Enterprise AI Governance: Verifiable RAG, Model Invariants & Hallucination Guardrails
Technical guidance on deploying enterprise generative AI: private document grounding, strict source citation, latency budgets, and compliance with the EU AI Act.
Flagship Product: Fraoula AI
Private, source-cited enterprise research assistant connected directly to internal company knowledge.
Architectural Pillars & Governance Invariants
Deterministic Grounding
Constrain language model generation to verifiable vector chunks extracted from verified enterprise document repositories.
Granular Document RBAC
Enforce real-time identity and permission checks so models only cite documents the requesting user is authorized to read.
EU AI Act Article 10 Auditability
Trace training data provenance, evaluation datasets, and runtime generation logs to satisfy high-risk AI regulatory standards.
Sub-500ms Vector Retrieval
In-memory indexing and hybrid sparse-dense embedding retrieval tailored for low-latency enterprise query answering.
Core Engineering Research & Benchmark Teardowns
Comparing Fine-Tuning and RAG Pipelines in Large Enterprises: Cost and Governance →
Detailed economic analysis of weight specialization versus dynamic retrieval-augmented generation.
Harnessing Retrieval-Augmented Generation to Transform Enterprise Knowledge →
Architecture for semantic chunking, embedding generation, and source-attributed synthesis.
EU AI Act Article 10 Compliance: Technical Data Audit Blueprint →
A concrete engineering checklist for training set validation, bias mitigation, and logging requirements.
The 3 AM Rejection: Inside Workday's AI Hiring Lawsuit & Algorithmic Liability →
Legal and technical breakdown of algorithmic bias audits and disparate impact in automated screening.
Validate Your Production Pipelines Before Schema Drift Occurs
Deploy Fraoula's sub-350ms in-memory inspection layer across Snowflake, Databricks, Kafka, and Postgres with zero disk storage.