Fintech & payments
Transaction anomaly detection
Every transaction scored in real time against learned normal behaviour — as a standalone signal or an extra layer over your existing risk rules.
Data & Machine Learning
Machine learning that earns its place in production
We build the data pipelines and predictive models behind real decisions — scoring, forecasting, classification and anomaly detection — and deploy them as fast APIs with monitoring, so your team can trust a prediction and act on it.
models · churn-v7 · production
- ROC AUC
- 0.91
- p95 latency
- 38 ms
- Data drift
- Low
Churn score distribution24h
0.0at risk ≥ 0.71.0
Top drivers
- days_since_last_login0.31
- support_tickets_30d0.22
- seats_used_ratio0.17
- plan_downgrade0.12
Sound familiar?
Where machine learning pays off
- 01
Decisions to approve, flag or prioritize are made by hand, or by rules nobody dares to touch.
- 02
Your data is spread across systems and nobody trusts the numbers on the dashboard.
- 03
A model was built once in a notebook and never made it into a live system.
- 04
Fraud, churn or demand still catches you by surprise instead of being predicted.
Capabilities
From raw data to live predictions
Data engineering and ML engineering in one team, so models ship on clean, reliable data.
Data pipelines & ETL/ELT
Batch and streaming pipelines that consolidate your sources into a clean, documented warehouse or lakehouse.
Predictive models
Classification, regression and forecasting models for churn, conversion, demand, pricing and risk.
Anomaly & fraud detection
Models that learn what normal looks like and flag suspicious transactions, orders or behavior in real time.
Real-time scoring APIs
Models served behind low-latency, versioned APIs, so any system can request a score at the moment of decision.
Explainable predictions
Feature importance and per-prediction explanations that analysts, auditors and regulators can follow.
MLOps & drift monitoring
Automated retraining, data-quality checks and drift alerts that stop accuracy from decaying silently.
Use cases
Machine learning use cases
Real estate
Lead scoring & valuation
Inquiries ranked by likelihood to convert, and property prices estimated from listing features and comparable sales.
Retail & eCommerce
Demand forecasting
Demand forecast by product and location to plan stock and staffing — with confidence intervals, not a single guess.
Subscription businesses
Churn prediction
Customers at risk identified before they cancel, with the drivers behind each score, so retention teams can act in time.
Process
Our machine learning delivery process
- 01
Frame the decision
We define the decision the model supports, the metric that matters and the cost of each type of error.
Deliverable: Problem framing & success metric
- 02
Data foundation
Pipelines, quality checks and features built from your sources — the step most projects underestimate.
Deliverable: Clean training dataset
- 03
Model & validate
Baselines first, then models validated on held-out, later data against the baseline and your current process.
Deliverable: Validated model & report
- 04
Deploy & monitor
A serving API, shadow mode, then live traffic — with drift and performance monitoring from day one.
Deliverable: Production API & monitoring
Technology
Our data & ML toolkit
- Data
- PostgreSQLBigQuerydbtAirflow
- Modeling
- Pythonscikit-learnXGBoost & LightGBMPyTorch
- Serving
- FastAPIDockerMLflowAzure & Google Cloud
How much data do we need for machine learning?
Less than most teams expect, but it has to be relevant. Many useful models train on a few thousand labeled examples; for rare events like fraud, we combine historical data with anomaly detection and domain rules. A short data audit tells you what's feasible before any modeling starts.
Machine learning or business rules — which do we need?
Often both. Rules encode what you already know and are easy to audit; models catch the patterns rules miss. We usually start from a rules baseline, then show with data where a model adds measurable lift.
How do you keep a model accurate over time?
We monitor input data and prediction quality continuously, alert on drift and retrain on fresh data through an automated, versioned pipeline — with instant rollback if a new version underperforms.
Can the model's decisions be explained?
Yes. We favor interpretable models where they perform well and add per-prediction explanations to complex ones, so every score can be understood by analysts, customers and auditors.
Where does our data live during the project?
In your cloud or ours, in the region you choose, including the EU. We work on minimized and, where possible, pseudonymized data, with access limited to the people on the project.
Data & Machine Learning
Turn a recurring decision into a prediction
Tell us which decision you want to make faster or more accurately. We'll check whether your data can support a model — and what it would be worth.