From idea to production, developed end to end
A five-stage development process, each stage concluding with a defined deliverable.
01
Feasibility
We define the problem, audit the data you hold, and decide together which number success will be measured by. At this stage we establish the performance range reachable with current methods, along with the principal risks.
- What we ask from you
- Information about the process and existing data where available. Where none exists, the collection plan is established together.
- What we deliver
- feasibility report, proposed approach, estimated timeline and cost range
02
Data
We collect, label and quality-check the data. We write the labelling guide together; without domain knowledge there is no correct label.
- What we deliver
- a versioned, documented dataset ready for training
03
Model
We develop the model, compare alternative approaches and evaluate it on a held-out test set. We share results as they are; where the model fails goes into the report too.
- What we deliver
- trained model, evaluation report and comparative metrics
04
Integration
We connect the model to your existing systems: API, interface, edge installation or cloud deployment. Load testing and commissioning happen at this stage.
- What we deliver
- a working system, technical documentation and installation
05
Monitoring
We track performance in production: data drift, quality degradation, latency and cost. When needed, we run the retraining cycle.
- What we deliver
- monitoring dashboard, alerting rules and a retraining plan
Every step is a decision point
This structure allows the project to be concluded at any stage. Where the feasibility study establishes that the problem cannot be solved with the available data, that assessment is communicated at the earliest stage.
A technical assessment for your AI project
Your project's feasibility, risks and timeline are assessed in a technical consultation.