Predictive models, language AI and computer vision, taken to production.
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§ Signs
Our data science services turn your data into models that forecast, classify, detect and explain, and keep them working in production. We build predictive models for demand forecasting, churn, maintenance, fraud and pricing; language AI for document intelligence, retrieval-augmented assistants and NLP; and computer vision for quality inspection and video analytics. Every model is measured against an agreed baseline on held-out data before it ships. MLOps practices, including deployment, monitoring, drift detection, feature stores and CI/CD for machine learning, keep models accurate as your data changes.
Data science pays off when a repeated decision can be made better, faster or at a larger scale.
Demand, staffing or cash forecasts are built by hand and often miss.
Promising notebooks exist, but nothing runs in production or reaches a decision maker.
A model went live a year ago and nobody knows whether it still works.
Fraud, anomaly or routing rules multiply and still miss new patterns.
Camera feeds, scanned forms or contracts hold information nobody has time to extract.
§ What we deliver
Choose an area to see the services in it.
§ Proof
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
§ Approach
We agree the decision the model supports, the success metric and the baseline to beat.
You receive Problem framing and evaluation plan
We audit the available data, its quality and gaps, and engineer candidate features.
You receive Data audit and feature set
Candidate models are trained and evaluated on held-out data against the baseline.
You receive Evaluated model with an evaluation report
We back-test against held-out data, compare with the current method or a simple baseline, and review results with your domain experts.
You receive Evaluation report with a go or no-go recommendation
The model is deployed with CI/CD, integrated with your systems and documented.
You receive Production deployment with documentation
Performance and drift are monitored, and models are retrained on a schedule.
You receive Monitoring dashboard and retraining schedule
§ Tech stack
Azure Machine Learning§ Engagement models
A defined scope, timeline and deliverables, from design through go-live.
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Data engineers who join your team and work in your tools and processes.
Consultants and developers working remotely, with overlap in your working hours.
Architecture reviews, platform choices and design guidance for your own team.
Monitoring, incident handling and ongoing fixes for pipelines and databases in production.
A short, fixed-scope engagement to test an approach on your data or plan a migration.
§ FAQs
Predictive models (demand forecasting, churn prediction, predictive maintenance, fraud and anomaly detection, pricing optimization), language AI (document intelligence with LLMs, RAG knowledge assistants, NLP) and computer vision (quality inspection, video analytics).
We agree a success metric and a simple baseline first, then evaluate every model on held-out data against that baseline before it goes to production.
MLOps: we monitor performance and data drift, track experiments, and retrain and redeploy models through CI/CD for machine learning.
Models need reliable data. If pipelines or a warehouse are missing, our data engineering services build them alongside the first model.
Yes. We deploy on the platform you already use where it fits, for example Databricks or Snowflake, or as an API service.
It depends on the problem. Forecasting needs enough history to cover seasonality, and classification needs enough labeled examples of each outcome. The data audit answers this for your case before modeling starts.
Usually a few weeks, time-boxed and agreed up front, ending with an evaluation report and a go or no-go recommendation.
Yes. We provide feature importance and per-prediction explanations such as SHAP values, so people can see why a score is high or low.
LLMs suit unstructured text, documents and conversation. Classical models are usually cheaper, faster and easier to validate for structured prediction such as forecasting or churn. Many solutions combine both.
You do. Models, code and documentation are delivered to your repository and environment.
We minimize the personal data a model uses, pseudonymize where possible, keep processing in your environment and design controls around your GDPR, HIPAA or SOC 2 obligations.
§ In detail
The decision, the success metric and the baseline to beat, agreed before any modeling.
What your data can and cannot support, and a reproducible feature pipeline.
The model, its accuracy against the baseline and where it is weak.
Batch scoring or a real-time API, delivered into the tools your teams already use.
Drift and performance tracking with alerts and a retraining routine.
A model card covering data, intended use, limits and fairness checks.
The people who make the decision today review features, errors and results.
A time-boxed proof of concept with a clear go or no-go before larger investment.
We work in your cloud environment wherever possible, under NDA, with access limited to what the use case needs.
Your team takes over with documentation, or we keep running and retraining the models.
Video analytics that detect people, vehicles and events in CCTV feeds, as in our CCTV intelligence case study.
Prediction and performance models built on event data, and AI voice pipelines for broadcast content.
Demand forecasting, churn prediction and pricing models.
Predictive maintenance on sensor data and visual quality inspection on the line.
Risk and engagement models built with privacy controls designed around HIPAA and state privacy rules.
§ Contact
Tell us the decision you want to improve and the data you have.
