A RAG development company UK businesses can trust to build production retrieval systems not a demo that falls over on real data. Retrieval-augmented generation over your own documents, with citations, security, and answers you can actually defend.
Claude and GPT are trained on the public internet up to a cut-off date. They have never read your contracts, your policies, your product data, or last week's decision. Ask them something specific and they either refuse or, worse, invent a confident answer you cannot trace to anything.
Chunk a folder, embed it, wire it to a chat box, and it looks great in a five-minute demo. Then it retrieves the wrong paragraph, misses the one document that mattered, and cites nothing. Retrieval quality, chunking strategy, and evaluation are the whole job, and they are exactly what a weekend prototype skips.
The moment you point AI at company knowledge, you are handling sensitive material: customer records, contracts, HR files. Where it is stored, which model sees it, and whether anyone trains on it become questions an auditor will ask. Most quick builds have no answer.
We build retrieval-augmented generation over your actual content, with citations, evaluation, and data you control end to end.
See what you get →Every build ships with the same core package. It is the baseline any RAG development company UK businesses pay for should meet, whether it answers customers or powers an internal knowledge assistant.
Your documents, databases, and knowledge sources parsed, chunked, embedded, and indexed into a vector database, with a repeatable pipeline for keeping them fresh.
Every response points back to the source passage it came from, so your team and your users can verify it instead of trusting a black box.
Precision and recall measured on a real question set from your business, so you know how often the right context is actually retrieved before it goes live.
Vector search combined with keyword and metadata filters, tuned so the system finds the right passage across multiple sources, not just the closest embedding.
Documented data flows, retention rules, and model providers under no-training agreements. Where the use case demands it, we run open models so nothing leaves your environment.
Retrieval tuning, re-indexing, and drift monitoring after launch, from the team that built it.
If any of these describe you, we will tell you on the first call rather than waste your time or ours.
Founder at Seconddate
Foundry 5 really impressed me with their work on my AI-powered dating app. They nailed the UI/UX, starting with core app screens and then translating that vision to the web. The React site they built is sleek and polished - exactly what I was hoping for. What I appreciated most was their thoughtful approach and how easy they were to work with. They got the job done without needing me to micromanage.
CEO at StreaksAI
Foundry 5 surpassed all expectations in delivering our web development product swiftly and flawlessly. Their speed and eye for detail, especially under tight time constraints, showcase their commitment to excellence.
CEO, Xcelsior Capital
Working with Foundry 5 has been a great experience from start to finish. They seamlessly translated our vision into a website that not only showcases our brand and operations but also enhances our online presence as an investor in natural resources. Nothing was too much for them and they always put the client first.
CEO of Ove
Working with Foundry 5 to create the Ove app has been an absolute pleasure. Their professionalism, work ethic, and willingness to always go the extra mile has really impressed me. I would recommend them to anyone.
Loom Founder
Foundry 5 have gone above and beyond to bring my vision of Loom to life. Not only this, but they continue to be an integral part of our team. They are lovely people to work with, and I recommend them to anyone looking for a software partner. Particularly for non-technical tech founders (like me!) who need to ensure that their business is in safe hands.
Chief Product Officer Gather
The team has been instrumental in driving both the design and development of Gather, pairing a proactive, highly responsive workflow with the technical depth needed to handle our platform's complexity. Their partnership continues to move the product forward in a reliable and impactful way.
| Feature | Foundry 5 | Raw LLM | SaaS bot |
|---|---|---|---|
| Answers from your documents and data | |||
| Citations back to the source passage | |||
| Retrieval accuracy measured before go-live | |||
| Hybrid semantic and keyword search | |||
| Private data, no training on your content | |||
| Connects to your existing systems | |||
| You own the code and can extend it | |||
| Honest advice on build vs. buy |
Pricing a RAG development company UK founders can plan around: we scope the work, agree a price, and stick to it. No surprise invoices and no scope creep charges. You get a written scope document before code starts, and you own the pipeline outright once it is live.
For systems that grow: new knowledge sources, new document types, new use cases. You reprioritise at the start of each sprint as you learn what your users actually ask.
Anything else? Just book a call. We'd rather talk than write another FAQ entry.
✓Risk-free first project · Full refund if you’re not satisfied
RAG is a technique that gives a language model access to your own information at the moment it answers. Instead of relying only on what the model learned during training, the system first retrieves the most relevant passages from your documents or database, then asks the model to answer using that retrieved context. The result is an answer grounded in your data, with the sources it came from, rather than the model's general recollection.
A standard chatbot answers from a help centre you have to keep writing, and a raw LLM answers from public training data. Neither knows your contracts, your product database, or last week's policy change. RAG connects the model to your actual content, so it can answer specific questions accurately and cite where each answer came from. When a wrong answer carries real cost, that grounding and traceability is the difference.
Yes, that is the entire point. We ingest the documents you already have — PDFs, Word files, spreadsheets, wiki pages, database records — parse and chunk them, and index them into a vector database. The assistant then answers from that content. We do a knowledge audit first to see how clean the source material is and what is worth indexing, because retrieval quality depends on the data going in.
Common formats including PDF, Word, PowerPoint, Excel and CSV, HTML and Markdown, plain text, and structured data from databases and APIs. Scanned documents can be handled with OCR. Messy real-world files — inconsistent formatting, tables, scanned contracts — are where careful parsing matters, and that work is part of the build rather than an afterthought.
As secure as it is engineered to be, which is why we treat it as a first-class concern. Your content is stored in a vector database in infrastructure you control, and we use model providers under agreements that prohibit training on your data. We document what is sent to the model, what is retained, and for how long, so you have a data flow you can show an auditor. Where a use case demands it, we run open models so nothing leaves your environment.
Yes. Most of our RAG work goes into products and internal tools that already exist. We connect to your current APIs, database, and authentication, and expose the assistant wherever your users already are — your web app, an internal dashboard, or a support channel. It behaves like a feature of your system, not a separate tool bolted on the side.
Four to eight weeks from scope to live for most builds. Week one is scoping and a knowledge audit, weeks two and three produce a working prototype benchmarked on your real questions, and the production build and hardening follow. You see retrieval accuracy numbers before you commit to the full build, so there are no surprises.
Yes. We build connectors to the sources where your knowledge actually lives — SharePoint, Google Drive, Confluence, Notion, databases, and internal APIs — and keep the index in sync as content changes. Access controls carry through, so the assistant only surfaces what a given user is allowed to see.
Book a 30-minute AI discovery call. No pitch. We will tell you whether the RAG development company UK businesses like yours need is worth building here, and how a custom retrieval system would make your AI more accurate, secure, and useful.
✓Risk-free first project · Full refund if you’re not satisfied
