Building trust in AI.
Trust tools for AI builders.

Because trust has to be built in, not bolted on.

Engine agnostic.   In production with customers.

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Building AI you can trust is hard.
Most teams have Al pilots. Almost none have Al they'd put in front of a client, a regulator, or an auditor. That gap is a trust problem.

Compliance

"Where did this answer come from?"

Customer

"Can I trust what it told me?"

Lawyer

"Can we defend this if it's challenged?"

Reviewer

"Who checked this before it shipped?"

Auditor

"Show me why it decided that?"

If your Al can't answer them, you can't ship it. The numbers say most:

0%

of engineering leaders are confident AI-generated code will behave correctly in production.

Lightrun, 2026.

17 to 33%

hallucination rate in purpose-built legal AI tools, even with
retrieval-augmented generation.

Stanford RegLab and HAI.

Trust can't be prompted in. It has to be architected.

Most trust tools work around your AI. Not in it.

Three places trust can live. Only one is inside the application itself.

Platform governance

Govern the platform.

Who can use which models, under what policies. Necessary. Says nothing about what your application actually does.

Output scanning

Check the output.

Catches problems after the answer exists. Useful. Can't tell you where an answer came from or why it was made.

Application trust • Primal

Build trust into the application

Provenance, verification, and oversight generated into the application as it's built. This is what Primal does.

You'll likely use all three. The first two exist. The third is why we're here.

Primal Trust Skills.

Installable components that give AI coding agents trust capabilities.

Engine-agnostic.    Used by Primal in every solution we build.

Provenance.

Where every answer came from.

Verification.

Proof the answer is correct.

Disambiguation.

Picks the right meaning, not a guess.

Explainability.

Plain reason for every answer.

Quality Gating.

Blocks bad output before it ships.

Oversight.

Humans stay in control.

Accountable Personalization

Tailored with a clear audit trail.

Governed Training Data.

Only data you're allowed to use.

90-150%

output quality lift in preliminary testing

3x

improvement on source attribution, auditability, defensibility

Preliminary numbers from preliminary testing. Ask us and we'll show you the methodology.

Runs on Claude Code, Cursor, OpenAI Codex, and any agentic code engine.

How customers get them today

Provenant Sprints.

A timeboxed experiment with our team, inside your real workflows.

You leave with a working, trustworthy workflow and a team that knows how it was built.

8h

One workflow.

16h

Multiple workflows.

40h

Multi-team.

Custom

Larger scope.

Book a Sprint →
Early proof. Trust Skills used in regulated workflows.
Three regulated SMBs. Built with Primal.

"

What suprised me was how quickly we had real answers, and not just about what to build. We found out just as fast what wasn't worth pursuing. That kind of clarity would have taken us months to get to on our own, if we got there at all.

Partner, Canadian corporate law firm.

We build with our own tools. These are three examples of what Primal's Trust Skills make possible.

Legal.

Talk with my case.

Clients access their case anytime. Every answer grounded in lawyer-approved documents, verified at the sentence level

Health.

Talk with my specialist.

Patients ask about their care between visits. Responses constrained to physician-approved sources.

M&A.

AI-powered due diligence.

Diligence at the speed of conversation. Every claim verified against the deal room.

Read the full case study →

A 15-year head start.

Fifteen years of Al research, 185+ patents worldwide, built into every Skill.

five architecture pillars

Automic knowledge representation

Preference-guided semantic processing

Verifiable provenance

Decision auditability

Governed search and personalization

Building AI that has to hold up? Talk to us.
We'll tell you honestly where we can help and where we can't on a 30 minute call.
Book a call →