Verify. Govern. Prove.
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 high-stakes 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. Verification is risk-routed to keep costs in line — the full four-model panel engages on high-stakes, high-impact calls, routine traffic takes a lighter path, and your team tunes what counts as high-stakes to its own risk policies. 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.
Intelligence makes your AI powerful. Trust is what lets you put it near money, patients, and contracts. Veridect is the trust layer — over any models you choose.
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. You’re never locked into a single vendor — as the frontier moves, any model can be swapped or reweighted without re-architecting your stack.
That four-model firepower — and the adversarial verifier mesh layered on top of it — is aimed at the high-stakes and adversarial calls that warrant it: the decisions where one model being confidently wrong is expensive. It’s scrutiny matched to the stakes.
We don’t run four models on everything — by design.
Routine questions settle on a fast multi-model quorum, and the hard compliance rules — money over a limit, personal data, protected-class factors, health data — run in plain code at zero model cost. The full four-model cross-examination is spent where it belongs: the high-stakes, hard-to-reverse action where one wrong call costs millions. So four models isn’t four times your bill — it’s a few cents of extra checking on the one action in a thousand that could really hurt you. And one engine across six departments replaces a vendor per function, so your total AI spend nets down, not up.
Veridect doesn’t just return a number. It attributes the uncertainty to its source, so a low score tells you why.
Your proprietary data is your advantage — Veridect is built to protect it, not absorb it. Nothing you send is used to train a model or make our system smarter. 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.
Agreement is the point of consensus — and it’s worth the most when the models reach it on their own. When four models share the same blind spot or lean on the same stale fact, they can be confidently wrong together, and a plain “4 of 4 agreed” hides it. Veridect scores how independent a consensus really is and catches the dangerous case — strong agreement resting on weak independence, the consensus trap. On the pre-action gate, that elevated correlation risk escalates the action to a human, so unanimous-but-correlated agreement is never mistaken for four separate confirmations.
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. Veridect is the trust spine, not the plumbing — it doesn’t replace your identity, retrieval, or data pipes; it governs and proves the decision that runs through them.
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.
Routing a risky action to a human is the easy half. The hard half is proving the human genuinely reviewed it — instead of waving it through in three seconds. Veridect records each human resolution of an escalated action as a tamper-evident row in the same hash chain as the verdict, and reads, across a team’s reviews, when the pattern starts to look like rubber-stamping. It turns human-in-the-loop from a box on a policy document into a control you can prove is working.
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 →
Verifying one answer and gating one action were only the start. After that, the industry kept hitting three walls: risk that shows up only across a fleet of agents working together, audit records you still had to take a vendor’s word for, and no way to put a real, underwritable number on the liability of an autonomous action. Veridect built and shipped all three. Each is the thing the market has said can’t be bought — and they’re running live, together, on one Trust Layer today, a combination no single vendor has brought to the action layer.
Govern the constellation, not the star. A single-action gate can’t see the risk that builds up across agents. When several agents cooperate on one task — one reads sensitive records, another moves money, a third sends data out — no single step trips a rule, but the pattern does. Constellation adds a human-review escalation on top of the per-action gate — never approving or blocking an action on its own — and watches the whole workflow at once, escalating the instant their combined behavior crosses the line: catching the violation no single agent commits, with each agent’s part attributed.
See cross-agent governance →Don’t take our word for it — check ours. Proving a giant model is infeasible — but you don’t need to: the governance decision lives in deterministic code, so it can be re-derived exactly. Each model’s output is sealed inside the bundle, so Proof re-checks the decision and the integrity of the record rather than re-running the models. An auditor, a regulator, or your own risk team can re-derive the gate’s verdict and re-verify the SHA-256 hash chain on their own machine, offline, with a standalone verifier they download. And every bundle is signed: an Ed25519 proof-of-origin signature, checkable offline against our published key, proves the record came from Veridect and hasn’t been altered — the hash shows nothing changed, the signature shows who sealed it. The record proves itself — no live call to us, no need to trust our servers.
See verifiable governance →AI actions are becoming insurable events — and insurers need evidence, not assurances. Assurance turns each governed action into an underwriting-grade evidence record: a neutral, structured account of what was proposed, how it was governed, and how it scored, in a five-category taxonomy a risk-transfer partner can assess. It carries only structural identifiers — no free text, no personal data. Veridect isn’t an insurer and makes no coverage promise — it produces the evidence an underwriter needs to write the policy.
See the evidence record →One layer, three promises no single vendor makes together: Constellation governs the whole agent society, Proof lets anyone verify the decision, and Assurance turns every decision into evidence an insurer will accept.
How the three fit together. Constellation sits on top of the per-action gate and only ever escalates — and it’s built to stay up: if its shared state ever blips, the call simply falls back to the normal single-action verdict, so the primary gate keeps running no matter what. Proof independently re-derives the verdict and re-checks the hash-chained record offline, so anyone can confirm a decision without re-running the models. Assurance turns each decision into an underwriting-grade evidence record an insurer’s team can act on — we produce the evidence, not the pricing or the coverage, so Veridect stays your governance layer, not your insurer.
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.
You don’t just get an answer today — over time you build a permanent, tamper-evident record of every AI decision your business makes with Veridect, and the reasoning behind it. Your governance gets stronger the longer you run.
And you can see all of it in one place. The Governance Command Center reads that ledger back live — every verdict, across every agent, over time. Not one answer, but the whole program: how often your agents are cleared, escalated, or blocked; which policies are firing; where the risk sits — each line drawn from the same tamper-evident record an auditor can replay, tenant by tenant. Governance you can observe across every layer, over your whole fleet, as it happens.
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 system.
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.
Built to scale with you. Those numbers come from our development environment, and they point to an architecture that holds up well under load. Taking it to full SI scale is a short, well-worn last mile — pointing the already contract-tested cloud KMS adapters at your live KMS and running a production load test in your own environment.
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.
Signed at the source. Beyond the hash chain, every bundle is signed the moment it’s sealed — an Ed25519 proof-of-origin signature over its own hash. The hash proves the record wasn’t altered; the signature proves it came from us, and it can’t be forged without our private key. Pin our published public key once, and any bundle you download verifies offline, forever — on your own machine, no call back to us. The decision proves itself.
NewKeys roll; the record still checks out. The signing key rolls forward to a fresh version whenever you need it to — and every bundle ever signed keeps verifying, against the exact key version it was sealed under. Each bundle carries its own version and we publish the full key history, so an auditor confirms a signature offline years later, long after the key has moved on. Rotating the key never invalidates a single past decision.
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 controls 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 controls 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.
Real numbers, openly shared. The win isn’t betting on whichever model happens to be right on a given call — it’s a cross-model verdict with a record behind it, built to govern AI decisions, not to replace the people who own them. On a K-12 benchmark, consensus hit a full 100% (+1.3pp over the individual-provider average) — that result was on a prior model generation, so we cite it for K-12 work specifically. Every raw benchmark file is yours to check 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. Routine calls route lighter, and you decide which decisions warrant the full panel — so verification spend follows risk, not volume.
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 live system yourself 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 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 system 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.
Contact us →