For banks, insurers, healthcare, and the integrators serving them, Veridect verifies what your AI says and governs what your agents do — one control plane for both. Four leading models check every answer in parallel; a pre-action gate blocks anything an agent has no authority to do, escalates the sensitive calls to a human, and returns each verdict with a calibrated confidence score and a tamper-evident, hash-verified audit trail. Tested at volume against an independent policy spec and hardened to run across many clients at once — built for high-stakes work where one model’s answer can’t be trusted and an agent’s action can’t be undone.
Enterprises are putting large language models — and increasingly, autonomous agents — into regulated, high-consequence workflows: contracts, treasury, claims, HR, clinical operations. A single model’s answer can’t be independently verified, and an agent can move money or change a record before any human sees it. Veridect is the layer that verifies the answer, governs the action, and enforces your data-governance policy — with a record that survives an audit.
Verify what AI says. Four leading models answer the same question in parallel, cross-examine each other through a verifier mesh, and return one answer with weighted-consensus voting and a confidence score that attributes where any uncertainty comes from.
How verification works →Govern what it does. A pre-action gate intercepts an autonomous agent’s proposed action before it executes and returns one verdict in seconds — greenlight, escalate to a human, or block — with a hash-verified audit bundle for every decision.
How the gate works →Enforce your rules. The policy layer inside the gate holds every agent to the authority it was granted and to four deterministic policy classes — money over a limit, protected personal data, protected-class-adjacent factors, and regulated health data — so a sensitive action escalates to a person, and one reaching past its authority is blocked, before it ever runs.
How your policy is enforced →Instead of trusting one model, Veridect runs Claude Opus 4.5, GPT-5.1, Gemini 2.5 Pro, and Sonar Pro in parallel on the same question, cross-examines their outputs through a verifier mesh, and returns a single answer with weighted-consensus voting and a calibrated confidence score.
Veridect doesn’t just return a number. It attributes the uncertainty to its source, so a low score tells you why.
Verifying an answer starts with what goes into it. Before Veridect puts a question to four models, it does two things at the door: it strips personal identifiers — emails, phone and card numbers, and the like — out of the text, and it turns away input that is genuinely harmful or is trying to hijack the system. So those identifiers never reach a provider, and known attacks are refused before the four-model consensus runs.
Formatted data, by design. The scrub targets formatted identifiers — the data most likely to leak by accident — not names, which many workflows legitimately need to keep. It runs at the question surface of the consensus engine, before the four models are called.
Before an autonomous agent moves money, sends data, or changes a record, Veridect intercepts the proposed action, runs it through the consensus engine, and returns one verdict in seconds. Most guardrails see only the prompt. Veridect governs the whole action lifecycle — before, during, and after.
High-confidence, in-scope actions clear automatically, so your team only sees what needs them.
Uncertain, high-risk, or out-of-policy actions route to a human with the full reasoning attached.
Scope violations — an agent reaching past its authority — are stopped before they execute.
AI no longer just answers — it acts. An agent can move money, change a record, or send personal data on its own, in the time it takes to read one email. Veridect turns the rules your business already lives by into enforcement that runs at the one moment that matters: the instant before the action executes.
Four models cross-check every high-stakes answer before anyone acts on it.
A pre-action gate inspects the act itself — not just the words a model produced.
Your spend limits, data-handling lines, and regulatory boundaries become what the gate enforces.
The moment an agent tries something sensitive, the gate weighs the action against the policy you set and the engine’s read of the risk, and routes it to a person before it runs — and an agent reaching past the authority it was granted is stopped outright. Four of those rules ship enforced in code as deterministic policy classes, each validated in independent red-team testing:
A payment, transfer, or financial commitment above a limit you set is held for human sign-off before it executes.
Actions that change protected personal information are routed to a person rather than completed unattended.
Decisions touching protected-class-adjacent attributes are surfaced for human review, where fairness rules apply.
Access to or transmission of regulated health information is escalated to a person instead of proceeding on its own.
Serious data loss rarely looks like one dramatic action. It looks like a run of small, individually reasonable reads — a record here, a field there — and then one ordinary-looking send. Each step clears a single-action check on its own. So Veridect keeps a running tally across each working session your platform defines: once an agent has read too much sensitive material, the next outbound action escalates to a person — enforced at runtime from that running count, independent of which model is driving the agent.
One clear line. The only action the gate refuses outright is an agent reaching past the authority it declared. Everything the gate classifies as sensitive escalates to a person — nothing high-stakes is silently allowed through, and nothing is quietly blocked behind your team’s back. See how this maps to SR 11-7, NAIC, FDA AI/ML, NIST AI RMF, and the EU AI Act →
Whether it’s a question to verify or an agent action to govern, the request runs the same path — and ends with a record you can replay.
A question to verify, or an autonomous agent’s proposed action, enters the engine.
Four leading models answer in parallel; weighted voting finds the agreement.
A verifier mesh cross-examines the outputs; confidence is scored and attributed to its source.
A verified answer, or a greenlight / escalate / block — with a SHA-256-chained audit bundle.
The action proceeds — and is recorded in the audit bundle.
A sensitive call is routed to a person before it can run.
An agent reaching past its authority is refused outright.
In May 2026, a credentialed Fortune 100 model-risk reviewer ran seven adversarial scenarios — across legal, finance, HR, supply chain, multi-jurisdiction, and healthcare — live on these public demos. The gate matched its pre-stated behavior on each: holding boundaries, escalating at the right thresholds, and decomposing failure modes correctly — independent, adversarial proof, run live on the demos rather than asserted on a slide.
See the seven scenarios →Boundaries flipped at the exact policy threshold — reproducible right now on the live demo.
Confidence is discounted by the kind of ambiguity, not just disagreement count. Anchors observed in the external testing set:
The question has a defensible answer once a single missing detail is pinned down.
One genuine dimension of uncertainty remains — scored lower, surfaced clearly.
Jurisdictional or multi-factor conflict. Discounted hardest — exactly where a human should look.
Spend above a configured ceiling flips from clear to escalate.
Writes that change personal data require a second set of eyes.
Actions near protected-class factors are held for review.
Health-data access and transmission route to the highest-risk tier.
The pre-action gate feels like the part that’s going to get serious attention from global systems-integrator and tier-1 bank buyers — this has enterprise-grade realism.
Independent red-teaming proves the gate holds on the hard scenarios. Coverage proves it holds across the policy space we swept. Veridect tests the gate against an independently authored policy oracle — a hand-written specification of the business rules that treats the gate as a black box, computing the expected verdict for every scenario from the spec’s own rules, never from the gate’s code. That makes the result diligence-grade: non-circular, mutation-tested, reproducible, and kept separate from live model behavior. We report two numbers, and we keep them apart.
Hard to replicate, by design. The defensible work isn’t the 4,697 count — it’s the discipline behind it: business rules translated into an independent oracle, boundary cases generated across tenants and risk tiers, mutation tests proving the harness catches regressions, and live consensus sampled separately. That’s the difference between a demo guardrail and a governance system you can build a regulated stack on. Raw harness report, corpus version, mutation-test output, and load-test results are available under NDA.
Run a live verdict →Every divergence moved toward human review. Nothing policy said to stop was allowed through — when the gate disagreed with the spec, it disagreed by being safer.
Veridect is no longer a single-tenant demo gate. The trust layer now carries the substrate a Fortune 500 platform or systems integrator expects before embedding it across clients — tenant-scoped state, keys, quotas, and audit chains; per-tenant encrypted fields; zero-downtime key rotation; and write-once audit mirroring, with isolation we tested under load.
State, rate limits, and audit chains are keyed per tenant and shared across processes through a pluggable state layer, with a clean single-process fallback. Under concurrent load testing in development: zero cross-tenant violations.
Every verdict’s bundle is mirrored to a content-addressed object store keyed by its own hash — append-only at the application boundary, tamper-evident, and independently re-verifiable against the ledger.
Per-tenant audit fields are AES-256-GCM envelope-encrypted at rest — each tenant’s data key is wrapped by a key-encryption key and never persisted in plaintext. The key-encryption key runs locally today; the AWS KMS, Azure Key Vault, and GCP KMS adapters are contract-tested, fail-closed seams that bind to your own customer-managed key at deployment. The audit hash stays over plaintext, so integrity checks are unaffected.
Provider capacity is reserved before the models run and refunded after. An over-quota tenant gets a clean hold-and-escalate and never spends a call — verified to hold exactly to cap under contention.
Tenants rotate their own keys with a grace window, so in-flight calls never break. Self-service routes are strictly tenant-scoped, auth fails closed, and no API key is ever logged.
Control plane sustained ~805 requests/sec at concurrency 40 with zero errors and zero isolation violations; quota counters held exactly to cap under contention with no oversell; pre-action latency is provider-bound at ~1.7s median.
Rollout boundary. The measured throughput is development-environment evidence — directional proof that the architecture holds, not a production-RPM guarantee. For an SI-scale rollout, the named next steps are binding the contract-tested cloud KMS adapters to the buyer’s live KMS and running a formal production load test.
One control plane for AI answers, agent actions, policy enforcement, and regulator-ready evidence — not a single feature clipped onto a model.
Each verdict writes a single, tamper-evident bundle — SHA-256-chained to the one before it — into a write-once store: the database rejects edits and deletes at the trigger level, and when the agent acts, that downstream action is cryptographically bound to its decision by appending a new linked record, never by mutating the original. The chain re-verifies independently: each record’s hash is recomputed from its own content and every link to the prior record is checked. Nothing is reconstructed after the fact; the decision and the evidence behind it are captured at the moment the action is gated.
What that means in practice. Your audit trail shows what your agent actually did — not just what it was cleared to do. The decision and the action it authorized stay linked in one record, so a reviewer sees intent and outcome together.
Built to integrate. Four exports ship today: a normalized governance JSON for programmatic ingestion, a flat CSV for spreadsheets and BI tools, a print-ready HTML report that saves to a board-ready PDF straight from your browser, and an OpenLineage event feed your SIEM or data-governance tooling can ingest directly.
A convincing demo is the easy part. What earns a place in a regulated stack is everything after — when the record has to hold up under audit, every client has to stay sealed off from every other, and the record has to export in formats your governance team can review or ingest — JSON, CSV, a print-ready report, and an OpenLineage feed for your SIEM today. That hardening is built in from the start, not added afterward.
The audit ledger rejects edits and deletes at the database trigger level — not in application code a later change could quietly weaken. When the agent acts, that action extends the chain with a new linked record; the original decision is never touched. Every record re-verifies from its own contents.
Records live in durable storage and outlive any restart. A database constraint prevents two records from forking off the same prior hash, and the integrity guarantees re-check themselves on every deploy — so what protects the record in a demo is exactly what protects it in production.
Every engagement is its own tenant, with per-tenant API keys and durable per-tenant policy. One tenant’s audit trail can’t be read — or even detected — by another. Isolation is enforced in storage, not left to convention; retention windows and export policies are set per engagement.
Two kinds of proof: a head-to-head accuracy benchmark on the current production lineup, and the measurement discipline these guardrails were built on before they ran under Fortune 100 contracts.
MMLU-Pro, N=100, on the current production lineup — Claude Opus 4.5, GPT-5.1, Gemini 2.5 Pro, Sonar Pro. Best single model: 82.0% (Claude Opus 4.5). Raw output files reproducible on request.
These guardrails weren’t built in a lab — they were built measuring real outcomes, with an auditable record behind every score. That same verification discipline now runs under Fortune 100 contracts.
Labeled and reproducible. The win isn’t betting on the one model that happens to be right on a given call — it’s a cross-model verdict with a record behind it, built to govern AI decisions rather than replace the people who own them. A separate K-12 benchmark reached 100% consensus (+1.3pp over the individual-provider average) on a prior model generation, cited for K-12 contexts only. Every raw benchmark output file is reproducible on request.
Veridect sits beside your models — four of them — with its own confidence scoring, risk routing, and chained audit. Six capabilities that a single-vendor LLM, a gateway, or a prompt-filter does not give you together.
On every high-stakes call, an oppositional pass actively tries to break the consensus answer — surfacing the omission, the edge case, the unstated assumption that a single model, or even a clean majority vote, would wave straight through. It’s the difference between four models that happen to agree and a system built to find where they’re confidently wrong. A gateway, a guardrail library, or a single-vendor LLM can’t reproduce it without rebuilding the whole stack — which is why it anchors the six capabilities below.
Four leading models verify every high-stakes answer or action in parallel — not one model checking itself.
Disagreement is attributed to reasoning gaps, stale knowledge, hallucination risk, or domain mismatch.
A SHA-256-chained, tamper-evident bundle of the action, the model votes, and the verdict logic — built for regulator review.
Agent tool-calls are intercepted and returned greenlight / escalate / block in roughly 3.1s, before anything executes.
High-risk actions trigger an oppositional pass that surfaces omissions and gaps a single answer would miss.
Swap providers for self-hosted or air-gapped models and the governance surface — heatmap, audit — is unchanged.
The incumbents are structurally anti-aligned with cross-provider consensus — a hyperscaler has little incentive to grade a rival’s model — and the gateway and guardrail vendors lack the integrated audit and failure-mode decomposition layer. Veridect’s durable lane is the intersection of five capabilities, owned together:
The four providers are already collapsed behind one interface, with circuit breakers, automatic failover, and caching built in. That structural choice is the speed — it removes most of the integration work you’d take on building your own consensus layer. You integrate once, against standard auth patterns, instead of wiring up four vendors with four rate limits, four error patterns, and four auth models.
Claude Opus 4.5, GPT-5.1, Gemini 2.5 Pro, and Sonar Pro sit behind a single REST endpoint. Circuit breakers, automatic failover, and caching are already built — far less integration work than rolling your own consensus layer.
Each buyer gets API keys you provision and revoke on demand, durable per-tenant policy overrides, isolated audit records, and usage limits — enforced by tenant-scoped storage, strict tenant-ID validation, authenticated-tenant resolution, and per-tenant rate limiting. The full isolation inventory is available under NDA.
Department- and industry-adaptive consensus weights, tuned to the decisions your sector actually makes — finance, insurance, healthcare, legal — not a one-size-fits-all model.
Adoption is a scoped engagement. Every buyer gets:
The audit trail is built to support model-governance and AI-risk review across the regimes your second and third lines already answer to.
How the audit trail maps to what each regime actually asks for:
| Framework | What it asks for | How Veridect supports it |
|---|---|---|
| SR 11-7 | Independent model validation and documented audit trails | A second, independent cross-provider check on every answer, plus SHA-256-chained bundles capturing the action, the model votes, and the verdict logic |
| NAIC Model Bulletin | Documented governance over AI used in insurance decisions | Replayable per-decision records and configurable escalation thresholds for protected-class-adjacent actions |
| FDA AI/ML SaMD | Traceability and change control over model behavior | A provider-invariant governance surface and bundles that record the exact model lineup behind every decision |
| NIST AI RMF | Measurable, documented AI risk management | Calibrated confidence scoring captured in the record on every answer and action, with disagreement decomposed into failure modes by the consensus engine |
| EU AI Act | Logging and human oversight for high-risk AI | Pre-action gating that routes high-risk actions to human review, with a tamper-evident log of every verdict |
Veridect supplies the verification and audit layer these regimes call for — it is not itself a certified or FDA-cleared system.
Operates on SOC 2 Type II · SOC 3 · CSA STAR-certified hosting infrastructure.
Running in production inside Fortune 100 enterprises, with a multi-year track record of passing their annual vendor security risk assessments.
Hash-verified, replayable records designed for model-governance review such as SR 11-7.
A single REST endpoint, deployable on AWS, Azure, GCP, or on-premises in your own environment.
SOC 2 Type II, SOC 3, and CSA STAR attestation come from the certified hosting infrastructure Veridect runs on; the detailed compliance binder, BAA status, and additional certification roadmap are available under NDA.
Pick one and run it yourself. Every verdict, heatmap, and audit bundle is reproducible right now.
Our June 26, 2026 announcement — “Becker Transactions Announces Veridect, the Verdict Layer for Enterprise AI” — was distributed by Becker Transactions and carried on AP News, the National Law Review, and MENAFN. Read it where it was published, download a clean copy of either page, or download the full release.
No long procurement runway just to see whether this fits. You can go from reading the docs to running Veridect on your own data in three steps.
Run the three live demos and read the public integration guide, white paper, and independent validation summary. Everything you need for a first technical read is public — no sign-up, no call required.
When you’re ready to see it on real work, we stand up a dedicated trial in an environment you control — your data, your workflows, your team — before any licensing or commercial discussion.
Once we’re working together, a mutual NDA opens a private integration document built to your specs, followed by hands-on working sessions to wire Veridect into your stack.
Run the demos yourself first. When you’re ready to put it in front of your own data and team, we’ll set up a working session in an environment you control.
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