AI that paysfor itself.
Most AI projects stall in the pilot. Ours do not, because we start from where the money actually leaks — then build the agents and pipelines that close the gap, deployed into the tools your team already uses.
We work as an AI consultant to founder-led businesses that are past experimenting and want something in production. The engagement starts with a grounded read on where artificial intelligence genuinely pays back in your operation — and, just as often, where it does not.
As a generative AI consultant the temptation is to lead with the model. We lead with the workflow. An LLM dropped into a process nobody owns produces confident output that nobody trusts and nobody uses. So we map the process first, fix what is broken in it, and only then automate — which is why the tooling we ship still gets used six months later.
Because we build as well as advise, there is no handover gap. The person who wrote the assessment is the person who deploys the agent.
The work, in four parts.
AI opportunity assessment
A costed view of where AI moves a number in your business — ranked by payback, not by novelty.
- Workflow-level automation audit
- Cost-to-serve and time-saved modelling
- Build-versus-buy recommendation
- Sequenced 90-day roadmap
Generative AI & LLM systems
Retrieval, prompting, evaluation and the plumbing that makes a language model reliable enough to put in front of a customer.
- RAG pipelines over your own data
- Multi-agent orchestration
- Evaluation harnesses and regression suites
- Cost, latency and token budgeting
Agentic workflow automation
Agents that do the manual work — reading, routing, drafting, reconciling — inside the systems you already run.
- Tool-calling and MCP integration
- CRM, helpdesk and finance connectors
- Human-in-the-loop guardrails
- Audit trails and observability
Data foundations for AI
The unglamorous half of every data and AI engagement: making the inputs trustworthy before anything reads them.
- Source-of-truth definition
- Pipelines, warehousing and sync
- Data quality and lineage checks
- Metric definitions the team agrees on
This is for you if
- 01You have run an AI pilot that never made it to production
- 02Your team is spending hours on work that a model could do in seconds
- 03You have been quoted a six-figure AI programme and want a second opinion
- 04You need someone who will build it, not just scope it
Deliverables, all yours.
- AI opportunity assessment with ranked payback
- Target workflow designs, AI-enabled end to end
- Deployed agents and automations, in production
- Evaluation suite so quality is measured, not assumed
- Documentation and training for your team
- Source code and repositories, owned by you
AI consulting, answered.
An agency sells build hours against a brief you wrote. We start earlier — deciding what is worth building at all — and we are accountable for whether the thing gets adopted, not just whether it ships. On roughly a third of engagements the honest recommendation is a process fix rather than a model.
Find out what is actually slowing you down.
A 30-minute discovery call. You will leave with at least one thing worth fixing — whether or not you work with us.