RAG Implementation Company UK & US Startups
We're a RAG implementation company for UK businesses and a RAG implementation services partner for US startups — building retrieval-augmented generation systems that are accurate, grounded, and production-ready.
Custom LLM Integration Company
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.
Agentic RAG for SaaS Companies
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.
Hybrid Search for Business Applications
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.
Pricing & Delivery FAQs
Transparent expectations on investment, timelines, and technical ownership before starting.
help_outlineWhat is the difference between RAG and fine-tuning an LLM?
RAG grounds an LLM in your live product data, docs, and customer context without retraining the model — better when your knowledge base changes often. Fine-tuning adapts the model itself and suits cases where you need consistent tone, format, or domain reasoning baked in. Many production systems use both.
help_outlineHow do you prevent LLM hallucinations in a RAG pipeline?
We build custom evaluation suites that benchmark retrieval accuracy and response consistency before deployment, use contextual compression and cross-encoder re-ranking to surface the right source material, and add feedback loops so retrieval quality keeps improving after launch.
help_outlineCan RAG work across multiple tenants without data leaking between customers?
Yes — this is a core requirement for multi-tenant SaaS platforms, and most generic RAG tutorials don’t address it. We build tenant isolation into the retrieval layer itself, so customer-specific answers stay accurate without cross-tenant data leakage.
help_outlineWhich LLM providers do you support?
We integrate OpenAI, Anthropic Claude, Google Gemini, and open-source models, and design for model portability so your product isn’t locked into a single vendor’s API if pricing or quality shifts.
Ready to ground
your LLM in real data?
Whether you need a first RAG pipeline or a rebuild of one that hallucinates too often, we'll scope it together.