Provenly is the decision layer between enterprise evidence, AI agents, and business execution. It turns documents, data, and policy into evidence-backed, policy-verified, auditable decisions — with controlled human or autonomous execution.
The next wave of enterprise AI is moving from generate to delegate. The missing infrastructure is the ability to prove, govern, and control decisions at runtime.
The bottleneck is no longer model access — it's trustworthy execution inside regulated, evidence-heavy environments. Firms don't lack AI tools; the advisor's desk alone spans 600+ of them. They lack a way to trust AI with a consequential decision.
Facts live across PDFs, statements, policies, and systems of record. AI gets an incomplete context window, not a defensible evidence set.
A model can produce a persuasive recommendation without preserving which facts, policy versions, and tools drove it.
Once an agent can call APIs and move records, a hallucination becomes an operational and control problem — not a text-quality one.
Identity, policy, model controls, approvals, and audit evidence sit in separate systems — nothing is provable end to end.
Provenly connects evidence to a verified decision, then to controlled execution — as four composable fabrics, not another point tool.
Classify, extract, normalize, and validate structured facts from complex documents into provenance-tracked Evidence Units.
Combine evidence, the knowledge graph, policy, and AI reasoning to produce grounded decision candidates.
Check evidence sufficiency, policy constraints, confidence, permissions, and approval requirements before anything is trusted.
Expose verified decisions through APIs and orchestrate downstream systems, workflows, and human approvals.
Documents and custodian feeds are classified, extracted, validated, and PII-redacted into Evidence Units — each pinned to a source, page, and fact.
Schema, required-field, business-rule, and cross-document checks gate a document before it becomes evidence — catching breaches the way an examiner would.
Evidence is checked against policy, producing a Decision Record: approve, refer, or decline — with cited reasons ready for an adverse-action notice.
Not a black-box answer — a machine-readable provenance chain that survives an audit and produces a reason on demand.
The outcome — approve, refer, decline, remediate — with severity.
Authoritative sources, timestamps, and extracted facts.
The model/agent, retrieved knowledge, calculations, and tool outputs.
Policy version, suitability/risk rules, permissions, and thresholds.
Deterministic checks, cross-source validation, confidence, exceptions.
Human or role authorization with timestamp and rationale.
The downstream action, transaction/workflow id, and resulting state.
A tamper-evident record for investigation, review, and examination.
“Verification is a control, not proof. Ambiguity fails toward human judgement — never auto-certification.”
One reusable decision fabric, deployed first where documentation is heavy and the decision must be defensible.
Advisor decision verification · suitability · fee & billing accuracy.
Evidence-backed recommendations and continuous suitability & fee checks — advisor productivity without surrendering the compliance record.
Underwriting · covenant & exception decisions.
Faster, document-grounded decisions with policy traceability and adverse-action reasons already on the record. See it live
Claims · underwriting · policy validation.
Evidence-driven decisions and controlled agentic operations across policy and claim documents.
Research · trade · reconciliation workflows.
Decision provenance, cross-source validation, and execution controls where accuracy is non-negotiable.
Automate the repeatable work, route only genuine exceptions to a person — so effort scales with exceptions, not with volume.
Workspace/tenant boundaries with tenant-specific policies, connectors, models, and retention — no cross-tenant leakage.
Separate synchronous decision APIs from asynchronous ingestion, verification, and long-running batch workflows.
Keep the decision contract independent of any single model provider; route by capability, cost, latency, and policy.
The most important control is at the point of action. An agent isn't trusted because it passed an eval — its proposed action must pass identity, authorization, evidence, and policy.
SSO/OIDC, RBAC/ABAC, tenant isolation, scoped agent permissions, approval-based elevation.
Encryption in transit and at rest, managed keys, secret isolation, retention, configurable residency.
Tool allowlists, scoped credentials, action budgets, prompt-injection defenses, and execution gates.
Source lineage, document hashes, timestamps, and tamper-evident records for consequential decisions.
Logical isolation by workspace, tenant-scoped authorization, strict cross-tenant retrieval boundaries.
Explicit delegation levels; high-impact actions can require named human approval before execution.
This describes the target enterprise architecture. Individual controls are implemented, planned, or customer-configurable; Provenly makes no certification or regulatory-attestation claims until formally obtained and independently validated.
Confident output with no source, no policy version, and no way to reconstruct it.
An outcome with citations, a reasoning path, and the exact policy version — reproducible on demand.
Manual verification of documents, calculations, and rules — costly, slow, and inconsistent.
The repeatable work is verified automatically; only ambiguous cases reach a person.
Weeks assembling evidence no one indexed, hoping the story holds together.
Every run retains its exact versions and evidence — an audit pack is generated, not reconstructed.
The same governed engine, whether you're reconciling an advisory fee or underwriting a loan.
We're onboarding design partners across wealth, lending, and insurance. Bring a real workflow — we'll show you the governed version of it.
Occasional updates on verifiable decision intelligence for financial services.