Compliance Infrastructure for
Regulated Industries.
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 rebuilding from scratch over and over.
Every obligation verbatim, traceable, and reproducible.Built to withstand examination.
How It's Built
Four Layers. One Unified
Regulatory Data Model.
Regulatory Obligation Mapping
Source → Object
Pre-structured obligation registers mapped to MAS TRM, BSP MORB, BSP AMLC, and other regional frameworks. Use them directly in your compliance program, no rebuilding required.
Policy Framework Engine
Framework → Control
Jurisdiction-specific policy templates that slot directly into your compliance program. Structured for your team to own, not generic documents that need weeks of rework.
Evidence Collection Layer
Control → Proof
Pre-built audit evidence structures so your team collects once and reuses across every audit cycle. Stop starting from scratch every quarter.
Audit Documentation Packs
Evidence → Package
Ready-to-use documentation packages that survive regulator scrutiny. Structured for MAS and BSP with more jurisdictions shipping continuously.
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.
How Teams Use It
Your Team Stops Rebuilding. Starts Shipping.
Pull the obligation register for any MAS or BSP regulation. Map controls. Collect evidence. Export audit packages. All structured. All machine-ready.
Dashboard view
Map controls, track evidence, monitor gaps across your program
Audit export
Package structured evidence and obligation citations for regulators
API access
Coming SoonPull any obligation by jurisdiction, framework, or risk domain
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.
Structured Data That
AI Can Actually Reason Over.
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.
Claude & LLM Integrations
MCP Server + API Surface
AI coding agents can call structured obligations, controls, and evidence requirements on-demand, generating regulation-compliant implementations with traceability.
- MCP tools for obligations, controls, and evidence schemas
- API calls from coding agents during build and review
- Compliance-by-construction with citations
- Guardrails constrained to known regulatory requirements
Knowledge Graph
Dynamic, Interconnected Obligation Model
A dynamic model of regulatory obligations across Southeast Asian jurisdictions,
that evolves with regulatory changes and enforcement actions.
- Cross-jurisdiction obligation mapping
- Regulatory change tracking
- Enforcement action integration (roadmap)
- Machine-readable output formats
Advanced Gap Analysis
QBAF Adversarial Reasoning Agents
for Explainable Gap Analysis
ProfytAI’s Qualitative Bipolar Argument Framework (QBAF) applies structural AI challenge-and-defense reasoning to regulatory analysis. Every obligation is contested, defended, and adjudicated through adversarial evidence reasoning to produce transparent, explainable,
and audit-ready conclusions.
QBAF Decision Architecture
How ProfytAI compares regulations to banking documents
using source-grounded support and challenge reasoning.
1. Documents
Regulations, policies, procedures, controls, and legal documents.
- Regulatory source
- Bank document
- Legal / control evidence
2. Evidence Map
Exact text anchors connect obligations to bank evidence.
verbatim anchors, not summaries
3. QBAF Reasoning
Defense/Support and Challenge/Attack arguments are weighed together.
4. Decision Output
Coverage status, rationale, and cited evidence trail.
Evidence trail + rationale
Every finding links back to source clauses and the argument path.
What is QBAF?
A structured reasoning approach where every claim is contested by an attacking agent, defended by a supporting agent, and adjudicated under deterministic rules, so the path from evidence to verdict stays visible end-to-end.
How ProfytAI Uses It: ProfytAI implements the QBAF pattern with 3 specialist LLM agents:
a Prosecutor Agent, a Defender Agent, and an Orchestrator/Judge Agent, all operating on structured evidence.
The Result
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.
Roadmap
Where We Are. Where We're Going.
Infrastructure compounds. Every layer we ship makes the next one faster to build and harder for anyone else to replicate.
Structured compliance packs and kits
Usable, regulator-aligned frameworks for compliance teams across Southeast Asia. With more jurisdictions on the roadmap.
On-demand compliance configuration
Jurisdiction-scoped regulatory infrastructure generated on demand, via kits, packs, or MCP servers.
Regulatory infrastructure APIs
Structured obligation, control, and evidence data exposed via API. The data layer every compliance tool will integrate with.
The regulatory data backbone for Southeast Asia
A shared, evolving infrastructure layer that every regulator, institution, and AI compliance tool builds on.
Start here
The Infrastructure Is Live.
The Packs Are Coming Soon.
The structured regulatory data model is already powering our compliance packs and audit kits. Reach out to see what infrastructure-grade compliance documentation actually looks like.