Have you introduced AI only to find that it is not delivering? Realising the full value of AI and data requires everything from understanding the business and developing algorithms to system design, engineering, testing, security and operations. We support the entire journey.
0M+/day
Records handled by our recommendation system
0.0×
CVR uplift from recommendation optimisation
+¥1.5M/mo
Sales impact from a dynamic-pricing campaign
0.0×
Sales multiplier measured for the same campaign
Figures are results from specific projects and periods.
Rather than labelling ourselves an "AI company", we organise our work by the business problems you can bring to us.
From analysing business data to demand forecasting, recommendation and price optimisation — validated not only for accuracy but for whether it can keep running.
Turning generative AI, image, video and voice models into products that hold up in day-to-day operations — not one-off demos.
Web, API, CMS and data platforms — plus analysis and improvement of the systems you already run.
Testing, monitoring, cost optimisation and vulnerability assessment. We design for what happens after launch.
Projects where we owned the work end to end — from understanding the business to running it in production.
Change what you offer to whom, and the outcome changes.
What looked like the problem
Improve recommendation accuracy.
What the real problem was
Decide the ordering that maximises business outcome within a fixed number of slots.
Designed around processing speed, data refresh and operational load — not accuracy alone.
Before you change the price, understand why people come.
What looked like the problem
Calculate the optimal price.
What the real problem was
Understand why, and when, someone decides to come back for another treatment.
Our analyst used the service directly, following the customer experience from booking through consultation, treatment and the return visit, before designing the system.
Not only building new systems — making the existing one stronger.
What looked like the problem
Rebuild the system.
What the real problem was
Make visible where cost and risk have accumulated inside the system you already run.
We start by identifying what can be improved in place, rather than assuming a rebuild.
An AI that can talk is not yet a business system.
What looked like the problem
Have an AI conduct the interview.
What the real problem was
Build something where candidates can complete an interview with confidence and companies can manage the results safely.
The system is designed around the candidate's experience and the company's operations, rather than placing the AI model at the centre.
Candidate
Applicant
Interview
AI interview
Recording / AI
Recording & AI processing
Data
Data storage
Admin CMS
Admin console & permissions
Operation
Operations, maintenance, security
From a tool that generates once, to a production system.
What looked like the problem
Generate high-quality video.
What the real problem was
Let people who don't know video production handle the production process.
Designed on the assumption that work in progress must be preserved and steps must be repeatable — not just that output looks good.
This is how we build. Where most teams write QA, we write Break.
Understand
Understand the business and the front line
Design
Design for the scale it will reach
Build
Build fast, with AI in the loop
Break
Work through every scenario we can think of
Observe
Check the data, the logs and the monitoring
Improve
Keep improving from real operation
We don't only confirm the happy path. We enumerate and verify the scenarios that actually happen: dropped connections, abandoned sessions, duplicate submissions, malformed input, concurrent operations.
And we don't stop at what the screen shows — we check the state inside the system: the database, the logs, the storage.
What the screen shows
What we check behind it
Release is not the finish line; it is where operation begins. Failures, abuse and cost all show up once the system is live.
Detect anomalies before a person notices
Keep what happened traceable after the fact
Show it only to the people who should see it
Break it ourselves, from the attacker's side, first
Design so nothing is lost when things go down
Verified all the way through to restore
Keep growth in usage from becoming growth in the bill
Keep fixing based on how it is actually used
Technology is a means to achieve business outcomes.
Language
Frontend
Backend
Data
Infrastructure
AI / ML
We select technology based on the problem and the operating conditions. We do not build proposals around a particular AI model, cloud or framework.
You don't need firm requirements. These are all good places to start: