AI & Data
Machine Learning on Your Data
Custom machine learning for prediction, classification, ranking and anomaly detection, trained and validated on your data and tied to a business decision.
Overview
Machine Learning Development
Machine learning development here means choosing the right modeling approach for a specific prediction problem. We frame the target, build features from your records, compare candidate algorithms and validate them honestly, including on data the model has never seen, so reported performance reflects what you will experience in use. That keeps expectations realistic and avoids surprises once the model meets live data.
We favor simpler models when they perform comparably, because they are cheaper to run and easier to explain. Where deep learning is justified, for images, audio or complex sequences, we use it. Throughout, we document assumptions, limitations and the cases where predictions should not be trusted. This protects you from relying on predictions in situations where they are weakest.
Context
Problems we solve
- 01
Poorly framed targets
A model is only as useful as the question it answers. We define the prediction target and its business consequences first.
- 02
Small or biased data
Limited or skewed data can mislead a model. We test for bias, leakage and coverage gaps before trusting results.
- 03
Overfitting
Models can look great in testing and fail in the real world. We use proper validation splits and realistic test conditions.
- 04
Black-box concerns
Stakeholders often need to understand why a prediction was made. We use explainability methods suited to the model.
Capabilities
What we deliver
- 01
Predictive modeling
We build models for churn, demand, risk scoring, pricing and similar tabular prediction tasks.
- 02
Classification and ranking
We develop classifiers and ranking models for triage, tagging, recommendations and search relevance.
- 03
Anomaly detection
We detect unusual behavior in transactions, sensors or logs, with thresholds tuned to your tolerance for false alarms.
- 04
Computer vision and NLP
We apply image and text models where the data calls for them, using pretrained models where sensible.
- 05
Feature engineering
We turn raw records into informative features, documenting each so the logic is transparent.
- 06
Explainability and reporting
We provide feature importance, error analysis and plain-language summaries for non-technical reviewers.
Where it fits
- Customer churn prediction
- Demand and inventory forecasting
- Fraud and anomaly flagging
- Product recommendation engines
- Image-based quality inspection
Technologies we use
- Python
- scikit-learn
- XGBoost
- TensorFlow
- PyTorch
- pandas
- OpenCV
- Jupyter
Approach
How we deliver
- 1
Discover
We learn your goals, users, constraints and existing systems before proposing anything.
- 2
Plan
Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.
- 3
Design
User flows, interfaces and technical design are shaped and reviewed with you early.
- 4
Build
Work is delivered in short iterations with regular demos, so progress is always visible.
- 5
Test
Functional, integration and performance checks run throughout, not only at the end.
- 6
Launch & support
We deploy, monitor and keep improving the product after it goes live.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
How much data do we need for machine learning?
It depends on the problem, the number of signals and how noisy the data is. We run a quick feasibility check on your sample data and tell you plainly if there is not enough yet.
Can you guarantee a certain accuracy?
No honest team can promise accuracy before seeing the data. We set target metrics together, test against them and report results, including where the model is weak.
Will the model need updating?
Usually yes. Behavior and data change over time, so we plan monitoring and periodic retraining rather than treating the model as finished at launch.
How is this different from data science work?
Machine learning development produces a model that runs inside a product or process. Data science is broader and often focuses on analysis and insight, which we cover separately.
Explore
Keep exploring
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Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.