Metatate is a B2B SaaS platform that removes cross functional complexity in data and AI compliance. The platform transforms legal documentation into actionable data policies through its Policy Agents system. It supports the full compliance lifecycle, from policy creation to application and real time observability.
Problem: complex compliance workflows slow business teams
Before implementing AI compliance software, compliance workflows relied heavily on legal teams and structured forms. Business users had to interpret dense policy documents or wait for expert reviews to determine whether a data activity met internal or regulatory requirements. Policy knowledge remained inaccessible to non legal teams, and teams manually interpreted policies stored in PostgreSQL repositories.
This led to slow turnaround times for compliance clarifications and inconsistent risk assessments across departments. As AI and data use cases expanded, complexity increased across the organization. As Metatate scaled across enterprise customers, the need for real time, self service compliance guidance became critical. The MVP supports English as the primary language, with architecture designed to support future expansion into additional languages.
Solution: AI compliance software powered by conversational intelligence
GoML designed and delivered MVP AI compliance software that interprets natural language descriptions of data activities and maps them to relevant policies stored in Metatate’s repository. The solution uses an Agentic AI framework that breaks queries into steps such as intent detection, policy retrieval, risk evaluation, and response generation to ensure accurate, grounded outputs.
This AI compliance software validates policy inference accuracy and structured risk assessment, creating a strong base for future AI driven compliance automation.
Conversational compliance interface
Users access the AI compliance software through a standalone web chat built with Streamlit or React. They can describe data scenarios in plain language, upload documents, and receive structured guidance with policy references, risk levels, and next steps.
RAG powered policy inference engine
The AI compliance software uses retrieval augmented generation to match user input with relevant policies. It leverages Claude 3.5 or 3.7 via Amazon Bedrock, vector search through OpenSearch or Neo4j, and structured policy chunks from PostgreSQL to deliver grounded, context aware responses.
Policy repository integration
The system securely connects to Metatate’s policy repository, structures and embeds policies into a vector database, and retrieves them in real time during conversations. The MVP avoids any production schema changes.
Risk assessment and actionable guidance
The AI compliance software provides policy references, clear risk levels, plain language explanations, and recommended next steps. For example, it can flag consent or cross border risks in AI training scenarios and suggest mitigation actions.
Backend infrastructure and security
The solution runs on AWS using Amazon Bedrock, Python services, DynamoDB, and OpenSearch or Neo4j. It includes secure APIs, encrypted data handling, and role based access controls.
Testing and validation
GoML tested predefined compliance scenarios to measure policy matching accuracy, risk relevance, clarity, and response time. SMEs validated outputs against expected compliance interpretations.
Impact
- 60% fewer manual queries to legal teams
- 40% faster policy clarification
- 70% better access to compliance documentation
- Consistent structured risk scoring across scenarios
About
Before Gen AI and after Gen AI
“With Metatate’s AI compliance software, we transformed static policy repositories into an interactive compliance intelligence layer that delivers real time, scenario specific guidance to business users.”
Prashanna Rao, Head of Engineering, GoML
Key takeaways for SaaS compliance platforms
Common challenges
- Legal documentation is hard for business users to interpret
- Manual compliance reviews slow product innovation
- Policy repositories lack intelligent retrieval
- Risk scoring varies across teams
Practical guidance
- Build AI compliance software as a self service compliance layer
- Use retrieval augmented generation to ground outputs in real policies
- Structure responses with risk indicators and next steps
- Design architecture on AWS for secure and modular scaling
Ready to modernize compliance workflows with AI compliance software?
Partner with GoML to accelerate the development of production ready AI compliance software with AI Matic.
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