Ship enterprise-grade AI agents in weeks, not quarters.
Govern every AI action. Prove every decision. Verdict checks each agent action in milliseconds, stops hallucinated figures before they ship, and records evidence your auditors can use.
Low latency
Deterministic checks that cannot be argued out of their answer. Judge pipelines add seconds; Verdict adds milliseconds.
Low cost
Retires the LLM-judge pipeline and makes smaller, cheaper models safe to run in production.
Compliant agents, shipped
One integration point, shadow mode on live traffic, then enforce. Rules change without redeploys.
One conversation, the whole story
Real screenshots, not mockups: a governed answer, a block, a gated step, and the proof.
01ALLOW
Every figure is bound to its source
Asked for a rate, the agent answers with the rate sheet's number, shipped as a verified reference and stamped fresh. A stale or invented figure cannot reach the customer.
02DENY
Push past policy and it stops, gracefully
The customer demands 5.50%, below the channel floor. Verdict returns DENY with the exact rule that fired, and the agent offers the compliant path instead.
03YES, IF
Rules that need a step run the step
An application lands: the national ID is tokenized on sight, and the flow holds until identity verification completes. Real rules are yes, if; Verdict runs the if, mid-conversation.
04PROOF
Every decision lands in a tamper-evident trail
One row per action with the outcome, the rule, and the ~10 ms check behind it. Export the evidence pack and hand it to your second line.
The two problems that keep agents out of production
Enterprise AI is not gated by models; it is gated by approvals. Two risks stall every approval, and Verdict removes both at the same place: the action.
Hallucination
The problem
Agents invent numbers and quote stale ones. One wrong rate in front of a customer is a lawsuit, not a bug.
Solved at the action
With Verdict, figures travel as verified references bound to the system of record. A number the record cannot support never reaches the customer.
0.0% hallucinated figures in our published benchmark
Compliance
The problem
Agents act without consent, disclosures, or approvals, and when the regulator asks why, all you have is a log of text.
Solved at the action
Policy runs before every action: ALLOW, DENY, or REQUIRE_CHANGES with the missing step executed in flow. Every decision lands in a signed, tamper-evident trail.
Consent, disclosure, approvals: enforced, not prompted
One enforcement point, where actions happen
Agents propose. Verdict decides. Your systems execute only what policy approves.
Propose
Your agents
Any framework, any model: OpenAI, Anthropic, LangChain, custom
Decide · in milliseconds
Verdict
- Policy check, every action
- Approvals, consent, disclosure
- Verified figures, tokenized PII
- Signed evidence pack
Execute
Your systems
Core banking, payments, claims, CRM: unchanged
Guardrails screen what the model says. Observability reports what happened. Verdict decides what an agent may do, and replaces neither.
A small model with Verdict outperformed a model costing 3x as much
Our own benchmark, same prompts and tools in every arm. A model upgrade buys a discount on the failure rate; enforcement buys the guarantee, at a third of the cost.
| Metric | Claude Haiku + Verdict | Claude Sonnet alone3.1x the cost | Claude Haiku alone |
|---|---|---|---|
| Figures shipped with no basis in the record | 0.0% | 4.3% | 6.7% |
| Expected-tool adherence | 84.3% | 74.5% | 76.5% |
| Cost for the 72-conversation set | $1.40 | $4.36 | $1.43 |
| Governed catches (steers, floor pushes, conduct) | 11 held | nothing gates | nothing gates |
72 scored conversations, raw data and method ship with the results. Every number is re-derivable.
Run it on your workflowThe questions your review will ask
Each one answered by a mechanism that runs on the action path, mid-conversation. Not process documents; running code.
Identity
Who is the agent acting for?
- Step-up identity verification mid-action
- Per-agent principals, least privilege
requireIdentityVerification
Data
What can it never see?
- PII tokenized before the model sees it
- Redaction and secure input fields
redact · tokenize
Permissions
What can it never do?
- Allow-list per tool and argument
- Value caps clamped to policy limits
capValue · allow-list
Compliance process
What must happen first?
- Consent captured before the action
- Disclosures attached in flow
requireConsent · addDisclosure
Oversight
Who signs off, and when?
- Approvals routed to the right human
- Graceful escalation, never a dead end
addApproval · escalate
Audit
Can you prove what happened?
- Signed, tamper-evident decision trail
- One-file evidence pack risk can sign off on
signed hash chain
From good to provable.
Verdict is the compliance layer for AI agents. Deterministic policy controls check every agent action before it runs, route approvals to people, and keep every decision on the record. Bring one workflow and the owners who sign off on it.