Frequently asked questions.

Most projects begin with technical discovery, followed by an implementation plan and hands-on delivery with your engineering or data team.

We start with a short call about the problem, the data you already have, and the constraints you work under. You leave with an honest view of whether machine learning is worth pursuing — and with next steps you can act on even if we never work together.

Teams that have decided to take machine learning seriously: product and engineering groups with a specific problem, data teams that need senior reinforcement, and leadership teams that want a realistic assessment before committing budget.

Both. Most work happens remotely with regular calls and shared documentation. On-site workshops are valuable at the start of a project and around major decisions, and we are happy to travel when it makes a difference.

Less than most people expect. We take responsibility for the analysis and the implementation, and ask for a small number of people who know the data and the business to be available for regular checkpoints.

Every engagement includes a short written update on an agreed cadence: what was done, what was decided, what is blocked, and what comes next. You should never have to ask what is happening.

Definitely. We help assess whether ML is the right solution, define success metrics, evaluate data readiness, and design a pragmatic first implementation.

We compare candidate use cases on business value, data readiness, effort, and risk. The first project should be small enough to finish, real enough to prove value, and connected to a decision someone actually makes.

Then we say so. A deterministic rule, a better report, or a simpler automation is often cheaper and more reliable than a model. Being clear about that early saves budget and credibility.

It depends on scope, access to data, and how quickly decisions are made. We agree on phases and milestones in writing before work starts, and review progress at the end of each phase so nothing drifts.

Yes. We regularly join an in-flight programme, either to deliver a defined workstream or to remove a specific blocker, and we adapt to the planning and reporting rhythm your team already uses.

Always. Every engagement produces material your team can maintain: architecture notes, decisions, evaluation results, runbooks, and next steps. If we disappeared tomorrow, the work should still hold together.

Less than you may fear for a first version, and more than you would like for a reliable one. We look at coverage, labelling quality, and how well the data represents the cases that matter before recommending an approach.

Rarely. Messy data is normal, and improving it is usually the main part of the work. We map what exists, then fix the quality issues that actually affect the outcome instead of cleaning everything for its own sake.

Yes. We audit the data, the evaluation, and the deployment assumptions, find the constraint that is really holding performance back, and fix it — frequently without changing the model architecture at all.

When they are the right tool. LLM-based features are genuinely useful for unstructured text and language tasks, but they bring cost, latency, evaluation, and privacy trade-offs that we assess like any other design decision.

With metrics that reflect the decision the model supports, tested on data that genuinely represents production. We establish a baseline first, hold data out properly, check for leakage, and report results by segment rather than as one headline number.

We define in advance what degradation looks like, what triggers retraining, and who acts on it. Monitoring and an agreed retraining path are part of delivery, not an afterthought.

Yes. We begin by understanding your data sources, cloud environment, codebase, and delivery constraints, then recommend the smallest sensible change that moves the system forward.

Mostly mainstream, well-supported tooling: Python, SQL, Git, containers, and the cloud or warehouse platform you already run on. We prefer solutions your team can hire for and maintain after we leave.

Yes, when access allows. We work inside your environment using your existing CI/CD, secrets management, and review process, so that delivery follows the standards you already hold.

You do. Everything built for you — code, configuration, documentation, and trained models — belongs to you, with no dependency on our internal tooling.

Documentation, a walkthrough of the system, and time with the engineers taking it over. Where it helps, we pair on the first changes so your team builds confidence while we are still around.

We agree on measurable technical and business outcomes upfront. Model quality, inference reliability, deployment speed, cost, or analyst time saved are all plausible metrics.

Data stays in your environment wherever possible, we work with the minimum access required, and we follow your security review and access procedures. Where data has to move, it is covered by an agreement and the appropriate controls.

Yes. We are happy to sign your standard NDA or DPA before any technical detail is shared.

It depends on scope, access to data, and how much of the implementation your team takes on. After the first conversation we propose a structure — a defined phase, an ongoing engagement, or a review — with the cost stated upfront.

Yes. A focused audit or model review is a common way to start: a contained piece of work that ends with findings, priorities, and an honest recommendation about what to do next.