Machine Learning Development Services Built Around Your Data
Models and tools built around your data. Our machine learning development services are for problems that general AI tools can't solve on their own: predicting demand, spotting fraud, classifying images, ranking products or flagging risk, using the data only your business has. We prepare the data, build and test the model, and put it to work inside the tools your team already uses.
When a custom model is the right answer, and when it isn't
Custom machine learning is worth it when three things are true: the problem is specific and repeated often, getting it right has real value, and you have (or can collect) data that reflects it. If any of these is missing, simpler options may serve you better. Google's own Rules of Machine Learning begin with a rule we agree with: don't be afraid to launch a product without machine learning.
| Situation | Usually the better choice |
|---|---|
| A specific prediction or classification, with good historical data | A custom ML model (this page) |
| Understanding or generating text, with no special data | A general-purpose language model, integrated into your product |
| Clear, stable rules that experts can write down | Rule-based logic or workflow automation |
| Little or no relevant data yet | Start collecting data, and use rules in the meantime |
What our machine learning development services cover
Problem framing
We turn a business question ("which customers are likely to leave?") into a precise ML task with a clear measure of success, and agree what accuracy is good enough to be useful.
Data assessment and preparation
Most of the work in ML is the data. We review what you have, check its quality and coverage, clean and label it, and build reliable pipelines so new data flows in consistently.
Model development
We test several approaches, from simple baselines to more advanced models, and choose the one that performs best on your data while staying understandable and affordable to run.
Common solution types
- Predictive models: demand, churn, lead scoring, delivery times
- Classification: routing documents, detecting fraud or defects, tagging content
- Computer vision: reading images and video, from product photos to scanned documents
- Recommendations: suggesting products, content or next actions
- Anomaly detection: spotting unusual transactions, readings or behaviour
Evaluation and fairness checks
We test models on data they haven't seen, look at where they fail, and check whether errors fall unevenly on particular groups. Results are explained in plain language, not only as metrics.
Deployment and integration
A model only creates value when people use it. We serve it through an API and connect it to your dashboards, apps or workflows, using a Node.js and PostgreSQL backend where it fits.
Monitoring and retraining
Data changes over time, and model accuracy can drift with it. We monitor performance in production and retrain or adjust the model when results start to slip.
Responsible by design
We use widely recognised guidance, such as the NIST AI Risk Management Framework, to think through risks from the start. If your model will be used in sensitive areas such as hiring, credit or access to services, it may fall under the EU AI Act's rules for high-risk systems, which the European Commission says will apply in stages from 2027. We'll flag this early so you can take legal advice.
How an ML project runs
Discovery
Define the problem, the decision it supports and how success is measured.
Data review
Assess availability, quality and gaps.
Proof of concept
A first model tested on historical data to see whether the approach works.
Build
Improve the model, set up data pipelines and build the integration.
Deploy
Put the model into use, often first alongside existing decisions.
Monitor
Track performance and retrain as needed.
Is this the right service for you?
| If you need… | Start with |
|---|---|
| A model trained on your own data to predict, classify or recommend | Machine learning development services (this page) |
| General-purpose language AI added to your product | LLM & API integration |
| Repetitive processes automated, with AI steps | Workflow automation |
| Help deciding whether AI is worth it at all | AI consulting & strategy |
Frequently asked questions
Typically problem framing, data assessment and preparation, model development, evaluation, deployment into your systems, and ongoing monitoring and retraining.
It depends on the problem. Some tasks work with relatively few good examples, while others need far more. We assess your data early and tell you honestly whether it's enough.
Yes. Most projects start from data you already hold in databases, spreadsheets, documents or images. We handle cleaning, labelling and structuring.
General language models are trained on broad public data and are good at language tasks. Custom ML models learn patterns from your specific data to make predictions or classifications about your business.
Only if it's monitored. As data and behaviour change, accuracy can drift. We set up monitoring and retraining so the model stays useful.
Timelines and pricing depend on scope, so contact us for an estimate. Data readiness is usually the biggest factor.
Let's look at your data
Tell us the decision you'd like to improve and the data you hold. We'll come back with a clear next step.