From a single integration to a full platform, we cover the entire arc of building with AI. Pick a lane, or hand us the whole roadmap.
40+
AI systems shipped
8
core service lines
2
flagship products
24h
avg. response time
LLM-powered apps, intelligent copilots, and generative features tailored to your product and users.
Secure API connections and automated workflows that reason, plan, and act — turning repetitive work into reliable systems built with Make, Zapier, or custom Python scripts.
Robust pipelines that collect, clean, and serve your data to models — from prototype to production scale.
We wire the right models, APIs, and tooling into your existing stack with clean, maintainable engineering.
Semantic search and retrieval-augmented generation grounded in your own knowledge base — accurate and explainable.
Rigorous eval harnesses, guardrails, and observability so your AI behaves predictably in production.
Bespoke databases, enterprise web applications, and internal digital tools engineered from scratch around your business — not around a template.
We audit enterprise workflows and safely integrate large language models (LLMs) into your existing infrastructure — architecture, security, and roadmap.
Deep experience across the modern AI stack — the frameworks, models, and infrastructure behind production systems. No buzzwords, just what we actually use on client builds.
We orchestrate LLM workflows with LangChain and build stateful multi-agent systems on LangGraph. When a project only needs a direct model call, we use that instead — the right tool, not the trendy one.
Retrieval-augmented generation grounded in your knowledge base — embeddings, chunking, hybrid keyword + semantic search, reranking, and citations so every answer is traceable and current.
Pinecone, pgvector, Qdrant, or Weaviate — we pick the vector store that fits your scale and latency budget, and tune indexing and retrieval quality around your data.
Agents that plan, use tools, and act across your systems — with human-in-the-loop checkpoints, self-healing failure handling, and observability so they behave predictably in production.
When off-the-shelf models fall short, we fine-tune open weights or train lightweight custom models for your domain — cutting token costs and locking in consistent behaviour.
ETL and streaming pipelines, feature stores, model serving, monitoring, and retraining loops. We run our own products on this discipline — evals, drift tracking, and cost budgets included.
Evaluation harnesses, safety rails, prompt-injection defences, red-teaming, and tracing. Every AI system we ship is measured against a test suite before it reaches production.
We wire OpenAI, Anthropic, and open-weight models behind one interface with graceful fallbacks — and self-host where latency, privacy, or cost makes it the right call.
No one-size-fits-all. Pick the model that matches where your project is.
A defined deliverable at a fixed price. Ideal for MVPs, integrations, and feature builds with a clear spec.
A senior pod on tap each month — ship features, improve AI quality, and iterate on feedback continuously.
We co-build and license you the product — the same model behind our own flagship products.
Workshop, data & user audit, and a concrete roadmap with clear scope and metrics.
A working prototype in weeks so you can feel the product before we scale it.
Production-grade code, evals, monitoring, and a smooth launch alongside you.
Prototypes land within 2–3 weeks. A production MVP is typically 6–12 weeks depending on scope.
A production AI app typically costs $25,000–$60,000 (£20,000–£48,000) over 6–10 weeks. Simple MVPs run $8,000–$20,000; multi-agent or enterprise systems start at $60,000. Budget 15–25% of build cost annually for inference and maintenance.
Yes. We integrate with what you have rather than forcing a rewrite. Our integration and agentic services are built for this.
Plan for $500–$3,000 per month on a typical production app — model API calls, hosting, a vector database, and observability. We architect to keep inference cheap, including self-hosting where it pays off.
Yes — always. Full IP transfer, clean documentation, and no lock-in. Your code, your data, and your models are yours from day one.
RAG grounds answers in your documents at query time — fast to build, cheap to run. Fine-tuning changes model behaviour and tone. Agents plan and execute multi-step actions across your tools. We recommend the simplest option that solves the problem, and layer them when needed.
LangChain and LangGraph for orchestration, vector stores like pgvector and Pinecone for retrieval, evals and guardrails on every build, and open-weight or API models depending on latency, privacy, and cost. We use whatever fits the problem — and self-host where it pays off.
Security reviews, guardrails, and observability are part of every build. For US clients we follow SOC 2, HIPAA, CCPA and PCI-DSS patterns; for UK and EU clients we follow UK GDPR, the ICO’s AI guidance, DPIA, and the EU AI Act where it applies.
We are AI-native. We build AI products ourselves (two shipped, including a world-record 193ms calling agent), measure them against hard latency and cost targets, and apply the same discipline to client work.
We’ll map the shortest path from your idea to a working product — prototype in hand within weeks.
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