AI that reaches production, with the controls to keep it there.
Most AI projects stall between the demo and the daily workflow. We close that gap. The practice runs from a discovery workshop that ranks opportunities by value, through proof of concept and production build, to the data engineering, adoption and governance that make the system last inside a regulated, multi-market organisation. Every engagement can end in a decision not to build, and we count that as a good outcome.
The problems that bring people to us.
- 01
Pilots that never ship.
The demo impressed the board. Six months later nobody uses it, because it was never wired into the tools people work in.
- 02
No data to build on.
The idea is sound but the data lives in five systems across several subsidiaries, none of them clean. The model is the easy part.
- 03
Risk nobody owns.
Staff are already using AI tools with company data, and there is no policy, no controls and no one accountable to the audit committee.
AI Consulting & Automation services.
01AI Strategy & Discovery Workshop
Map where AI creates measurable value in your data and processes, and leave with a ranked roadmap.
02Proof of Concept & Pilot
A short build with real data that proves or kills an idea before serious budget is committed.
03Workflow Automation & AI Agents
Agents and automations wired into CRM, ERP and support tools with human approval where it matters.
04Custom LLM & RAG Solutions
Assistants, copilots and knowledge tools grounded in your own documents and data.
05Data Engineering & MLOps
Pipelines, feature stores, model deployment and monitoring so AI keeps working after launch.
06AI Adoption & Team Enablement
Training, operating model and change management so people use what has been built.
07AI Governance, Risk & Compliance
Model risk controls, EU AI Act readiness and responsible-AI policies that regulators and customers accept.
08AI Visibility / Model Presence (GEO)
Audit and improve how assistants and answer engines describe and cite your brand.
Our method, tuned for this work.
The same spine runs through every engagement, whatever the discipline. It keeps decisions visible and lets you see where you are at any point.
- 01
Map
Find where AI creates measurable value in your processes and data, and rank the opportunities.
- 02
Prove
A short proof of concept that tests the riskiest assumption with real data.
- 03
Build
Production systems with integrations, guardrails and evaluation built in.
- 04
Activate
Training, operating model and change management so people use what was built.
- 05
Govern
Monitoring, evaluation and policy that keep the system safe and improving.
Engineers who ship.
The people who run the workshop also build the system. No hand-off from strategy to a delivery team that wasn't in the room.
Security and governance built in.
Our cybersecurity practice tests every AI system we build, and governance work runs alongside engineering rather than after it.
Willing to say no.
If the data isn't ready or the case doesn't hold, we say so early and suggest what to fix first.
- Starting with a tool rather than a problem.
- Building a chatbot because everyone else has one.
- Skipping evaluation, so nobody knows whether the answers are right.
- Giving an agent tool access without a human approval step.
- Ignoring the staff who are already using AI without guidance.
- OpenAI
- Anthropic Claude
- Azure OpenAI
- Google Vertex AI
- LangGraph
- LlamaIndex
- Pinecone
- pgvector
- n8n
- Make
- dbt
- Databricks
- MLflow
- Langfuse
Three shapes of mandate. One standard.
Fixed-scope programme
A defined outcome, a defined team and a date. Best for audits, platform builds and migrations.
Retained partnership
A standing senior team with a rolling backlog. Best for marketing, security operations and product estates.
Embedded leadership
Our specialists inside your organisation, on your tools and governance, for as long as the mandate runs.
Questions, answered.
With the discovery workshop. It takes a few weeks, ranks opportunities by value and feasibility and ends with a plan you can act on whether or not you work with us afterwards.
Both. We use foundation models from the major providers and ground them in your data through retrieval, fine-tuning or structured integration depending on the case.
Data stays in your environment or in enterprise agreements that exclude training on your inputs. We document every data flow and involve your data protection lead from the start.
Workshops and proofs of concept are fixed scope. Production builds are fixed-scope phases. Ongoing improvement and monitoring run on a retainer. An embedded team model is available for larger programmes.
Then you have learned something early, and the memo explains why and what would need to change. That is a better result than learning it after the build.
Yes. The governance service covers classification of your systems, the obligations that follow and the controls and documentation to meet them.