Generative AI, LLM & RAG
Large language models become truly useful when they can access your context. With RAG (Retrieval-Augmented Generation), we connect LLMs to your documents, databases and systems to deliver accurate answers with cited sources and access control.
Expected outcomes
What we deliver
A conversational knowledge base over manuals, contracts, policies and tickets.
Find information by meaning, not just keywords, across large repositories.
Read PDFs, emails and forms and output structured data to your systems.
Quality testing plus monitoring of hallucinations, cost and latency in production.
Use cases
Tech stack
Case Studies
Full stack and analytics intelligence
An analytics tool that turns large volumes of historical data into predictions and reports for control engineers.
Web development and SEO
The Procurement Club’s content platform, with high performance, flexible content management and strong organic rankings on Google.
Frequently asked questions
RAG is a technique that retrieves relevant passages from your documents and passes them to the model along with the question. The answer is then based on current, verifiable information, reducing hallucinations.
In most cases, no. RAG with good data and prompts solves the problem at a lower cost. Fine-tuning only makes sense in specific scenarios, and we assess that during discovery.
PDF, Word, spreadsheets, web pages, SQL databases, tickets, emails and more. We build ingestion pipelines with automatic updates.
Let’s talk
A 30-minute call to understand your challenge and recommend the best path forward. No strings attached.
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