Technical Document AI
Turn your engineering documentation into an AI assistant your team can actually ask questions.
The problem
Your engineers have access to thousands of pages of procedures, manuals, safety documents, and technical specs. But when they need a specific piece of information under time pressure, they search manually — and that search often takes longer than the work itself.
The knowledge is there. The problem is that it's not retrievable at the speed your operations require.
Who this is for
- Engineering and industrial companies with large volumes of technical documentation
- Oil & gas, energy, utilities, manufacturing, and construction organizations
- Teams where senior employees hold critical knowledge that hasn't been systematically captured
- Operations where slow information retrieval creates risk or efficiency losses
What I build
A custom AI assistant trained on your documentation — not a generic chatbot, but a system that understands your terminology, your procedures, and your operational context.
What it does:
- Answers natural language questions using your actual documents as the source
- Returns precise, cited answers (not hallucinated summaries)
- Handles complex queries across multiple documents simultaneously
- Works with PDFs, Word documents, internal wikis, and structured data
- Integrates with your existing tools via API
What it doesn't do:
- Replace your documentation — it makes it accessible
- Require your team to change how they work — it fits into existing workflows
- Need ongoing maintenance from you once deployed
My approach
- Document audit — Understand what you have, how it's structured, and what queries matter most
- Ingestion pipeline — Clean, chunk, and embed your documents into a vector database
- Retrieval architecture — Design the search layer to balance precision and recall for your use case
- LLM integration — Connect retrieval to a language model configured for technical accuracy
- API and interface — Expose the system via API or a simple web interface your team can use
- Validation — Test against real queries from your domain experts before handoff
Use cases
- Drilling engineers querying well procedures in the field
- Maintenance teams checking equipment specs without digging through binders
- New hires onboarding faster by asking the knowledge base instead of colleagues
- Safety officers verifying regulatory compliance across multiple procedure documents
- Engineers checking whether a specific configuration has been tested or approved before
Experience signals
- 22 years as a Drilling Engineer at Repsol, ADNOC, and Moeve — I've been the user of this kind of documentation
- PhD in Engineering from Universidad Politécnica de Madrid
- Built a production RAG system for Quiet Links: 200+ academic papers, delivered in 6 weeks, saved an estimated year of solo development
- Technical stack: Python, FastAPI, LangChain, Weaviate, Pinecone, OpenAI, Anthropic
See the Quiet Links case study →
Frequently asked questions
What document formats do you support?
PDF, Word, PowerPoint, plain text, Markdown, HTML, and most structured formats. If your documentation is in a proprietary format, we evaluate it in the scoping phase.
Does my data stay secure?
Yes. I sign NDAs before any project starts. Documents can be processed entirely within your infrastructure if required. Nothing is sent to external services without explicit agreement.
How long does implementation take?
Typical projects run 4–8 weeks depending on document volume and integration complexity. A focused pilot with a single document corpus can be done in 2–3 weeks.
Do I need a technical team to maintain it?
No. The system is designed for handoff to non-technical users. I provide full documentation and a brief training session. Ongoing support is available as a retainer if needed.
Can it handle documents in multiple languages?
Yes, with some caveats depending on the LLM model chosen. Spanish, English, French, and German are well-supported. Specialized technical terminology in any language works best when the embedding model is evaluated against your corpus first.
Related articles
-
The Hidden Cost of Buried Knowledge
Why your engineers spend days searching for information that should take minutes to find — and what to do about it.
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Building a RAG System for Technical Documentation
A practical walkthrough of building a production-grade RAG system: document parsing, architecture decisions, deployment.