Banks and insurers run on regulation. Their AI agents should know it.
The Operational Context Graph for financial services — BFSI ontology, public-data spine, agents that cite the rule. Healthcare ships today. Financial Services ships Q3 2026. The design-partner cohort is open — shaping the ontology before GA.
Every Amandil OCG carries the same dimensions — public-data spine, reference standards, regulatory frameworks, canonical operational example. The substance is per-vertical. Healthcare is live today; financial ships Q3 2026.
The same two sides as the live healthcare graph — the knowledge an agent reasons from, and the guardrails that govern what it does — instantiated for banking, markets, and insurance. Final scope shaped with the design-partner cohort.
L1 domains across banking, markets, insurance. Reference processes with owners, metrics, and dependencies. Anchored to ISO 20022, FIX, FpML, Basel III. Stitched to FDIC, FFIEC, SEC, NAIC.
Dodd-Frank, BSA/AML, SOX, GLBA, Reg CC. Policy clauses, audit checkpoints, escalation paths. Supervisory expectations and reporting requirements. Trace coverage on every agent action.
Three example archetypes shown on the healthcare page — Reconstructor, Pattern Analyzer, Submission Compiler — illustrate shapes the substrate produces. Each will be tuned to the BFSI substrate with the cohort.
Rebuilds a customer due-diligence packet from account history, OFAC near-matches, jurisdiction rules, and the supervisory expectation for the bank's tier.
Classifies AML flags against the BSA/AML rulebook, prior-decision history, peer-resolution patterns, and the policy clauses each flag type maps to.
Assembles a regulator-ready Call Report or Y-9 from the bank's operational data, cited against every line item's source schedule and FR Y-9 instruction.
The same three customer segments that anchor every Amandil vertical. Banks and insurers run the operations. Risk-and-reg practices compress discovery into branded analysis. Fintech and core-banking vendors embed the graph into their products.
"Where should we invest in AI?" You run the ops — risk, KYC, supervisory reporting, claims. Most teams pick what to automate based on a vendor pitch or what feels problematic to an executive. You need a benchmark-anchored answer — and agents that cite the rule, name the supervisory expectation, and return the audit trail.
You get: diagnose against the BFSI benchmark library → prioritize by automation score reweighted by your gaps → simulate the top candidates → ship agents grounded in the graph via MCP.
"How do we scale our financial AI engagements?" Every project opens with a discovery phase. Weeks rebuilding the same KYC workflow, the same control mapping, the same supervisory report. The sameness goes unclaimed — and the artifact ages on contact with reality.
You get: the discovery phase compressed to a 30-minute branded analysis. White-label per client. Branded blueprints, business cases, diagnostics. Multi-client workspaces with scoped grants.
"What grounds our financial AI features?" You're shipping AI inside your product. Every LLM-integrated feature hits the same wall — the model has no grounded knowledge of how a bank, insurer, or asset manager operates. Building the graph yourself is a multi-year, multi-million-dollar undertaking.
You get: a native MCP server. OAuth 2.0 plus headless bearer tokens, instantly revocable. Grant-scoped per partner. Versioned, validated graph surface. Multi-year build, solved on day one.
Shaping the BFSI ontology before GA. Design partners shape the ontology, get the substrate at cost during the cohort window, and lock pricing for two years post-GA. We don't take partners we can't deeply serve.
Tell us where you sit — where the AI-vs-ops gap is sharpest, what regulation pins your team down, and what you'd want the first agent to do. We'll come back with a roadmap shaped to that.