From first conversation to production AI. Here is how.
We match the approach to the problem and can work within your team to build in your environment. Progression from discovery to pilot and production depends on test results and the criteria agreed with you.
Engagement Path
From first contact to full operations
Engagement Models
Choose the shape that fits
Discovery
We understand the process, the data and the constraints, and write the success criterion down. We tell you early if AI is not the answer or if the data falls short. You leave with a prioritised plan, whether you continue with us or not.
Best for
Organisations exploring AI for the first time or choosing between several ideas.
Output
Prioritised use cases, a written success criterion, the state of your data and a board-ready summary.
Proof of Concept
A working prototype for one use case, on your real data and measured against a test set built with you. At the end, a recommendation to go ahead or not, with the results in front of you.
Best for
Validating a specific idea with real data before committing further.
Output
Working prototype, test set, measured results and a go/no-go recommendation.
Pilot
The system in real use with a small group, in shadow mode or with human approval on actions that cannot be undone, and the cost per transaction measured.
Best for
Organisations with a positive proof of concept that want to see the system working day to day.
Output
Pilot system in use, a record of every action, monitoring and measured cost.
Production
We harden what the pilot proved: pinned model versions, regression tests on every change, rollback, access controls and operations runbooks. Where the data cannot leave the company, we use local models.
Best for
Organisations ready to put AI into production.
Output
System in production, operations runbooks, a trained internal team and a rollback plan.
Ongoing Support
Monitoring, review of the records, re-testing when vendors release new versions, team training and architecture advice, for as long as it makes sense.
Best for
Organisations running AI in production that want expert support without a large internal team.
Output
Performance reports, operational reviews, regression tests and advisory sessions.
Working with us.
Visible progress in every sprint
You see real progress throughout the work, in demos.
Honest about what AI cannot do
We will tell you if AI is not the right answer before we start. Limitations are stated clearly before any commitment is made.
One team, end-to-end
No handoff between sales and delivery. The people who scope your project are the people who build it.
Outcomes over hours
We aim for measurable results. Every project starts with a written success criterion and a baseline, and we evaluate on your own data.
Questions and answers.
What does "forward deployed" mean, and is it the only way you work?
Forward deployed means we embed with your team and build AI into your business with production deployment subject to agreed acceptance criteria, instead of handing over a deck. It is our main method, but not the only one. We also build AI agents, new products and tools from scratch, and redesigned processes. We pick the approach your problem needs.
How long until we see results?
It depends on the scope, the data and the number of systems involved. Indicative planning estimates, confirmed after discovery: discovery 1 to 2 weeks, proof of concept 2 to 6, pilot 4 to 8, and production depends on what the pilot shows. We show progress through real demos along the way.
Do we need to have good data first?
Not necessarily. Discovery assesses the state of your data, and we tell you early if it cannot support the question. If there are gaps, the first phase becomes building the data foundation. Data intelligence is one of our example services for exactly this reason.
Can you work with our existing IT team?
Absolutely. We integrate with your team's workflows, tools, and processes. Knowledge transfer is built into every engagement.
What about data privacy and GDPR?
We design for GDPR. We map which obligations may apply to each system and confirm them with your legal advisers. When data cannot leave the company, we use local models on your own infrastructure. Before sending personal data to an external provider, we confirm the lawful basis, data-processing agreement and applicable transfer conditions with your legal advisers. We define the necessary data, appropriate pseudonymisation measures and processing locations.
We are a small company. Is AI relevant for us?
Yes. The work scales with the size of the problem, not the size of the company. An initial assessment explores whether AI fits your situation, and we start with a single process. Restaurants, architecture studios, workshops and clinics are examples of where AI can help.
What technologies do you use?
We choose the model for each task after testing it on your data, and test again when vendors release new versions. We can use vendor models, open models on local infrastructure, or both, depending on your context and the rules that apply to your case.