RAG Implementation Company UK & US Startups

As a custom LLM integration company, we connect OpenAI, Anthropic Claude, Google Gemini, and open-source models directly into your product and internal tools — not as a bolt-on chatbot widget, but as a core part of how your application works. Every integration includes prompt engineering and a proper evaluation harness, so you know how the system performs before it reaches customers.

Where it earns its cost, we also fine-tune models on your own data, so outputs stay accurate, on-brand, and genuinely useful in production rather than impressive only in a demo. We design for model portability too — your product shouldn’t be locked into a single vendor’s API if pricing or quality shifts.

We build agentic RAG for SaaS companies whose product needs to ground LLM answers in live product data, documentation, and individual customer context — not a static knowledge base that goes stale. Rather than naive chunk-and-retrieve, our pipelines use contextual compression, cross-encoder re-ranking, and query routing to pull back the right information, not just the most similar-sounding text.

For multi-tenant SaaS platforms specifically, this means customer-specific, accurate answers without data leaking across tenants — a requirement most generic RAG tutorials simply don’t address. We also build in feedback loops so retrieval quality improves as real users interact with the system, rather than staying frozen at launch-day performance.

Our hybrid search for business applications combines dense vector embeddings with sparse keyword retrieval (BM25), because pure vector search alone misses exact product codes, acronyms, and the precise domain jargon your users actually type into a search bar. Hybrid retrieval catches both the conceptually similar result and the exact-match result a single approach would miss.

We tune the balance between semantic and keyword signals to your specific content — internal documentation, product catalogues, support tickets — and add metadata filtering so results respect business rules like permissions, regions, or product lines. The outcome is search that holds up under real usage, not just clean benchmark queries.