Workflow-led systems build
Automation that fitshow you already work.
LLM integrations and internal tooling built around how your team already works, not around a demo.
The useful first conversation is about the workflow, not the model. Sometimes the answer is a script rather than an LLM, and we will say so.
Why this exists
Most AI projects fail at the same point: the demo is impressive, and then nobody uses it, because it was designed around the model rather than around anyone’s actual job.
We start from the workflow. What is being done manually, how often, and what would have to be true for someone to trust a machine doing it. Sometimes the honest answer is that a script beats a model.
How it runs
Four steps, and you seework at every one.
- 01
Map the real workflow
We sit with the people doing the work. The documented process and the actual process are rarely the same, and the difference is where automation breaks.
- 02
Find the honest win
We look for the repetitive, high-volume, low-judgement steps. If the task needs judgement, we automate around it rather than through it.
- 03
Build with a human in the loop
Anything consequential gets a review step until it has earned trust. Confidence is a feature, not a metric.
- 04
Measure and hand over
Hours saved, error rates, and cost per run — reported honestly, including where it did not help.
What you get
Yours to keep,from day one.
Not deliverables handed over at the end. These exist in your accounts, in your name, while the work is happening.
- A written map of the workflow before and after
- Working tooling deployed into your environment
- Evaluation harness so you can measure quality over time
- Cost and usage monitoring per workflow
- Fallback behaviour for when the model is wrong or unavailable
- Training for the team who will operate it
Capabilities
The full list, so you cancheck we do your thing.
AI integration
- LLM integration into existing products and workflows
- Retrieval over your own documents and data
- Evaluation harnesses and quality monitoring
- Prompt and cost optimisation
Automation
- Internal tooling and admin interfaces
- Workflow and process automation
- Data pipelines and reporting
- System-to-system integration
Advisory
- AI strategy and feasibility review
- Build-versus-buy assessment
- Data readiness review
How we work together
Pick the shape thatfits the work.
Fixed price
One number, agreed up front
For work we can scope tightly. You get a written scope and a price before anything is built, and the price does not move unless the scope does.
Phased
Priced and approved per phase
For larger builds. Each phase is scoped, priced and signed off on its own, so you can stop or change direction at a known point rather than being locked into the whole thing.
Retainer
Monthly capacity
For ongoing work after launch. A set amount of our time each month, and you decide what it goes on. No minimum term.
Scoping is free in every case, and the written scope is yours whether or not you hire us.
Questions
About ai & automation.
Will our data be used to train a model?
No. We use providers and configurations where your data is not retained for training, and we will document exactly where your data goes as part of the engagement.
What if AI is the wrong answer?
We will say so. A rules engine or a well-written script is often cheaper, faster and more reliable, and telling you that costs us a bigger invoice.
How do you handle the model getting it wrong?
Every consequential action has a review step or a fallback until the numbers justify removing it. We would rather ship something narrower that people trust.