AI is only useful when it ships into a real workflow. We build retrieval-augmented systems that ingest your documents — PDFs, presentations, policy manuals, support tickets — and turn them into answers with source citations. We connect LLMs to your CRM, ERP, and internal APIs so your team can ask natural-language questions and get real data back. Every pipeline is built on infrastructure you control, with Claude or OpenAI as the model layer, Qdrant or pgvector for retrieval, and a query classifier that decides when to search documents, invoke tools, or hand off to a human.
What we won't do: build a chatbot that hallucinates because nobody hooked it to your data. Generate fake demos that look impressive in a slide deck but fail the first time a user asks a real question. Lock you into a single model provider — your prompts and your data stay yours.
The Teilor Opal KB is a working example: three services in Go, React, and Python, ingesting hundreds of internal documents and querying live ERP data, all in Romanian and English, deployed on the client's own infrastructure.
What you get
- Answers grounded in your data, not the model's training set
- Source citations on every response so users trust what they read
- Live integration with CRMs, ERPs, and internal APIs
- Multilingual support — Romanian, English, anything you need
- Self-hosted option for data sovereignty and compliance
- Built on infrastructure your team can maintain