AI and ML Engineering Services

Your FAQs on AI and ML Engineering Services

Q1: What is the difference between AI and ML engineering and an AI Discovery Workshop?

A1: The AI Discovery Workshop is a short, structured engagement that decides what to build. We map your candidate use cases against technical feasibility, the data you can realistically get and the business value at stake, then prioritise the ones worth funding and rule out the ones that will not pay off. AI and ML engineering services are the build that follows: data pipelines, model development, deployment and the monitoring that keeps the system running in production. Teams who are still deciding where AI fits usually start with the workshop, while teams who already know the use case come straight to engineering. The two connect cleanly, so the feasibility findings, success criteria and architecture sketch from the workshop feed directly into the build.

Q2: Can you take our proof of concept to production?

A2: Yes, and it is the step most teams underestimate. A proof of concept proves an idea on clean, static data, while production has to survive live traffic, messy inputs, latency limits and cost ceilings, which is where most models quietly break. We review what you have, keep the parts that scale and rebuild the parts that do not, then add the data pipelines, model serving, automated testing and monitoring a production system needs. You finish with a model that is versioned, observable and maintainable by a team rather than a script that only one engineer understands. In practice this is the difference between a demo that impresses once and a feature your users rely on every day.

Q3: Do you work with our data, or do we need a clean dataset?

A3: You do not need a clean dataset to start. Working with raw, incomplete or unlabelled data is part of the engagement, and in most projects data engineering is the larger half of the effort. We handle collection, cleaning, labelling, feature engineering and augmentation, and we set up the pipelines that keep that data flowing once the model is live. We are also explicit early about where the data is too thin, too biased or too inconsistent to support the outcome you want, because an honest read on data quality at the start saves you from a model that tests well and then fails in the field.

Q4: How do you handle AI in a medical or regulated product?

A4: We engineer to the right framework from the first sprint rather than bolting compliance on at the end. AI that diagnoses, monitors or informs treatment usually falls under EU MDR, where Rule 11 drives the software risk classification, and many products now also sit inside a risk tier of the EU AI Act. Our in-house regulatory team works alongside the engineers and aligns the model, its data governance and its technical documentation with IEC 62304, ISO 14971 and ISO 13485 as the product is built. That way classification, risk management and traceability grow with the system, so you reach audit and conformity assessment without a last-minute scramble. Having the regulatory and engineering work under one roof also means design decisions are checked against the standard before they are baked in, not after.

Q5: Can you build with LLMs and generative AI securely?

A5: Yes, this is something we can build. We develop LLM and generative-AI features on EU-hosted infrastructure, with access controls, audit logging and data handling that keep your and your users' data inside the boundary you set. Where confidentiality matters we use retrieval-augmented generation over your own content, keep tenant data isolated and make sure your data is not used to train third-party models. We design for the failure modes specific to generative AI, including prompt injection, data leakage, hallucination and unbounded cost, with guardrails and evaluation rather than assumptions. Our ISO 27001 certification covers information security across every active project, and we help you meet the EU AI Act transparency obligations that apply to your product.

Q6: Can the model run on-device or at the edge?

A6: Yes. When latency, cost, connectivity or privacy rule out a round trip to the cloud, we can run inference on the edge or directly on the device. We shrink models to fit constrained hardware through quantisation, pruning and distillation, and we trade accuracy against memory, power and speed so the model performs within the limits of a wearable, sensor or embedded board. We draw on our embedded and wearable engineering experience for this, and where it makes sense we split the workload, running fast inference on-device and heavier processing in the cloud, so you get responsiveness without giving up model quality.

Q7: How do you keep a model accurate after launch?

A7: Models degrade as the world they were trained on moves, so monitoring belongs in the build rather than as an afterthought. We can track accuracy, data drift and live performance, alert when metrics slip past the thresholds we agree with you and retrain on fresh data when the gap matters. The MLOps pipeline we set up makes retraining and redeployment a routine, tested operation rather than a rebuild, and keeps a full record of which model version is live and why. We agree the review cadence and accuracy targets with you up front, so the system can stay reliable over months and years instead of drifting silently after launch.