AI development services UK: LLM, RAG and AI agents
AI development that ships, not demos.
Everyone has an AI proof-of-concept. Almost no one has it in production. We build the agents, copilots and RAG systems that actually make it to real users — fast, measured and safe — so your AI bet turns into a feature people use, not a slide in a deck.
AI development problems we fix.
Your AI prototype wowed the room — then never reached customers.
We build for production from day one: evals, guardrails and a clear path to launch, so the impressive demo becomes a shipped feature.
You're not sure which model, framework or pattern to bet on.
We're model-agnostic. We pick OpenAI, Anthropic or open models per the job, and choose RAG, fine-tuning or agents based on your data — not hype.
You're worried about hallucinations, cost and things going wrong in front of users.
We wire in evaluation, fallbacks, rate limits and observability so quality is measured, costs are controlled and failures are graceful.
Our AI development services.
AI agents & automations
Agents that take real actions across your tools and data — researching, drafting, triaging and executing multi-step workflows reliably.
Copilots & assistants
In-product copilots and chat that understand your domain and your user's context — grounded in your data, not the open web.
RAG & knowledge systems
Retrieval-augmented generation over your documents and databases, with the chunking, embeddings and reranking that make answers actually accurate.
Evals, guardrails & observability
Automated evaluation, prompt and output guardrails, and full tracing — so you can prove quality and watch cost in real time.
What you get from our AI development.
- A working AI feature in production, used by real customers
- A measurable quality bar, not vibes
- Cost and latency you can see and control
- A codebase your team can own and extend
Our AI development stack.
AI development questions, answered.
How much does it cost to build an AI feature?
Most AI builds at Softgen start from £18,000 and range up to around £120,000 for larger, multi-agent systems. We give you a fixed price and timeline after a short discovery — the scope drives the number, not guesswork.
Which AI models do you use?
We're model-agnostic. We use frontier models from OpenAI and Anthropic, plus open models where they fit, and choose per the task on quality, cost and data-privacy grounds.
Can you add AI to our existing product?
Yes — most of our AI work is adding agents, copilots or RAG into a product that already exists. We integrate with your stack and ship behind a feature flag.
Do you offer RAG development services?
Yes. We build retrieval-augmented generation (RAG) systems that ground an LLM in your own documents and data: ingestion, chunking, embeddings, vector search, re-ranking, citations and evals, shipped to production with access control.
What's the difference between RAG and AI agents?
RAG gives the LLM the right facts from your data before it answers. An AI agent lets the LLM plan steps and call tools to finish a task. Most production agents use RAG for their knowledge, and we build both, together or apart.
How do you stop the AI from hallucinating?
We ground responses in your data with RAG, add output guardrails and evaluation suites, and design graceful fallbacks. You get a measured quality bar, not a black box.
Know before you build.
What is RAG? Retrieval-augmented generation in plain English
RAG lets an AI answer from your own documents instead of guessing. Here's how it works, when it beats fine-tuning, and what separates a demo from a production system.
Read the guide AI · 8 min readHow to build an AI agent for your product
Most AI agents die in the demo. Here's how to build one that actually ships — the architecture, the evals, and the guardrails that keep it useful and safe.
Read the guide AI · 9 min readAI evals explained — how to stop shipping demos
You cannot ship AI on 'it feels good in testing'. Evals are the automated test suite for LLM behaviour. Here's exactly what to measure and how we build them so you can iterate safely.
Read the guide AI · 7 min readModel Context Protocol (MCP), explained
MCP is the standard that lets AI assistants plug into your tools and data the way USB-C plugs into devices. Here's what it is, why it matters, and how to use it.
Read the guide AI · 11 min readBuilding multi-agent systems that ship in 2026
Single agents are table stakes. Multi-agent systems unlock real workflows — but most fall apart on handoffs and observability. Here's exactly how to build them for production.
Read the guide AI · 7 min readWhat is an AI copilot — and should you build one into your product?
Every product is bolting on a copilot. Here's what an AI copilot actually is, how it differs from a chatbot and an agent, and whether yours needs one.
Read the guideWhat we've built, and the calls to make first.
A support copilot that handled the questions humans shouldn't have to.
An in-product AI copilot, grounded in the company's own docs and ticket history, that resolved routine questions instantly and freed the team for the hard ones.
Read more Case study · Consumer AIAn AI dating app that matches people on personality and values.
Lamp Dating is an AI dating app for people who want a serious relationship. Softgen's founder is its co-founder and CTO: the matching engine, the API and the AI features are his team's work.
Read more ComparisonIn-house AI team vs AI development agency
Hire and build an AI team, or work with an AI agency? Here's a fair comparison across speed, expertise, cost and risk — and when each is the right call.
Read more ComparisonRAG vs fine-tuning
Two ways to make an LLM useful for your product: feed it your data at query time (RAG), or retrain it on your data (fine-tuning). Here's which to reach for, and when.
Read more ComparisonBuild vs buy software
Buy an off-the-shelf product or build your own? Here's a fair comparison on cost, speed, fit and ownership — and the test that decides it.
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