.

If your practice needs AI-assisted research that can survive informed review,

We are looking for consequential workflows where evidence support, reviewer provenance and auditability matter more than faster prose.

Explore the advisory method →