AI application development
We turn AI capabilities into product features. We do not train foundation models.
The hard part is not the model
A demo takes days. A feature you can put in front of users takes considerably longer, and most of the work in between has little to do with the model:
- Evaluation — what standard decides that it is good enough, and who applies it
- Failure handling — what the product does when the model is wrong. This is usually what determines whether a feature can ship at all
- Cost and latency — the per-call cost multiplied by real call volume is often the number that decides feasibility
- Data boundaries — what may leave the system, what may not, and how that is demonstrated
- Long-term operation — models and APIs change; something has to watch it after launch
How we work
- Assessment — first, whether the problem warrants AI at all. A fair share of requests are better served by rules or search: cheaper, more predictable, easier to debug
- Prototype and evaluation — against your real data, not demo data
- Productisation — integration with existing systems, edge cases, monitoring
- Deployment and ongoing support — see managed server operations
What we do not do
- We do not train foundation models.
- We do not quote accuracy figures before seeing your data and your evaluation criteria. Any number offered at that stage is invented.
Deliverables
Assessment and recommendation · prototype and evaluation results · source code and integration documentation · deployment and monitoring configuration
Get in touch
Tell us the problem you are trying to solve, and how it is handled today.