Eligibility, approvals, routing, pricing tiers
- Keeps every rule current and blocks retired versions
- Proves which rule and version produced each outcome
- Turns repeated manual overrides into rule updates
KrisVen is the trust layer for enterprise decisions. It reasons like your best people and guides each call, for deterministic and probabilistic decisions alike. Every outcome is sealed in your Decision Ledger.
Most decisions follow a predefined rule set. The rest call for judgment. KrisVen enhances trustworthiness for all decisions.
Ingests raw and contextual data, structured or unstructured, into one living context for every decision.
Data Stores, Lakes, Warehouses and GraphsAgent intelligence that mirrors human judgment, applied against the rules in force today.
Expert ReasoningDecides when it is confident. When it isn't, it guides a person through a prepared case, like a mentor would.
Human-in-the-loop by exceptionSeals evidence, rule version and approver into a tamper-evident record you can replay any time.
Decision LedgerDecision intelligence recommends. Evals score the model. KrisVen proves the decision was right.
Vendor-neutral and yours to keep: one tamper-evident record for every decision, whichever rules engine, model or agent made it.
Ten questions, about three minutes. It works for decisions made by rules, by AI or by people with AI help. Your score updates as you answer.
We're working with a small group of design partners.
Take the AI Decision Trust Score and see where your decisions are exposed.
Pick one workflow and baseline its trust score and human review time.
Your private KrisVen Studio: run one workflow end to end, starting on synthetic data.
Consulting firm or AI platform? Add Expert Reasoning and the Decision Ledger to what you already deliver, under your own brand.
A 30-minute call to review your score and scope your 30-day KrisVen Studio. Tell us about the workflow and pick a time.
Notes on decision evidence, governed memory and the questions people ask about AI decisions: from examiners in lending and banking to finance teams, partners and customers in retail and beyond.
Retailers let AI set prices, allocate stock and approve returns every day. When one of those calls goes wrong, the question is not whether the model was good. It is whether anyone can show what it saw.
Read the postOutside banking and insurance, nobody audits your AI decisions. That is not the same as nobody asking about them.
Banks already govern what goes into their AI. Almost nothing governs what comes back out, until an examiner asks a question nobody can answer.
Most fintech AI budgets point at the model. Most of the avoidable cost, and most of the value on the table, sits one layer below it, in the data the model is fed.
A retrieved fact and a verified one look identical to a language model. In commercial lending, that difference is the entire job.