Skip to main content

AI that works in production

From custom model deployment to production RAG pipelines, we build AI systems that create real competitive advantage — not demos that impress in a boardroom and break in production.

What We Build

  • Custom AI model training and deployment
  • RAG (Retrieval Augmented Generation) pipelines for enterprise knowledge
  • LLM integration and prompt engineering for business applications
  • AI-powered analytics and business intelligence dashboards
  • Real-time data pipelines and ETL/ELT architecture
  • Vector databases and semantic search infrastructure
  • Computer vision and document processing systems
  • AI agent systems and autonomous workflows

How We Work

  • Assessment First: We evaluate where AI creates genuine value vs where it's theatre. Not every problem needs a model.
  • Production-Grade: Our AI systems ship with monitoring, fallback handling, cost controls, and human-in-the-loop where needed.
  • Data Foundation: AI is only as good as the data. We build the pipelines, governance, and quality controls before the models.
  • Responsible AI: EU AI Act compliance, GDPR-aware data handling, bias testing, and transparent decision-making built in from the start.

Capabilities in detail

"AI" is a broad label. Here's what we actually build for UK clients:

  • RAG pipelines: retrieval over your own documents, policies, product data, or support history. Vector databases (pgvector, Pinecone, Weaviate), chunking strategies that actually reflect your content, and evaluation harnesses so you know when the pipeline is degrading.
  • LLM integration: wiring frontier models into existing products. OpenAI (GPT-4, GPT-5), Anthropic (Claude), Google Gemini, and open-source (Llama, Mistral) where cost, latency, or data residency requires it. We pick the model for the problem, not the brand.
  • Agent workflows: multi-step AI processes that call tools, query data, and hand off to humans. Built with proper guardrails, cost caps, and fallback paths — not demoware.
  • AI-powered CRM and data extraction: turning unstructured inputs (emails, PDFs, transcripts, forms) into structured CRM records, enriched leads, or operational events.
  • Document AI: OCR, classification, entity extraction, and layout-aware parsing for contracts, invoices, compliance paperwork, and dealer paperwork.
  • AI-assisted QA: test generation, coverage analysis, and bug triage baked into your engineering workflow — useful for teams with legacy test debt.
  • Custom fine-tuning: where off-the-shelf won't cut it on accuracy, tone, or latency. We've fine-tuned on open-source families and run the evaluations to prove it moves the needle.

Timelines and engagement

We scope in weeks, not quarters. Indicative production timelines based on recent engagements:

  • RAG pilots typically ship in 4–6 weeks — retrieval index, evaluation harness, and a working UI.
  • LLM integrations into an existing product typically take 6–10 weeks including prompt engineering, observability, and cost controls.
  • Agent workflows with tool use and human-in-the-loop usually take 8–16 weeks depending on the number of integrations and the regulatory surface.
  • Custom model fine-tuning runs 6–12 weeks including dataset curation, training, and evaluation against a production baseline.

How we're different

We build AI into production systems — not LLM wrappers. We don't do research, we do deployment. That changes what the work looks like:

  • Senior engineers only. No offshore, no junior tier, no "learning in public" on your budget.
  • We own infrastructure, observability, and cost controls from day one. If a prompt starts costing you £2,000 a day, you'll know before the invoice arrives.
  • We write evaluations before we write prompts. Accuracy targets are agreed and measurable, not vibes.
  • We hand off clean. Your team should be able to maintain what we built without our phone number on speed dial.

Industries We Serve

Our AI and data engineering expertise spans multiple verticals, with deep experience in:

  • E-commerce AI — recommendation engines, semantic search, AI-assisted content generation, dynamic pricing, and demand forecasting.
  • Fintech AI — compliance automation, KYC document processing, transaction classification, and risk summarisation for regulated firms.
  • Automotive AI — dealer tooling, inventory optimisation, customer ops agents, and predictive analytics across dealer networks.
  • Venture capital — AI readiness assessments, due diligence automation, portfolio analytics.

From Our Insights

Frequently Asked Questions

Do you build custom AI models or use off-the-shelf? +
Both, depending on the problem. Most business applications are best served by fine-tuned foundation models or RAG architectures. We build custom models only when off-the-shelf can't deliver the accuracy or latency your use case requires.
How do you handle data privacy and compliance? +
Every engagement starts with a data governance review. We build to GDPR standards by default, implement data processing agreements, and ensure AI systems meet transparency requirements under the EU AI Act where applicable.
What's the difference between AI consulting and what you do? +
Consultants deliver recommendations. We deliver production systems. Our AI engineers build, deploy, and monitor models in your infrastructure — not in a slide deck.

Got an AI use case?

Tell us the problem and we'll tell you if AI is the right solution — and how we'd build it.

Get In Touch