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 starting 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 manual setup 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 Repeating Work. 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
BAF Adversarial Reasoning
Agents for Defensible Gap Analysis
ProfytAI’s Bipolar Argumentation Framework (BAF) applies structured, qualitative challenge-and-defense reasoning to regulatory analysis. Every obligation is contested, defended, and adjudicated.
Agent 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. BAF 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 a BAF?
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, 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.
How Agents Share Context
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.
ProfytAI separates reading from adjudication
- Worker agents carry the bank corpus and do the data-heavy reading across your policy library.
- The orchestrator never ingests that corpus. It reasons from a compact structural index of each document plus the structured findings workers return, so its working memory stays lean at any library size.
Heavy context is paid for once per run
- The corpus is a stable, cacheable prefix on every worker call: processed once, then reused for each obligation instead of re-sent every time.
- Reasoning context never bloats as the run progresses.
- Accuracy holds from the first obligation to the last.
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.
Regulatory Infrastructure
& Analysis in Production
Obligation extraction, gap analysis, and regulatory mapping running today against jurisdiction-specific regulatory text.
See the BSP Case StudyCompliance Packs, Kits, and On-Demand Configuration
Regulator-aligned packs and audit-ready templates, with jurisdiction-scoped configuration generated on demand via kits and packs.
Regulatory Infrastructure
APIs & MCP Servers
Structured obligation, control, and evidence data exposed via REST API and MCP servers. 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 live today; the compliance packs and audit kits it powers ship Q3 2026. Reach out to see what infrastructure-grade compliance documentation actually looks like.