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Machine learning, generative AI, computer vision, analytics and training-data work — scoped against a process you can name, built into the software you already use, and handed over with the failure modes written down.
Most AI projects fail at the same point: nobody agreed what the model was allowed to be wrong about. A demo that is right nine times out of ten looks impressive; the same model approving refunds is a liability. So the first thing we do on an AI engagement is not modelling. It is writing down the decision, the cost of a wrong answer, and who reviews it before it takes effect.
The second thing is checking whether it needs AI at all. A rules engine, a better report or a fixed data-entry screen solves a surprising share of what arrives described as an AI problem, costs a fraction as much, and does not drift. We will tell you when that is the answer — we would rather build the smaller thing and be asked back.
Where AI genuinely is the answer, it lands inside the systems on the rest of this site. The same team builds the ERP, the portal or the POS it plugs into, which is why the integration is the part we are least worried about.
An assessment of where AI would actually pay in your operation, what it would cost to run, what your data can and cannot support, and a build order you can fund one step at a time.
Read moreAI-powered web and mobile applications, intelligent features inside existing systems, and custom AI solutions scoped against a decision your business actually makes.
Read moreCustom models for forecasting, classification, scoring, recommendation and anomaly detection — trained on your own history, evaluated against held-back data, and deployed where the prediction is acted on.
Read moreLLM integrations, retrieval-grounded assistants, internal copilots, customer chatbots and document intelligence — built so every answer can be traced back to the source it came from.
Read moreBusiness process automation, document and data-entry automation, support and reporting automation — with a person kept in the loop wherever being wrong would cost more than being slow.
Read moreObject detection, image classification, OCR, video analysis and visual inspection — built where a camera already exists and a person is currently doing the looking.
Read moreData pipelines, cleaning, dashboards, business intelligence and predictive analytics — built on one agreed definition of each number, so two departments stop arriving with different figures.
Read moreImage, video and text annotation — bounding boxes, polygons, segmentation, keypoints and classification — plus dataset cleaning, quality control and human-in-the-loop workflows.
Read moreModel-serving APIs, AI backends, cloud deployment, monitoring, cost control and maintenance — for models we built and for models you already have that never made it out of a notebook.
Read moreNot listed here? Tell us what you need — most of our work starts with someone describing an operational problem rather than naming a product.
Each service below starts from a kind of problem rather than a technology. If yours is not on the list, or you are not sure AI belongs in it at all, AI consulting is where it gets named.
The order is the same across every service above, because the risk sits in the same places.
Most of what goes wrong in an AI project starts with a promise made before anyone looked at the data. These are the ones we do not make.
We will map it, tell you what is worth building first, and quote a fixed price against a written scope.