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AI Integration & RAG Engineering

For teams who want AI inside their own product or data. We build retrieval pipelines that find the right information, evaluation suites that prove it works, and private deployments when data can’t leave your servers.

The problem

Plugging a model into your data looks easy in a demo, then gives vague or wrong answers in real use. Without retrieval done properly and a way to measure quality, you can’t trust it.

What changes

  • Answers grounded in your documents, with citations
  • Measured quality, with tests that catch regressions
  • Private, on-premise options when data must stay in-house
From our work: Grounded Document Research Assistant

Capabilities

What we build for you

01

Document ingestion

PDF, Word, Excel, PowerPoint and web pages, with structure-aware chunking chosen per document.

02

Hybrid retrieval

Vector search combined with keyword search, rank fusion and reranking, so the right passage wins.

03

Evaluation

Golden question sets, precision and recall checks, hallucination probes and regression gates.

04

Private and local models

Open models running on your own hardware through Ollama, with cloud calls only by explicit consent.

05

Fine-tuning

QLoRA fine-tunes when they help, and honest RAG-versus-fine-tune comparisons when they don’t.

06

Multi-provider

OpenAI, Anthropic, Groq and open models behind one interface, with structured outputs.

Proof, not promises

Things we’ve built with this

Deliverables

What you get

  • Retrieval pipeline and vector store
  • Evaluation suite with baseline results
  • Integration with your app or API
  • Private deployment option
  • Architecture and runbook docs

Tools

What we build with

We pick the model and tools per project, based on accuracy, cost and where your data is allowed to go.

pgvectorQdrantChromaDBLangChainLangGraphOllamaFastAPIPython

FAQ

AI Integration, answered.

Didn’t find what you were looking for? Ask us directly.

Ask a question

Most business use cases need retrieval, because your information changes. Fine-tuning helps with tone or narrow tasks. We test both when it matters and recommend based on results.

Yes. We can run models and embeddings on your own infrastructure, so documents never leave it.

Every build comes with an evaluation set from your real questions, and the results before launch.

Let’s find where AI can take work off your plate.

Book a free call. We’ll look at how your business runs and tell you honestly what’s worth automating, and what isn’t.