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AI agents and LLM apps: RAG, structured outputs and document automation

Assistants that save your team hours.

Large language models are good at reading and writing, but on their own they don't know your documents, can't act in your systems and don't return data in a shape your software can trust. That is the engineering we do.

Book a free call →See what you get ↓

Who it’s for

Is this you?

  • Companies with large document sets (policies, contracts, manuals, wikis) that staff search all day.
  • Operations teams processing invoices, forms or emails by hand.
  • Product teams adding an AI assistant or copilot to their own software.

What you get

Here’s what’s included.

  1. 01RAG knowledge assistants

    Your PDFs and wikis indexed, searched in natural language, and answered with citations back to the source.

  2. 02Structured outputs

    Documents and messages turned into fields (for example an invoice's supplier, dates and totals) validated against Pydantic schemas before they reach your systems.

  3. 03Document automation

    Extraction on the way in, and PDF or Word generation on the way out.

  4. 04Agents with tools

    Assistants that look things up and take actions in your systems, with a human approval step where a mistake would be costly.

  5. 05AI inside your product

    Copilot features such as a tutor that knows the user's material or an assistant inside an operations platform.

How it works

5 steps. No surprises.

We build retrieval (RAG) so answers come from your own documents with sources, structured outputs so AI results land in your database as validated fields, and agents that call your tools with approval steps where needed.

  1. 1
    Discovery call

    Free, 30 minutes: the documents, the questions and the systems involved.

  2. 2
    Proposal in 12 hours

    Scope, timeline and a fixed quote.

  3. 3
    Data and retrieval

    Ingest and index the sources; decide what the AI may and may not access.

  4. 4
    Build on staging

    The assistant or pipeline on a staging link, tested on your real documents, with human review set up for low-confidence results. Weekly check-in call.

  5. 5
    Launch and support

    30 days of bug fixes included, then an optional maintenance plan.

How every project runs →

LLM apps we built

RAG Knowledge Assistant→
RAG Knowledge AssistantKnowledge assistant
Echo AI→
Echo AIAI study companion
APEX→
APEXAI strength-coaching platform
AI Trivia Game→
AI Trivia GameAI trivia game

Tools we use

  • OpenAI API
  • Claude API
  • Pydantic
  • LangChain
  • Vector database
  • Python
  • React
  • Supabase

Questions

Questions? Straight answers.

Still unsure?

Ask me directly on a free 30-minute call.

Book a call →
RAG or fine-tuning?

For answering from business documents, retrieval (RAG) is usually the right start: documents can change without retraining, and every answer can cite its source. Fine-tuning is for changing how a model writes or behaves, not for teaching it your facts.

Can answers cite their sources?

Yes. A RAG assistant can return the passages and documents each answer is based on, so people can check it.

OpenAI or Claude?

We use both through their APIs and choose per task, based on output quality, cost and data requirements.

What happens when the AI is unsure?

Low-confidence results are routed to a person for review instead of being passed on automatically.

How is access to our data controlled?

The AI only reaches the sources and systems you approve. Credentials are kept in an encrypted password manager, access is least-privilege, and all access is removed at handover.

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