AI development that survives contact with real users
We build AI features and products that hold up in production — RAG pipelines, evals, guardrails and cost engineering — from a senior team that shipped enterprise AI systems on AWS Bedrock. Fixed pricing, agreed upfront.
How we approach it
The gap between an AI demo and an AI product is enormous, and it’s where most projects die. A prototype that wows the boardroom takes a weekend; a system that answers correctly at 2am, on the thousandth edge case, at a cost per query the business can afford, is an engineering problem. That production gap is exactly what we specialise in. Our team built enterprise AI systems at AWS, on AWS Bedrock, for clients who couldn’t accept “it usually works” — and we bring that same standard to every build.
Most of the AI features businesses actually need are grounded in their own data, which is why retrieval-augmented generation (RAG) sits at the centre of much of our work: chunking and embedding your documents, building the retrieval layer, and engineering prompts so the model answers from your content rather than inventing its own. Where RAG isn’t the right tool, we’ll say so. Fine-tuning, careful prompting or a smaller task-specific model each win in different situations, and we choose based on your accuracy, cost and latency requirements — not on what’s fashionable.
You can’t improve what you can’t measure, so every build gets an evaluation suite: a test set of real inputs with known good answers, run automatically against every change. That’s how we prove a new prompt or model actually improved things instead of quietly breaking something else. Around the model we build guardrails — input validation, output checking, topic boundaries and safe fallbacks — so the system degrades gracefully instead of embarrassing you in front of a customer.
We work across AWS Bedrock, OpenAI and Anthropic APIs, and we’ll recommend the provider that fits your data-residency, cost and capability requirements rather than the one we have a partnership badge for. Cost and latency are engineered from the start — model selection, caching, batching and prompt design all affect what each query costs and how fast it feels. Pricing is fixed and agreed before we begin, starting with a free 30-minute scoping call.
AI Development, done properly
RAG pipelines done properly
Document ingestion, chunking, embeddings and retrieval engineered around your content, so answers are grounded in your data — with sources — not hallucinated.
Evals before opinions
Every build gets an automated evaluation suite of real inputs and expected outputs, so changes are proven to improve accuracy rather than assumed to.
Guardrails and safe failure
Input validation, output checking, topic boundaries and graceful fallbacks — so when the model is unsure, the system does something sensible, not something embarrassing.
Cost and latency engineering
Model selection, prompt design, caching and batching tuned so each query costs pennies not pounds, and responses feel instant rather than laboured.
Provider-agnostic builds
AWS Bedrock, OpenAI or Anthropic — chosen for your data-residency, capability and budget requirements, with an abstraction layer so you’re never locked in.
Enterprise pedigree
Our engineers shipped production AI systems on AWS Bedrock for enterprise clients — the same discipline, testing and monitoring, whatever your size.
From first call to launch
- 01
Scope and de-risk
A free call to define what the AI feature must do, what accuracy is acceptable, and where the risks sit. You get a fixed quote and an honest read on feasibility.
- 02
Prototype against real data
A working prototype built on your actual documents and inputs — not toy examples — with a first eval suite, so we know early whether the approach holds.
- 03
Engineer for production
Guardrails, error handling, monitoring, cost controls and integration with your systems — the unglamorous work that separates products from demos.
- 04
Ship and iterate on evidence
We launch with logging and evals in place, then improve accuracy and cost using real usage data — every change measured before it goes live.
AI Development questions, answered
What’s the difference between an AI demo and a production system?
A demo handles the happy path in front of a friendly audience. A production system handles ambiguous inputs, adversarial users, provider outages and the ten-thousandth query at an acceptable cost — and does it unattended. Practically, the difference is evals, guardrails, monitoring, retry logic, cost controls and integration work. That is typically 80% of the engineering effort, it’s the part most AI projects skip, and it’s the reason so many pilots never make it past the pilot.
Should we fine-tune a model or just use prompting?
Start with prompting — it’s faster, cheaper and easier to change, and with today’s models it covers most business use cases, especially combined with RAG for grounding answers in your data. Fine-tuning earns its cost when you need consistent behaviour on a narrow, well-defined task at high volume, or a specific tone that prompting can’t reliably hold. We’ll test the prompting approach against your eval set first, and only recommend fine-tuning if the numbers say it’s needed.
How do you stop the AI making things up?
You can’t eliminate hallucination entirely, but you can engineer it down to acceptable levels. We ground answers in your own content with RAG so the model quotes rather than invents, constrain outputs with validation and topic boundaries, require the system to say “I don’t know” instead of guessing, and cite sources so answers can be checked. Then we measure it — our eval suites include hallucination checks, so accuracy is a tracked number, not a hope.
Which AI provider do you build on — OpenAI, Anthropic or AWS?
All three, chosen per project. AWS Bedrock suits organisations with strict data-residency or compliance needs, and it’s where our team’s enterprise experience runs deepest. OpenAI and Anthropic APIs offer strong models with fast-moving capabilities. We benchmark candidate models against your actual task and budget during scoping, and we build behind an abstraction layer so switching providers later is a configuration change, not a rewrite.
How much does AI development cost to build and run?
Two numbers matter: the build and the running cost. Builds are fixed-price and quoted after a free scoping call — a focused AI feature is a different project from a full AI product, and we’ll scope honestly. Running costs depend on query volume and model choice, so we estimate cost-per-query during prototyping and engineer it down with caching, prompt design and right-sized models. You’ll know both numbers before committing to production.
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