Machine Learning
We design and deploy machine learning solutions tailored to your data. From training custom models on your proprietary datasets to deploying inference APIs in production — regression, classification, clustering, NLP, time-series forecasting. We handle the full ML lifecycle: data prep, feature engineering, training, evaluation, and serving.
Ciclo completo de ML
Built for production.
Machine learning only pays off when predictions connect to a decision someone makes every day — reorder stock, call a lead, freeze an account. We start from that decision and work backward to features, labels, and deployment.
We have shipped forecasting, scoring, ranking, and NLP systems across retail, finance, and operations teams. The through-line is disciplined experimentation: leakage checks, holdout strategies that match reality, and serving paths that do not require a PhD to operate.
If you already have data in a warehouse or events in a stream, we meet you there. If not, we help you instrument and collect what the model will need before training begins.
Use cases, in production.
Predictive models, classification pipelines, and ML systems that turn your data into actionable intelligence.
Demand & Sales Forecasting
Time-series models that predict future demand, revenue, or inventory needs. Feed historical data in, get accurate forecasts out — ready to plug into your planning or supply chain workflows.
Churn & Lead Scoring
Classification models that rank customers by churn risk or leads by conversion probability. Prioritize where your team focuses, backed by real behavioral signals.
Anomaly Detection
Detect fraud, equipment failure, or unusual patterns in operational data in real time. Rule-based systems miss edge cases — ML finds what humans overlook.
NLP & Text Classification
Train models to classify, cluster, or extract structured data from unstructured text — product reviews, support tickets, legal documents, or social media feeds.
Recommendation & ranking
Personalize feeds, search results, or next-best-offer using behavioral signals — with offline evaluation and safe online rollouts.
Computer vision QA
Detect defects, label assets, or verify packaging on production lines with models tuned for your lighting and hardware constraints.
From discovery to handoff.
A clear path with milestones you can plan around — no black box, no surprise scope at the end.
Problem framing
Translate the business question into a measurable ML task with baselines — often simpler than you expect.
Data & features
Audit labels, leakage, and freshness. Build feature pipelines that match training and serving.
Train & validate
Compare models against baselines with metrics tied to dollars or risk, not just accuracy.
Deploy & monitor
APIs, batch scores, or embedded inference with drift detection and retrain triggers.
What we ship.
What you receive.
Tangible outputs at the end of every engagement — code, docs, and systems your team can operate.
- Experiment report with model comparison
- Feature pipeline code & feature store hooks
- Trained model artifacts & versioning
- Inference API or batch scoring job
- Monitoring for drift & performance
- Documentation for retraining
Common questions.
How much data do we need?
It varies by task. Classification with clear labels can start in the thousands of rows; forecasting often needs longer history. We tell you honestly during discovery.
Will we depend on you to retrain?
We document pipelines so your team can retrain, or we offer retained MLOps support if you prefer us on the hook.
Can you explain model decisions?
We add explainability where regulations or ops require it — SHAP, feature importance, or rule overlays for high-stakes calls.
Explore the stack.
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