We build production-ready ML systems: forecasting, anomaly detection, recommendations, and decision automation. We deploy monitoring, drift detection, and retraining so performance doesn't decay after launch.
Most teams don't fail at ML because they lack ideas. They fail because the work stops at a model file—no data reliability, no deployment path, no monitoring, no ownership.
Each option below is a proven, high-demand applied ML category.
Forecast demand, revenue, churn, risk, or capacity using historical data (with confidence bands).
You get:
Trained model + confidence scoring + feature signals + retraining-ready pipeline.
Auto-classify and route work (fraud risk, leads, documents, tickets, quality checks) with clear thresholds.
You get:
Real-time inference API + confidence thresholds + human-review escalation + performance dashboard.
Recommend next-best actions, content, products, or workflows to increase adoption and conversion.
You get:
Recommendation engine + A/B test hooks + lift tracking (CTR/CR) + explainability options.
Detect unusual patterns early (fraud spikes, ops issues, data quality drops, process deviations).
You get:
Anomaly scoring + alert thresholds + investigation flow + false-positive tuning + drift monitoring.
Extract structured insights from images/video (defects, OCR, matching, inspection, visual search).
You get:
Vision model + inference endpoint + accuracy metrics + workflow integration + iteration plan.
Classify, extract, and summarize text from tickets, contracts, reviews, emails, and internal docs.
You get:
NLP pipeline + extraction/classification APIs + entity/sentiment scoring + quality evaluation.
Model time-based signals with seasonality and spikes (sales, IoT sensors, traffic, energy usage).
You get:
Time series model + forecast intervals + trend breakdown + anomaly detection on time-series.
Find natural segments in customers, behavior, risk, or products, then make them actionable.
You get:
Segment profiles + dashboards + scoring for new users/items + usable segment rules.
We stay tool-agnostic. What matters is the capability stack your ML system needs to work in production.
Training + Inference — model development, evaluation, and deployable inference services
Batch + Real-Time Scoring — scheduled scoring jobs and low-latency APIs where needed
Monitoring + Drift + Retraining — performance tracking, drift alerts, and retraining triggers
Data Quality + Feature Pipelines — reliable datasets, repeatable transformations, feature consistency
Governance + Auditability — access control, versioning, decision traceability, and rollback paths
Every engagement produces artifacts your team can immediately use
Trained on your data and validated for accuracy
For automated feature engineering and model training
With APIs, monitoring, and automated retraining
Showing model accuracy, business impact, and drift
Covering model architecture, features, and maintenance procedures
For continuous model improvement and retraining
Training your team to maintain and extend models
For model performance, troubleshooting, and optimization
Machine learning only matters when it moves a metric you can track and improves how decisions get made. This is what we optimize for.
Not open-ended ML work.
We don’t “build a small app and hope.” We offer a repeatable way to get a real
product into users’ hands fast without creating a fragile V1 you’ll regret.
Output: ML opportunity assessment + recommended pilot + success criteria + data requirements
Output: clean datasets + feature spec + pipeline code + data quality report
Output: trained models + benchmarks + feature importance + selection rationale
Output: pilot system + dashboard + A/B results + impact analysis
Output: production ML system + monitoring + retraining automation + runbook
Output: performance reports + model updates + expansion roadmap
Business outcomes over "model accuracy" — we optimize for decision quality and KPI lift
Pilot-first delivery — prove value before scaling spend
Production-grade MLOps — monitoring, drift detection, retraining, rollback
Explainability when it matters — thresholds, feature signals, and audit-friendly outputs
No tool lock-in — choose platforms based on your constraints, not our preferences
Team enablement — docs, runbooks, and knowledge transfer built-in
Answers to the most common pre-engagement questions.
ML is a good fit when you have historical data (inputs + outcomes) and repeatable decisions where patterns can improve results. We run a Week 1 feasibility check to confirm ROI and define the success metric.