The AI-native semantic and verification layer for insurance and reinsurance: model your business once, represent it everywhere, and prove every action an AI takes is authorized, correct, and current — for the EU, UK and Switzerland, built to scale as a global SaaS.
In a regulated business, an AI answer is only worth anything if you can prove it is authorized, correct, current, and honest. Everyone else bolts governance on after the model. Verum builds the proof into the foundation, so the same evidence survives a regulator, a CRO, and a procurement team.
Verum holds a single authoritative model of the insurance business — defined independently of any database or vendor — and your lakehouse tables, graph, SQL views and APIs are all generated projections of that one truth. This kills the failure mode where "premium" or "loss" means five subtly different things in five systems. And the concepts that are legally and financially distinct — a Treaty is not a Policy, a ceded recovery is not a claim payment — are guaranteed by machine never to collapse into each other.
WrittenPremium is modeled once (primary-owned) and projected to a single physical table. On the reinsurance side, CededPremium is a separate entity, DERIVED_FROM it via a declared cross-chain join. The reinsurance analyst sees ceded premium computed from the exact same written-premium record the underwriter booked — same number, no reconciliation — yet CededPremium keeps its own identity key and its own IFRS 17 "reinsurance contracts held" treatment, so the two are never silently merged.
One fact · two lenses · one join · zero driftEvery regulated action a Verum agent can take — write a motor policy, move a bodily-injury reserve, cede a treaty layer, sign off an ORSA — passes through a machine-checked contract layer before it can run. Verum doesn't test that the agent behaves; it proves the agent cannot misbehave, using the same formal-methods tool family (Dafny / Z3 / TLA+) that AWS uses to verify S3, DynamoDB and Lambda. That turns "our unit tests passed" into "here is a theorem that holds for every user, every schedule and every input."
A proof is static; the live agent has to behave. Verum's agents won't answer an ambiguous regulated question with a confident guess — they surface exactly what they need to know first. Ask "what's the capital requirement for this portfolio?" and a plausible-but-wrong number is the dangerous answer. Verum asks which rulebook, which basis, and as of when — before it commits to anything.
Verum keeps a governed watchlist of the authorities an EU/UK/CH insurer actually lives under — Solvency II, DORA, the EU AI Act, GDPR, IFRS 17, EIOPA/PRA/FCA/FINMA output, ACORD and Lloyd's — and on a fixed schedule re-fetches, snapshots and version-checks each one. Separately, every standards citation embedded in the model is machine-verified against the persisted text of the real regulation, so a plausible-but-wrong citation is caught by a machine, not discovered in an audit.
The solvency-ii entry now records the amendment by Directive (EU) 2025/2 (CELEX 32025L0002, staged from 30 Jan 2027). Found via the currency check, snapshotted, cross-verified against EUR-Lex, recorded as decision: action, then impact-evaluated into concrete ontology changes: a two-part SFCR split, a climate-risk enum on the ORSA entity, new liquidity-risk attributes. And the citation checker's real catches: EMIR "Art.12 trade confirmation" was actually Penalties (fixed to Art.11(1)(a)); CSDR "Art.3 settlement discipline" was actually book-entry form.
source drift → dated snapshot → impact review → ontology change · every step evidencedVerum turns the semantic layer into a live action surface: an agent resolves a plain-English request to the exact governed tool, checks the caller is allowed to run it, executes multi-step regulatory workflows that pause for a human sign-off, then returns a full audit trail. Below is the mechanism that makes it safe — pick a role and watch the same request get allowed or refused. This mirrors the runtime's real entitlement gate, live behind an access key today.
Choose who is asking. The gate decides before anything runs — a denial is a structured refusal, not a silent failure.
Proof only matters if it answers the question the accountable person is actually asking. Pick a desk — the one worry that keeps them up, and the exact Verum evidence that puts it to rest. Every figure here is the same one from the story above, turned to face a different signature.
Choose a role. Verum reframes its evidence around what that desk is accountable for.
A vendor that tells you exactly what is live is a vendor whose "live" you can believe. So here is the whole picture, without gloss.
The bottleneck on AI in insurance was never capability — it was evidence. A CRO, a named SMCR manager, or a bank's model-risk committee cannot deploy a system whose answers can't be shown to be authorized, correct and current. Verum makes that evidence structural: one model that can't drift, actions proven before they run, agents that refuse to guess, a rulebook monitored automatically, and a runtime that halts and explains itself on screen. It is the layer that lets you say yes to AI in the parts of the business where "the test passed" was never going to be enough.
The model is technology-agnostic and the representations are generated to a pattern — so it rides on your existing lakehouse, graph and API estate, not a rip-and-replace.
Built to run on-premise or in a customer's own cloud where data residency demands it — the same verified layer, wherever the data must live.
From a first EU / UK / CH footprint toward a global SaaS: model once for one carrier, then roll the same verified layer across entities, jurisdictions and both value chains. The proof travels with it.