Machine Learning Development Services Models that run in production, not notebooks.
Production machine learning for prediction, classification, recommendation, forecasting, and anomaly detection. Custom models, MLOps, feature stores, drift monitoring. Shipped in 10 to 18 weeks. USD pricing.
We tell you whether your problem needs ML or whether a rules engine plus LLM call would work better.
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Who we've built for.
How we work on machine learning
- What we build
- Prediction · Classification · Recommendation · Forecasting · Anomaly detection · Risk scoring · Churn models
- Stack
- Python · scikit-learn · PyTorch · LightGBM · XGBoost · MLflow · Feast · SageMaker · Vertex AI · Databricks
- MLOps
- Feature store · model registry · CI/CD for models · drift monitoring · shadow deploys · A/B testing
- Integrations
- Snowflake · BigQuery · Databricks · Redshift · dbt · Airflow · Prefect · Salesforce · HubSpot
- Pricing in USD
- ML pilot from $7,000 · Production ML system from $21,000 · Custom ML platform from $35,000
- Output
- Production model · feature pipeline · drift monitoring · retraining cadence · runbook · on-call coverage
Most ML projects fail at productionisation, not modelling. A model that scores 0.92 AUC on a notebook is useless if features are missing in production, the model drifts after eight weeks, or the inference API can't keep up with traffic. We build ML systems that actually run in production: feature pipelines, model registry, drift monitoring, retraining cadence, and observability baked in.
Related builds
Production ML and prediction systems in live environments:
What we build
Prediction and classification
Lead scoring, fraud detection, credit risk, churn prediction, propensity-to-buy. Trained on your historical data, deployed as a real-time API or batch scoring.
Recommendation
Product recommendations, content recommendations, next-best-action. Collaborative filtering, content-based, hybrid. Built on TensorFlow Recommenders, LightFM, or AWS Personalize.
Forecasting
Demand forecasting, capacity planning, financial forecasting. Prophet, statsforecast, NeuralProphet, or custom deep-learning forecasters. Hierarchical reconciliation where needed.
Anomaly detection
Fraud, intrusion, equipment failure, transaction monitoring. Isolation forest, autoencoder, time-series anomaly. Threshold tuning per false-positive budget.
Risk scoring with explainability
Credit risk, underwriting, claim severity. SHAP-based explainability. Adverse-action notice generation for regulated US workloads.
Related AI capabilities: AI & machine learning, AI-powered software, Computer vision, NLP development, AI consultation, Data pipeline engineering, Custom software development.
Use cases with cost ranges
Lead scoring for B2B SaaS
Train on historical CRM data. Feature engineering. LightGBM model. Real-time API integrated with Salesforce or HubSpot. SHAP explanations for sales team. Drift monitoring with retraining cadence. Typical build 10 to 14 weeks. Range $14,000 to $28,000 depending on data volume and feature complexity.
Demand forecasting for retail
Multi-product, multi-location forecasting. Prophet plus LightGBM ensemble. Hierarchical reconciliation. Integration with planning system. Weekly retraining. Typical build 12 to 16 weeks. Range $21,000 to $35,000 depending on SKU count and forecast horizon.
Fraud detection for fintech
Real-time fraud scoring on transactions. Isolation forest plus supervised classifier. Feature store with rolling-window features. Decision API with sub-100 ms latency. Integration with alert and case management. Typical build 14 to 18 weeks. Range $28,000 to $38,000 depending on transaction volume and feature complexity.
Recommendation engine for content or commerce
Two-tower deep retrieval model on TensorFlow Recommenders. Re-ranking layer with business rules. A/B testing infrastructure. Cold-start handling. Typical build 12 to 16 weeks. Range $21,000 to $35,000 depending on catalog size and personalisation depth.
How we run the build
Five-phase rhythm for ML builds. Productionisation runs in parallel with modelling.
- Discovery and data audit (2 weeks). Problem framing. Data audit. Baseline metric. Eval framework. Output: project brief plus baseline benchmark.
- Feature engineering and modelling (3 to 5 weeks). Feature store schema. Baseline model. Iteration with eval framework. Hyperparameter tuning.
- Productionisation (3 to 5 weeks). Inference API. Feature pipeline. Model registry. Monitoring. Shadow deploy.
- UAT and shadow validation (1 to 2 weeks). Side-by-side production validation. Business stakeholder sign-off. Drift baseline established.
- Launch and dual on-call (1 week plus 2 weeks). Production deploy. Monitoring. Retraining cadence configured. Runbook delivered.
Tech stack
- Modelling: scikit-learn, LightGBM, XGBoost for tabular. PyTorch or TensorFlow for deep learning. Hugging Face transformers for NLP and vision tasks.
- Feature store: Feast or Tecton for feature reuse and consistency between training and serving. PostgreSQL or BigQuery as backing store.
- Training infrastructure: SageMaker, Vertex AI, Databricks, or self-hosted on Kubernetes plus Kubeflow. Spot instances and managed training for cost efficiency.
- Model registry: MLflow Model Registry or SageMaker Model Registry. Every model versioned. Promotion via approval workflow.
- Serving: Real-time via FastAPI plus ONNX or TorchServe. Batch via Airflow or Prefect. Edge deployment via TFLite or ONNX Runtime.
- Monitoring: Evidently AI or WhyLabs for drift monitoring. Datadog or Grafana for system metrics. Custom dashboards for business KPI monitoring.
- Experimentation: Shadow deploys for risk-free production validation. A/B testing via Statsig, GrowthBook, or custom for online metrics.
Pricing
ML pilot
From $7,000
- Data audit plus baseline model plus single-batch scoring.
- 4 to 8 weeks. Validates ML investment before productionisation.
Production ML system
From $21,000
- Trained model, feature pipeline, inference API, drift monitoring, retraining cadence.
- 10 to 14 weeks.
Recommendation engine
From $21,000
- Two-tower retrieval plus re-ranking with A/B framework.
- 12 to 16 weeks.
Forecasting platform
From $21,000
- Multi-product forecasting with reconciliation and planning integration.
- 12 to 16 weeks.
Custom ML platform
From $35,000
- Multi-model platform with shared feature store, model registry, and experimentation.
- 14 to 20 weeks.
FAQ
Rules engines win when the logic is stable, explainable, and the data volume is low. ML wins when the logic is too complex to encode, the data volume is high, or the relationships shift over time. We assess both at the discovery phase and recommend the lower-cost option that meets the business need.