Do we need a data scientist on staff?
No. We handle the technical work. What you do need is someone who understands the business problem well enough to validate whether the model's outputs make sense. That person does not need to write code.
The six stages
Every project follows the same backbone. Some stages compress for smaller jobs; larger programmes may loop through stages three and four more than once. The timeline below assumes a mid-size engagement.
1. Discovery sprint (weeks 1–2)
We meet your team, look at the data you have, and figure out what is feasible. This is a paid engagement with a fixed fee of £2,400. You receive a written report covering data quality, model options, expected accuracy ranges, infrastructure needs, and a cost estimate for the full build. If the numbers do not justify going further, you keep the report and owe nothing else.
During discovery we also identify who on your side will own the system after handover. That person joins every fortnightly review from day one so there is no knowledge gap at the end.
2. Data preparation (weeks 3–5)
Raw data is rarely ready for modelling. We clean, de-duplicate, and structure it. If you store information across multiple systems, we write extraction scripts and unify everything into a single pipeline. We document every transformation so your internal team can reproduce or modify it later.
Data stays on your infrastructure unless you explicitly approve transfer to our development environment. When transfer is necessary, we use encrypted channels and delete copies within 14 days of project completion.
3. Model development (weeks 4–8)
This is where the AI gets built. We run experiments: trying different algorithms, tuning parameters, testing on held-out data. You get a weekly summary showing accuracy, precision, recall, and any trade-offs we have made. We do not hand you a black box; every model ships with a plain-English explanation of how it makes decisions.
4. Integration and testing (weeks 7–10)
The model connects to your live systems via an API or a batch pipeline, depending on your latency requirements. We write automated tests that flag when model accuracy drifts below an agreed threshold. A staging environment mirrors production so we can catch issues before they reach real users.
5. Deployment and training (week 10–11)
Go-live. We deploy to your cloud account (AWS, Azure, or GCP) or on-premise servers. Your designated owner receives a hands-on training session covering the dashboard, alert system, and basic troubleshooting. We also provide written runbooks.
6. Monitoring and support (ongoing)
For the first three months after launch we monitor model performance at no extra charge. After that, you can opt into a monthly support retainer (starting at £800/month) that covers retraining, drift detection, and priority bug fixes. Or you can run it yourself; the system is yours.
What you get vs. a typical consultancy
We have heard the same complaints from clients who tried larger firms first. Here is how our process compares on the points that matter most.
| Aspect | Ai Central Solutions | Typical large consultancy |
|---|---|---|
| Discovery cost | £2,400 fixed | Often £15k–£30k |
| Time to first working model | 6–8 weeks | 12–20 weeks |
| Code ownership | Yours from day one | Licensed back to you |
| Post-launch support | 3 months included | Separate contract |
| Team continuity | Same engineer throughout | Rotating staff |
Questions we hear often
What if our data is messy?
Most data is. That is what stage two is for. We have worked with spreadsheets held together by VLOOKUPs, legacy SQL Server databases with no documentation, and CSV exports from five different SaaS tools. If the information exists somewhere, we can usually get it into shape. The discovery report will flag any gaps that could block the project.
Can we cancel mid-project?
Yes. After discovery, each subsequent stage is billed on completion. If you decide to stop after data preparation, you pay for stages one and two only. All work product completed up to that point belongs to you.
How do you handle sensitive data?
We sign a data-processing agreement before any data changes hands. All transfers use TLS 1.3 encryption. Personal data can be pseudonymised before it reaches our development environment if your compliance team requires it. We have completed projects under NHS Data Security and Protection Toolkit requirements.
What happens if the model stops performing?
Models degrade when the real world shifts. Our monitoring layer catches this. If you are on a support retainer, we retrain the model on fresh data within five working days of a drift alert. If you manage it in-house, the runbook covers the retraining procedure step by step.