AI-Powered Documentation Engine for Manufacturers
Multi-tenant SaaS platform that automates regulatory documentation and self-assessment for manufacturers entering international markets. RAG architecture with AI agents for document analysis, standard mapping, and verdict suggestion.
The Client
The client operates a SaaS platform for product manufacturers who need to prepare and maintain regulatory documentation across international markets. The standards are dense, change frequently, and a single wrong answer can hold up shipments at customs.
The founders came to us with deep regulatory expertise and a thesis: if we could give a manufacturer's documentation team an AI assistant grounded in the current state of the relevant directives and standards, we could replace days of legal-review work with minutes of guided answers — without giving up the audit trail those teams need.
The challenge was building something that could be both genuinely helpful and demonstrably trustworthy.
The Challenge
Manual compliance audits took weeks per product, blocking time-to-market in Europe
Document analysis was inconsistent across reviewers — same evidence, different verdicts
Standard-to-product mapping was error-prone and impossible to audit at scale
The Approach
This was a RAG (retrieval-augmented generation) project from day one. We built a multi-tenant Laravel + Filament platform on top of PostgreSQL, with Pinecone as the vector store for the directive corpus. Every answer the AI gives is grounded in retrieved source passages with full citations — no "trust me" answers, ever.
The multi-tenant architecture matters because different manufacturers have different product portfolios, internal documents, and compliance histories. Each tenant gets their own private corpus layered on top of the shared regulatory base, and the retrieval layer respects those tenant boundaries strictly.
We chose PostgreSQL over MySQL specifically for the JSONB-heavy compliance metadata and the row-level security model that backs the tenant isolation. OpenAI handles the generation, but the value is in the retrieval layer we built around it.
Our Solution
RAG-powered document analysis grounded in the applicable industry standards and tenant-specific evidence
AI agents for standard mapping — clauses linked automatically to product features and test units
Verdict suggestion with human-in-the-loop review, full reasoning traces, and audit trail
Related work
Other businesses running on the same system.
