We developed this framework after running more than forty AI engagements over four years. The goal is straightforward: reduce risk early, build fast in the middle, and hand over something your team can maintain without us.
We meet your stakeholders, review existing data sources and map the business problem to a specific, measurable outcome. For a retailer this might be "reduce dead stock by 15% within six months." For a healthcare provider it could be "triage inbound referrals within four hours instead of two days."
At the end of discovery we deliver a written feasibility report. It covers data availability, estimated model accuracy ranges, infrastructure requirements and a fixed-price quote for the build phase. If the data is not there or the problem is not a good fit for machine learning, we say so. You pay only for the discovery work and walk away with a clear picture of what is and is not possible.
Raw data is rarely ready for modelling. We clean it, handle missing values, engineer features and build reproducible data pipelines. If your data lives in multiple systems we write extraction scripts that pull from each source and merge everything into a single, versioned dataset.
This stage often surfaces quality issues nobody knew about. One financial-services client discovered that 12% of their transaction records had duplicate timestamps, which had been silently skewing their internal reports for over a year. Fixing that alone justified the project cost.
We train candidate models, compare them on held-out validation data and select the best performer. Depending on the problem we might test gradient-boosted trees, neural networks, or a combination. We document every experiment so you can trace exactly how the final model was chosen.
Interpretability matters to us. Where possible we use SHAP values or similar techniques to explain individual predictions in plain language. Your compliance team should be able to understand why the model flagged a particular case, not just that it did.
A model sitting in a notebook is not useful. We package it as a REST API, embed it in your existing application or connect it to your data warehouse via scheduled batch jobs. We write integration tests and set up monitoring dashboards so your ops team can see model latency, error rates and prediction distributions in real time.
If you use Azure, AWS or Google Cloud we deploy on your existing infrastructure. For clients who prefer on-premise hosting we containerise the model with Docker and provide deployment scripts for your environment.
Before full rollout we run the model in shadow mode alongside your current process. Predictions are logged but not acted on. This lets us compare model output against real outcomes without any risk to operations.
Once shadow-mode results confirm the model meets the accuracy targets defined in discovery, we switch to live. The transition is usually a single configuration change, not a big-bang release.
Data drifts over time. Customer behaviour changes, product ranges shift, regulations evolve. We set up automated drift-detection alerts that notify your team when model performance drops below a threshold you choose.
Retraining can be scheduled quarterly, triggered by drift alerts, or both. We provide a retraining playbook so your internal data team can run the process independently. If you prefer, we offer a support retainer that covers quarterly retraining, performance reviews and priority bug fixes.
Communication happens weekly at a minimum. You get a short written update every Friday afternoon summarising what was done, what is planned next and whether anything is blocked. We use your preferred project tool, whether that is Jira, Asana, Trello or a shared spreadsheet.
We do not disappear between milestones. If a question comes up on a Tuesday morning, you can email or call and expect a response within a few hours. Our phone line is staffed Monday to Friday, 9 am to 5.30 pm.
All code and documentation belong to you. We work in a Git repository you own, and every commit is visible to your team from day one. When the project ends you have full access to the source code, trained model weights, data pipelines and deployment scripts. There is no vendor lock-in.
Discovery sprints start at £4,500. Full build projects range from £18,000 to £75,000 depending on complexity, data volume and integration requirements. We always provide a fixed-price quote after discovery so there are no surprises.
Not necessarily. We can handle the entire pipeline. That said, having at least one person on your side who understands the data sources speeds things up considerably. We are happy to train a member of your team during the project so they can take over maintenance afterwards.
We define success criteria in the discovery report. If the model cannot meet those criteria after a reasonable iteration cycle, we stop and explain why. You are not locked into paying for a build phase that is unlikely to deliver value. In our experience this happens roughly once in ten engagements, and the discovery report usually flags the risk before any build work begins.
Yes. Many clients begin with a single use case, measure the return, and then expand to additional models. A pilot typically covers discovery plus a minimal build and takes six to eight weeks. It is the lowest-risk way to test whether AI fits your operations.