Your competitors
are already
shipping AI.
AI strategy, product engineering, MLOps, voice and vision — one engineering partner that integrates production AI into the stack you already run, whatever that is: Postgres, Python, AWS, on-prem, edge. No rewrite, no 7-month hiring cycle, and no system you can't own or audit.
30 minutes with an engineer, not a salesperson. You leave knowing whether AI is the right answer to your problem — including an honest "it isn't" when that's the truth.
Everything you need.
One engineering partner.
We audit your business, identify high-leverage AI opportunities, and build a phased AI consulting roadmap grounded in ROI — not hype. Want to try the scoring yourself first? Use our free tool to prioritize AI use cases by ROI. Not sure where to start? See our AI readiness assessment.
From prototype to production. As an AI development company we design and engineer full AI products — LLM integration, AI agent development, RAG systems, generative AI development, and custom model fine-tuning — built to survive real-world scale. See it in action for e-commerce.
Reliability and scale are non-negotiable. Our MLOps work builds the pipelines, monitoring, CI/CD, and infra that keeps your AI system performant, observable, and cost-efficient in production.
Conversational voice AI agents that handle real customer calls — understanding intent, managing dialogue flow, escalating to humans when needed. Built for production telephony, not demo environments.
Custom computer vision models trained on your specific domain — deployed on edge hardware, embedded systems, or cloud inference. From prototype to grading line without the latency of a cloud round-trip.
What we've seen break.
Not case studies. Specific failure modes we've diagnosed and fixed — the kind that don't show up until production.
Root cause: no retrieval eval layer. The vector search was returning plausible but semantically off chunks. The LLM hallucinated confidently on top of bad context.
Root cause: no output schema enforcement on tool calls. The agent was given a list of actions it could take. With ambiguous prompts, it chose the wrong one — at scale, automatically.
Root cause: nobody modelled token usage at volume. A feature that cost $0.002 per call looked fine in staging. At 500k calls/day it was a $30k/month surprise.
Root cause: direct coupling to one provider's API throughout the codebase. When they deprecated a parameter, every call broke simultaneously. The fix took three weeks.
Root cause: no out-of-scope detection. The agent was given a knowledge base and told to answer questions. It extrapolated freely — giving confident wrong answers on topics outside its domain. Customers escalated angry, not confused.
Root cause: training data was clean studio images. Production camera had variable lighting, motion blur, and lens distortion. Model accuracy dropped from 94% in testing to 61% in the field. Nobody had tested on real hardware.
There are more where these came from. We write up the failure modes we hit — root cause, the fix, and what it cost to find out — as they happen.
Field notes & case studies →Structured for speed.
Built for depth.
We map your existing infrastructure — databases, APIs, data pipelines, auth systems — and identify exactly where AI slots in without breaking what works. You give us access. We give you a gap analysis.
A precise architecture doc: system diagrams, model selection rationale, data flow maps, cost projections, and a written Trust Spec covering privacy, guardrails, and compliance — before a single line of code.
A working proof of concept against your real data. Your engineers review the code, ask questions, push back. We iterate. No black boxes — your team can read everything we write.
Your first AI feature goes live — the one from the prototype, now production-hardened with logging and guardrails, and benchmarked against a defined quality threshold before it ships.
We broaden eval coverage, add fallback logic and CI/CD, and extend the system to the remaining scope — then hand over everything: source code, infra configs, runbooks, IP transferred in writing, and a live knowledge transfer session. You can operate this without us from day one of handoff.
What we've actually delivered.
Five engagements, four systems in production and one shipped prototype. Every number below is documented — on a call we'll walk you through the data behind it, the architecture that produced it, and the evals that keep it honest.
We don't publish client names without written permission — that's the agreement every client here has, and it's the one you'd get too. Company names, team contacts, and the full metric breakdown are shared under NDA on a discovery call.
Not a consultancy.
An engineering partner.
AI that your legal team
won't flag.
We know the real blocker isn't budget — it's fear. Fear of a hallucination embarrassing your company, a data leak, or a system you can't audit or control. We treat these as engineering problems with engineering solutions.
Your data never trains a third-party model. We architect data flows with strict isolation — what goes in stays in your environment.
- No data leakage to public model providers by default
- On-premise or VPC deployment options for sensitive workloads
- PII redaction pipelines before any LLM interaction
- Audit logs on every inference call, queryable at any time
Generative AI is probabilistic — but unreliable outputs are an engineering failure, not an inevitability. We build eval frameworks that catch bad outputs before users do.
- Automated eval suites run on every deployment
- Confidence scoring & fallback logic for uncertain outputs
- Human-in-the-loop checkpoints for high-stakes decisions
- Output schema enforcement — structured, validated responses
GDPR, SOC 2, HIPAA, the EU AI Act — regulatory surface area is growing fast. We design systems with compliance baked in, not bolted on after the fact.
- Data residency controls — processing stays in your required region
- Model cards & documentation for regulatory audit trails
- Bias & fairness testing as part of every eval pipeline
- Right-to-explanation architecture for regulated decisions
HIPAA-ready and SOC 2-ready approaches, and our vendor security overview.
We don't build systems that only work if you keep paying us — or if OpenAI changes their API. Portability and ownership are design constraints, not afterthoughts.
- Model-agnostic architecture — swap providers without rebuilding
- Repo access from day one — code lands in your repository as it's written, not at handoff
- Full source code & IP ownership transferred at handoff
- Comprehensive runbooks so your team can operate independently
- No proprietary black-box wrappers — open, auditable stack
Why not just hire in-house?
It's the right question. Here's the honest answer.
In-house is the right answer eventually — once you know exactly what you're building and need someone to own it long-term. We get you there faster, at lower risk, and hand over everything when you're ready.
Three ways
to start.
One question answered: is AI the right answer to this problem at all? A straight yes, no, or "not yet" from an engineer. No roadmap and no costings — those take real work.
Book a free call →The next question answered: what to build, in what order, at what cost. One fixed week — stack audit, ROI workshop, and a costed go/no-go roadmap that's yours whichever way it lands. £3,000, credited in full if you build with us.
First production AI feature in about 6 weeks, inside your existing stack. You own the code, scoped from the Sprint roadmap or from your own spec. Priced by milestone, never by the hour — you see the whole number before anything starts.
How we work ↓
Tell us what
you're trying to build.
Book 30 minutes. By the end of the call you'll know whether AI fits your problem, and whether it's worth putting a costed roadmap behind it. No pitch deck. No retainer proposal. Just a straight answer.
Or email us directly: [email protected]