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Custom AI model training means training or fine-tuning a model on your own records, documents and images, so it learns the part numbers, defect types and industry terms that off-the-shelf models miss. We audit and label your data, train the model, measure its accuracy on a held-out test set and deliver it to run on your own servers.
Off-the-shelf models don't know your products. A general-purpose model looks impressive in a demo, then stumbles on the things that make your business yours: part numbers, defect types, acceptance criteria, the shorthand your teams use every day. The result is a tool whose every output still needs a person to check it.
The fix is a model trained on your own records, documents and images. We handle the whole job: auditing whether your data is good enough, cleaning and labelling it, choosing and fine-tuning a suitable model, measuring accuracy on data the model has never seen, and wiring it into the software you already run. The model can live on your own servers, so training data does not have to leave the organisation.
We review ERP records, archived documents, sensor logs or site photos, remove incomplete, duplicate and contradictory entries, and agree exactly what the model will learn from.
Our team labels the data, with joint sessions alongside your specialists where domain knowledge matters. Training, validation and test sets are kept strictly separate.
We pick an image, text or time-series model to suit the problem. Fine-tuning an existing model usually needs far less data and budget than training from scratch.
Results are reported with precision, recall and a confusion matrix. Together we set the confidence level below which the model hands the decision to a person.
Run the model in the cloud, on your own servers or on a device on the shop floor. Sensitive data stays in-house and there is no dependency on an outside service.
As products, suppliers or processes change, performance is tracked. When it slips, the model is retrained on fresh data and the new version is tested against the old one.
For applied examples, see our computer vision, document OCR and demand forecasting services. If what you need is an assistant that talks to visitors on your website, you may not need to train a model at all: SmartOperator is an AI customer agent prepared with your company's knowledge base, catalogue and tone of voice, and you can buy SmartOperator through Smart360.
We assess the volume and quality of your data and tell you plainly whether the problem is worth solving with AI.
Data is gathered, cleansed and labelled. Where it is thin, augmentation or synthetic data is considered.
The model is trained and tuned, then scored on a held-out test set that played no part in training. You see the numbers.
The model connects to your existing software via API, and your team receives the retraining procedure and documentation.
If feasibility shows your data is too thin, or that a simple rule set would do the job, we will tell you. We would rather not take budget for a model that won't perform; if needed, we help you build the data collection first.
A distributor wants to match the products on its suppliers' price lists to its own stock codes. Each supplier writes the same item with different names, abbreviations and units, and a general-purpose model cannot always tell that "M8x30 DIN933 galv." and "Hex bolt 8*30 galvanised" are the same part. Matches made by hand in the past become training data, the model is fine-tuned on them and then scored on a test list it has never seen. In live use the model proposes the matches it is confident about and leaves the rest to a person, and every decision that person makes feeds the next round of training.
For more detail, read our custom AI model training guide, our article on data labelling for AI and our explainer on keeping a model accurate after go-live (MLOps). All related articles are on the AI guides page.
Tell us what data you have and the job you want done; after the data feasibility review we will tell you whether the model is viable and send the scope in a written proposal.
A custom AI model is trained or fine-tuned on your own records, documents and images. It learns the part numbers, defect types and industry terms that off-the-shelf models miss, so it makes fewer mistakes on the exceptions that are specific to your business, and you no longer need someone to check every output.
It depends on the problem, which is why every project starts with a data feasibility review. We assess how much data you have and how good it is, and remove incomplete or contradictory records. If data is scarce, augmentation or synthetic data can help. If the data is not good enough, or a simple rule set would do the job, we tell you plainly.
In most cases, fine-tuning an existing model gets results with less data and lower cost than training from scratch. We choose an image, text or time-series model to suit the problem. The right approach is settled during the feasibility stage, based on your data and the accuracy you need.
Training, validation and test sets are kept separate. Results are reported on a test set the model never saw during training, using precision, recall and a confusion matrix. Together we agree the confidence level below which the model hands the decision to a person, so it does not guess on uncertain cases.
Yes. The model can run in the cloud, on your own servers or on a device on site. Training data and sensitive data can stay in-house, with no dependency on an outside service. The model connects to your existing software through an API, and we hand over the retraining procedure and documentation.
Performance can drop as your product range, suppliers or processes change. We monitor it, retrain the model on new data when performance falls, and compare the old and new versions on the same test, so a new version only goes live once it is shown to be better.
Cost depends on the problem to be solved, how much data you have and how clean it is, how much labelling is needed and where the model will run. We first assess whether your data is sufficient, then send a written proposal with scope, stages and price. If a rule-based solution would solve the problem more cheaply, we say so plainly.
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