AI development agency in London
We design and ship production-grade LLM applications — autonomous agents, retrieval pipelines and AI products that hold up under real users, built with Claude, GPT and the best open-source models.
The short answer
We build production LLM applications — agents, retrieval systems and AI product surfaces — on Claude, GPT and open-source models. A focused MVP typically ships in 4–8 weeks; larger programmes run 3–6 months with regular delivery milestones. Everything is delivered into your repositories and owned by you. Tell us your budget and we will tell you what fits — see how we price.
On this page
Plenty of teams can bolt a chatbot onto a product. An AI development agency earns its keep somewhere else: turning a model's raw capability into software that is reliable, measurable and safe to put in front of customers. That means engineering around the failure modes — hallucination, drift, cost blowouts, prompt regressions — rather than pretending they don't exist. It's the difference between a demo that impresses in a meeting and a system your business can depend on.
BrashPixels is a small, senior London studio. We work in short, high-agency cycles: a working prototype early, validation with real users, then production hardening. No layers of account management, no hand-offs — the people you talk to are the people who build.
What "production-grade" actually means
The phrase gets used loosely enough to be meaningless, so here is the specific bar we build to. A system is production-grade when five things are true of it, and a prototype typically has none of them.
Quality is measured, not asserted. There is an evaluation suite — a fixed set of realistic cases, graded automatically — that runs on every change. Without it, "we improved the prompt" is a claim nobody can check, and quality decay is invisible until a customer finds it.
Cost is bounded. Per-request and per-task spend is forecast before launch and instrumented after it, with ceilings that stop a runaway loop becoming a runaway invoice. More AI projects are killed by an unexpected bill than by poor output quality.
Failure is designed for. Model APIs have outages, rate limits and latency spikes. Production systems degrade gracefully — fall back, queue, retry with backoff, tell the user something honest — rather than returning a stack trace.
Behaviour is reconstructable. When something surprising happens in month four, you can find out exactly what the model saw and what it did. That means logging inputs, outputs and tool calls, not just errors.
Someone else can maintain it. Documentation, tests and a handover session, so the system does not silently depend on the people who built it. This is the one most often skipped and the one clients thank us for later.
What we build
Agent frameworks
Custom agents with tool use, memory and guardrails — built on Claude with LangChain/LangGraph where it helps, or lean first-party orchestration where it doesn't.
RAG & search
Retrieval-augmented generation pipelines with careful chunking, embedding reranking and citation, so answers are grounded in your data.
Evals & observability
Systematic evaluations, prompt regression suites and real-time monitoring — the unglamorous work that makes AI dependable.
Model selection
Unbiased comparisons across capability, latency, cost and governance. Claude, GPT, Gemini or open-source — the use case decides, not a partnership deal.
Two of these are deep enough to have their own pages: AI agent development covers when an agent genuinely beats a fixed workflow and how agents are kept safe in production, and RAG development covers retrieval over your own documents, including permission-aware search and why long context windows have not made the pattern obsolete.
How we work
Every engagement starts with a short discovery phase — often the tail end of an AI consultancy sprint — where we pin down the user, the workflow and the measurable outcome the AI has to move. Then we prototype fast: a working slice of the product in the first couple of weeks, tested against real data.
From there it's production engineering: evaluation suites so quality is measured rather than guessed at, monitoring so regressions surface before your users find them, and infrastructure that keeps inference costs predictable. A focused MVP ships in 4–8 weeks; larger programmes run three to six months with regular delivery milestones.
One rule shapes the whole process: we define what "working" means as a number before we build anything. Not "the assistant should be helpful" but "it should resolve 70% of tier-one tickets without escalation, at under 8p per ticket, in under four seconds." If that number cannot be agreed, the project is not ready to start — and saying so early is considerably cheaper than discovering it in month three.
Models and stack
We are vendor-neutral and design so the model is swappable. The field moves fast enough that hard-coding a provider is a liability rather than a commitment, and the architectures that survive are the ones where changing model is a config change and a fresh eval run.
In practice, most production systems we ship run on Anthropic Claude or OpenAI GPT, with open-source models where cost, latency or data residency argue for it. The choice is driven by four things and never by a partnership: capability on your specific task as measured by your evals, latency budget, cost per request at your volume, and governance constraints such as data residency and retention.
Around the model, the parts that matter most are unglamorous: structured outputs so downstream code is not parsing prose, prompt caching where the workload allows it, retries and fallbacks across providers, and a tracing layer that captures every request. We generally prefer lean first-party orchestration over heavy frameworks — frameworks are excellent for getting to a prototype and frequently an obstacle to debugging one in production.
Why AI projects fail
After enough engagements the pattern is consistent, and it is almost never model capability.
- No definition of success. The project cannot end because nobody agreed what finished looks like. The most common failure by a wide margin.
- Data that was not ready. The demo used ten clean examples; production meets ten thousand messy ones. Data preparation routinely adds 40–60% to a budget that did not account for it.
- Cost discovered late. A per-request cost that was trivial in testing becomes material at real volume, and the project is cancelled by finance rather than by product.
- No evaluation harness. Quality drifts as prompts, models and data change, and nobody notices until users complain. By then trust is gone and it is very hard to win back.
- Building the impressive thing instead of the valuable one. An autonomous agent when a deterministic workflow automation would have shipped in a fortnight and worked better.
Cost and timelines
A focused MVP ships in 4–8 weeks; a production programme runs 3–6 months with regular delivery milestones. Rather than publish a rate card that would be wrong for most projects, we price each engagement against its actual scope — tell us your budget and we will tell you honestly what fits inside it. If you are not sure which you need, a discovery sprint will tell you, and the roadmap is yours whether or not we build it.
Model API usage, hosting and vector database costs are paid by you directly and never marked up by us — a supplier who profits from your token spend has the wrong incentive about how many tokens your system uses. Full detail, including UK market rates by provider type and the five factors that move a quote, is on our pricing page.
Recent work
Fintech compliance agent
Autonomous regulatory monitoring agent that reduced manual review time by 70%.
Legal document assistant
Claude-powered contract analysis with clause extraction and risk flagging.
E-commerce chat + search
Hybrid conversational search combining GPT-4o with vector search for product discovery.
Internal knowledge copilot
RAG-based assistant ingesting Notion, Slack and docs for instant team Q&A — the pattern behind much of our AI automation work.
London studio, working anywhere
We're based in central London and run workshops, sprints and working sessions in person when it helps — many clients find the early discovery phase lands much faster face to face. Delivery is remote-friendly by default, and we work with teams across the UK, Europe and the US.
Frequently asked questions
How much does AI development cost in the UK?
A focused MVP typically starts in the low tens of thousands of pounds and ships in 4–8 weeks. Larger programmes run 3–6 months with regular delivery milestones. The biggest cost drivers are integration complexity, data readiness and how much evaluation rigour the use case demands.
How long does an AI development project take?
A production-ready MVP usually ships in 4–8 weeks. We work in short cycles: a working prototype early, validation with real users, then production hardening with evals and monitoring.
Can you take an existing AI prototype to production?
Yes. We regularly take research code or proof-of-concept apps and productionise them with proper evaluations, prompt regression suites, monitoring and infrastructure.
Who owns the code and IP?
You do. Everything we build — code, prompts, eval suites and documentation — is delivered into your repositories and owned by you.
What does production-grade AI actually mean?
Five things are true of a production-grade AI system and none of them are true of a prototype: quality is measured by an automated evaluation suite rather than asserted; per-request cost is forecast and capped; the system degrades gracefully when the model API is slow or down; every input, output and tool call is logged so behaviour can be reconstructed later; and documentation plus tests mean someone other than the original builders can maintain it.
Do you build on Claude or GPT?
Both, and we design so the model is swappable. The choice is driven by measured capability on your specific task, latency budget, cost per request at your volume, and governance constraints such as data residency — never by a partnership. Architectures that survive are the ones where changing model is a config change and a fresh eval run.
Why do most AI projects fail?
Almost never model capability. The common causes are: no agreed definition of success, so the project cannot end; data that was not as ready as the demo suggested, adding 40–60% to the budget; per-request cost that was trivial in testing and material at real volume; no evaluation harness, so quality drifts unnoticed until users complain; and building an autonomous agent where a deterministic workflow automation would have shipped faster and worked better.
Start a project
Tell us what you're building and we'll set up a no-obligation call. Or email hello@brashpixels.com.