Southeast Asia's financial system is growing faster than its compliance infrastructure. We are building the structured regulatory backbone. The layer that every bank, fintech, and regulated enterprise in the region can operate on.
MAS, BSP, OJK, BOT, BNM, SBV,NBC, RMA, MMA, AMBD, CBSL
Banks, fintechs, insurance, and other regulated entitiesare all starting from scratch over and over.
Every obligation verbatim, traceable, and reproducible.Built to withstand examination.
Most compliance tools sit on top of unstructured documents. We start at the data layer by modeling regulatory obligations into a machine-readable format that our AI systems can use.
The Evidence Standard
Why General-Purpose AI Cannot Produce Audit-Grade Compliance Evidence.
A technical breakdown of why systems like Copilot fail compliance examination standards - and how this infrastructure is designed differently.
A neuro-symbolic extraction process where neural inference proposes candidates, deterministic verification proves provenance, and BAF adjudication formally accepts every regulatory obligation.
Regulatory PDFs enter our intelligent document pipeline. Deterministic parsing and configuration-driven processing produce framework-specific extraction recipes.
Schema-constrained LLM inference, restricted to judgment-requiring fields. The model is never trusted for source location, which is recovered deterministically by the verifier.
Byte-exact alignment to the parsed source. Source location metadata is recovered independently, never claimed by the model. Ungrounded quotes are rejected (hallucination guard).
A suite of falsifiable contracts spans structural, semantic, and accuracy dimensions. Strict invariants gate the build; quality heuristics gate against calibrated thresholds.
Three agents reason under formal, qualitative bipolar argumentation. The Judge returns a verdict plus a structured, self-healing, machine-actionable remediation directive from a closed action set.
Each obligation ships as a structured, self-contained record. Audit-defensible ground truth our applications and partner institutions build on.
ProfytAI’s Bipolar Argumentation Framework (BAF) applies structured, qualitative challenge-and-defense reasoning to regulatory analysis. Every obligation is contested, defended, and adjudicated.
Regulation
MORB Part IX §931
Required obligation text
Bank Document
MTPP
Parsed to machine-readable data
Defender
Coverage Ledger
Prosecutor
Gap Ledger
Judge
Reconciles the Ledgers
Reasons only from the structured findings, never the raw corpus.
Two Inputs
The regulation (MORB) and the bank's document (MTPP), parsed to machine-readable data.
Adversarial Reasoning
A Defender's coverage ledger (green) weighed against a Prosecutor's gap ledger (red).
One Traceable Verdict
The Judge reasons only from the structured findings, reconciling one of four statuses.
The diagram above, expanded into its four pipeline stages,
from raw documents to a cited, defensible decision.
Regulations, policies, procedures, controls, and legal documents are ingested into our intelligent document processing pipeline.
Verbatim quotes and source anchors tie every obligation to its supporting evidence in the bank's documents, building an auditable cross-reference per record.
verbatim anchors, not summaries
Defense/Support and Challenge/Attack arguments are weighed together.
Coverage status, rationale, and cited evidence trail.
Evidence trail + rationale
Every finding links back to source clauses and the argument path.
A structured reasoning approach where every claim is contested by an attacking agent, defended by a supporting agent, and adjudicated through structured, qualitative reasoning, so the path from evidence to verdict stays visible end-to-end.
How ProfytAI Uses It: ProfytAI implements the BAF pattern with 3 specialist LLM agents: a Prosecutor Agent, a Defender Agent, and an Orchestrator/Judge Agent that reconciles their structured findings into one verdict.
Defensible gap analysis where every conclusion can be opened, examined, and explained back to the source evidence, not a black-box AI verdict.
Every score traces back to the exact evidence and reasoning that produced it.
One agent cannot hold your full policy library and its own reasoning without degrading. The context window fills, attention dilutes, and earlier context gets dropped (context rot).
Most compliance data is locked in PDFs and spreadsheets. Ours is structured, modeled, and exposed via APIs and MCP servers, so AI can query, validate, and generate against it.
One Call · Obligation, Controls, Evidence, Citation
AI coding agents can call structured obligations, controls, and evidence requirements on-demand, generating regulation-compliant implementations with traceability.
A graph of a jurisdiction's obligations and how they interconnect, so a change to one rule surfaces every obligation, control, and evidence item it touches.
Pull the obligation register for any MAS or BSP regulation. Map controls. Collect evidence. Export the audit trail. All structured. All machine-ready.
Map controls, track evidence, monitor gaps across your program
Obligation registers, structured and ready for export into your own systems
Pull any obligation by jurisdiction, framework, or risk domain
Start here
The structured regulatory data model is live today. The obligation register packs and data licensing ship Q3 2026. Reach out to see what infrastructure-grade compliance documentation actually looks like.