LLM integration and RAG, grounded in your own data
We integrate LLM providers into your product and build retrieval-augmented generation over your own documents and data — so answers are grounded in what your business actually knows, not just what the model was trained on.
Why grounded beats generic
A model with no access to your data will confidently answer from what it was trained on — which is rarely what your product needs. Retrieval fixes that by giving the model your own, current information to work from.
Answers From Your Own Data
Retrieval over your documents, database, or knowledge base, so responses reflect what is actually true for your product, not the model’s general training.
Fewer Hallucinated Answers
Grounding responses in retrieved source material, with citations back to it, cuts down on confident wrong answers — though no system removes the risk entirely.
No Vendor Lock-In
Built through provider-agnostic tooling, so switching or mixing model providers is a configuration change, not a rewrite.
Evaluated, Not Just Shipped
Retrieval quality and answer accuracy are tested against real queries before launch, and monitored after.
What we deliver
Model & Provider Integration
Wiring OpenAI, Anthropic, or another provider’s API into your backend and UI, with fallback and cost controls in place.
Retrieval & Knowledge Base Design
Chunking, embedding, and indexing your documents or data into a retrieval pipeline built for your content, not a generic template.
Prompt & Context Engineering
Structuring what the model sees — retrieved context, instructions, and history — so answers stay accurate and on-brand.
Evaluation & Monitoring
A test set of real queries to measure accuracy before launch, plus monitoring in production to catch drift and regressions.
Process
- 01
Scope
Identify the data sources worth retrieving from and the queries the system needs to answer well.
- 02
Build
Build the retrieval pipeline and model integration, testing against real queries as we go.
- 03
Ship
Deploy with monitoring in place, then tune retrieval and prompts against what real usage actually looks like.
What we build on
Open standards and published guidance, not a black box — each card links to the source.
Common questions about LLM integration and RAG
Retrieval-augmented generation: before the model answers, we search your own documents or data for the relevant pieces and hand those to the model as context. The model answers from what it was just shown, not only from what it learned during training.
Yes — that is the point. Your documents stay in infrastructure you control; the pipeline retrieves from them at query time rather than sending your data off to retrain a model.
OpenAI and Anthropic most often, integrated through provider-agnostic tooling so switching later is a configuration change rather than a rewrite. We can also work with other providers on request.
No system eliminates them entirely. Grounding answers in retrieved, citable source material substantially reduces confident wrong answers, and we test against real queries to measure how often it still happens before you launch.
Whichever combination of your infrastructure and the model provider’s API fits your compliance and cost requirements — we design the pipeline around constraints you already have rather than imposing a fixed stack.
Yes. You own the codebase, the retrieval pipeline, and the configuration. It is documented and handed over runnable by your own team.
Explore other services
Book a call with our CEO
Denys Havryliak
Founder & CEO
- 10+ years in Software Engineering
- Master’s in Cybersecurity
- Deep, current knowledge of AI tooling
You’ll talk to the person who builds. Denys works hands-on across product, architecture, and delivery — and keeps a close watch on what today’s AI tooling can genuinely do in production, not just in a demo.