AI, Data & Emerging Technology · Machine learning
Machine learning models trained on your own data
We build machine learning models that classify, score, rank and detect using the data your business already collects, then deploy them behind APIs with the monitoring needed to keep predictions accurate as conditions change.
- Problem framing
- Feature engineering
- Right-sized algorithms
Overview
From a business question to a model in production
Machine learning finds patterns in historical data and uses them to predict, rank or classify new cases: which leads will convert, which transactions look unusual, which parts will fail, which customers need attention. The algorithm matters less than the framing. A well-defined target, honest training data and a decision that actually changes based on the prediction are what make a model worth having.
A common trade-off is accuracy against explainability. Gradient-boosted trees on tabular data are often both accurate and interpretable, while deep networks shine on images, audio and text but are harder to explain. We also weigh batch scoring against real-time prediction, since real-time adds infrastructure that many use cases never need. For many business uses, a nightly batch score is all that is required.
A good model beats a simple baseline, is monitored for drift, and comes with a retraining plan your team can run. It also has a clear owner, a documented list of what it should not be used for, and an agreed point at which falling accuracy means it gets retrained or retired.
Who it’s for
Built for teams like yours
- 01
Data-rich mid-size companies
Businesses with years of transaction, customer or operations data in a database or warehouse that have never turned it into predictions people can act on.
- 02
Operations and risk managers
Teams responsible for fraud review, quality control, maintenance or credit decisions who want to prioritize their attention using scores rather than fixed rules alone. Scores come with the reasons behind them.
- 03
Product teams adding intelligence
Software companies that want recommendations, ranking or anomaly alerts inside their product, built on their own usage data and served through a reliable API. We build for your existing stack.
Why it matters
Models that earn their place
Many problems do not need a large language model. Approving applications, flagging fraud, routing tickets or recommending products are often handled better by a smaller model trained on your own history: cheaper to run, faster to answer and easier to explain. The work is in framing the problem, preparing the data and proving the model beats the current approach.
We start with a baseline, measure against it honestly, and only ship a model when it clearly outperforms the rules or manual process it replaces, on data it has never seen before.
Every engagement includes
- Data audita review of what data exists, its quality and whether it can support the goal.
- Baseline modela simple benchmark that every later model has to beat to justify itself.
- Model developmentexperiments tracked and compared so the chosen model is defensible.
- Deploymentthe model served as a batch job or real-time API inside your infrastructure.
- Monitoring & retraining plandashboards, alerts and a documented path for refreshing the model.
- Handovercode, notebooks, model artifacts and a written model card describing limits and use.
Features
From data to deployed model
- 01
Problem framing
We translate a business question into a measurable prediction target with clear success criteria.
- 02
Feature engineering
Signals built from transactions, events and text that give the model something meaningful to learn.
- 03
Right-sized algorithms
Gradient boosting, linear models or neural networks chosen for accuracy, speed and explainability needs.
- 04
Explainable predictions
Feature importance and per-prediction explanations so staff and auditors understand each decision.
- 05
MLOps pipelines
Reproducible training, model registry and automated deployment using tools like MLflow and SageMaker.
- 06
Drift monitoring
Alerts when incoming data or accuracy shifts, with a defined process for retraining the model.
In practice
Typical machine learning applications
Lead and opportunity scoring
Each new lead or deal receives a score based on how similar past records turned out, so sales teams work the most promising ones first and marketing sees which sources produce them.
Anomaly and fraud flags
Transactions, claims or log events are scored for how unusual they look against normal patterns, sending the highest-risk cases to a reviewer instead of checking everything by hand. Reviewer decisions feed back into future training.
Predicting equipment maintenance needs
Sensor readings and service history are used to estimate when equipment is likely to need attention, so maintenance can be scheduled before a breakdown interrupts production. Planned work replaces emergency repairs where the signals allow it.
Personalized product recommendations
Customers see items or content ranked by what similar customers engaged with, using your catalog and behavior data, with business rules applied for stock and margin. Results are compared against your current merchandising approach.
Process
How we work
- 1
Target definition
We pin down exactly what the model predicts, over what time window, and what action follows, then check that the historical data actually records that outcome reliably. Vague targets are fixed here, not later.
- 2
Data and features
Raw tables are joined, cleaned and turned into features, with careful checks for leakage so the model never learns from information it would not have at prediction time. Feature definitions are documented for reuse.
- 3
Baseline then models
A simple rule or linear model sets the bar, then tracked experiments compare stronger algorithms, keeping only the ones that clearly beat the baseline on held-out data. Every experiment is logged with its settings and results.
- 4
Serving pipeline
The chosen model is packaged with its preprocessing, versioned in a registry and deployed as a scheduled batch job or an API, depending on how the predictions are used. Rollbacks to the previous version take one step.
- 5
Drift watch
Input distributions and prediction quality are monitored over time, with alerts when they shift, and retraining is scripted so it can be repeated without guesswork. Your team receives a written guide to each alert and the response it calls for.
Deliverables
What you receive
- Problem statement and success criteria
- Feature pipeline with leakage checks
- Baseline and final model comparison
- Model card with limits and explanations
- Deployed batch job or prediction API
- Drift and performance monitoring dashboard
- Scripted retraining pipeline
Tools & methods
Modeling
- Python
- scikit-learn
- XGBoost
- LightGBM
- PyTorch
- SHAP
Data
- SQL
- pandas
- dbt
- Snowflake
- BigQuery
MLOps
- MLflow
- Docker
- Airflow
- AWS SageMaker
- Evidently
FAQ
Frequently asked questions
Anything else about Machine learning? Ask us directly.
It depends on the problem. A churn or lead-scoring model can work with a few thousand labeled records, while image or language tasks often need more unless we start from a pre-trained model. The data audit tells you honestly whether you have enough, and what to start collecting if you do not.
Let’s work together
Have a project in mind?
Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.