Models trained on your data,
deployed in your operations.
The gap between a model in a notebook and a model in production is where most ML projects die. We build for production from day one — automated pipelines, real-time inference, monitoring, and retraining built in.
Promising on historical data. Impressive in a demo. Collects dust afterward.
- —Promising on historical data
- —Manual data prep
- —No monitoring or retraining
Automated pipelines, real-time scoring, and self-maintaining models that improve as data evolves.
- Automated data pipelines
- Deployed as a real-time service
- Monitored and retrained as data evolves
The notebook is a starting point.
We build what runs in production.
Data scientists build something promising on historical data, stakeholders get excited, and then the project stalls. Nobody planned for deployment, monitoring, retraining, or integration with the systems where decisions actually get made.
We build ML systems that ingest real-time data from your operations, generate predictions that feed into real workflows, and improve over time as new data arrives. Trained on your data, deployed in your infrastructure, measured against business outcomes.
Not proofs of concept that impress in a demo and collect dust afterward. Production systems with automated pipelines, versioned deployments, drift monitoring, and automated retraining designed in from the start.
Six model types.
One production pipeline.
Predictive Maintenance
Models that learn the normal behavior patterns of your machines — vibration signatures, temperature profiles, current draws, pressure trends — and flag deviations indicating developing failures weeks before they become emergencies. Integrated with your SCADA systems, historians, and CMMS so predictions generate work orders, not dashboard noise.
Demand Forecasting
Time-series models, gradient-boosted ensembles, and deep learning architectures selected based on your data characteristics. Accounts for seasonality, trends, promotions, and the patterns generic forecasting tools miss. Backtested rigorously with confidence intervals, integrated into your ERP and planning systems.
Anomaly Detection
Fraud detection, quality deviations, network intrusions, financial irregularities, sensor malfunctions. Systems that learn baseline behavior from historical data and flag genuine anomalies in real time without drowning your team in false positives. Feedback loops improve precision as your team responds.
Document Classification & Extraction
NLP models combined with layout analysis for invoices, contracts, compliance filings, and inspection reports. Fine-tuned on your specific document types for accuracy generic OCR tools can't match. Pushes structured data into your document management, workflow, and ERP systems for straight-through processing.
Customer & Revenue Analytics
Churn prediction, lifetime value modeling, segmentation, propensity scoring, pricing optimization, and recommendation systems. Built on your transaction history, behavioral data, and CRM records. Deployed as scoring services that run continuously and feed into your marketing automation and sales tools.
Process Optimization
Bayesian optimization, reinforcement learning, and simulation-based approaches for manufacturing parameters, supply chain routing, resource allocation, and energy consumption. Models that recommend optimal settings to operators or adjust parameters autonomously within defined safety bounds.
Real workflows,
real outcomes.
Each of these is a distinct problem with distinct data requirements, validation approaches, and integration points. The common thread: predictions that feed real decisions, not dashboards.
Learn the normal behavior of each asset in your fleet and alert your maintenance team when something shifts. Integrated with your historian and CMMS to generate work orders with enough lead time to plan repairs during scheduled downtime rather than scrambling after an unplanned shutdown.
Inaccurate demand forecasts cascade through your supply chain, manufacturing schedule, and financial planning. Models that account for your specific seasonality, promotions, and external factors feed directly into ERP and inventory systems — replacing the spreadsheets your planning team is currently trusting.
The challenge isn't flagging anomalies — it's flagging the right ones. Systems tuned to your baseline with contextual filtering distinguish genuine outliers from normal variation. Feedback loops improve precision as your team corrects the system over time.
Invoices, contracts, inspection reports, compliance filings. Models fine-tuned on your document types extract key fields, validate against business rules, and push structured data into your systems of record — replacing manual keying and routing.
Scoring services built on your own transaction history that run continuously: churn risk, conversion propensity, pricing gaps, next-best offer. Feed directly into your marketing automation, sales tools, and customer success workflows instead of sitting in a BI report nobody opens.
Any operational process with measurable inputs and outcomes can be optimized. Models identify the parameter combinations that maximize yield, minimize cost, reduce waste, or improve throughput — recommending settings to operators or adjusting autonomously within safety bounds.
From problem definition
to live model.
Define the problem precisely.
"We want to use ML" is not a problem statement. "We want to predict bearing failure on our CNC fleet 14+ days in advance with a false positive rate under 5%" is. We work with your team to define the prediction target, required accuracy, acceptable error rates, and the business decision the prediction feeds into. If the problem isn't well-defined, the model won't be either.
Assess and prepare the data.
We audit your data for volume, quality, completeness, and relevance to the prediction task. Missing values, labeling inconsistencies, sensor drift, class imbalance, and temporal leakage are identified before modeling begins. Data preparation consumes 60-70% of project effort. We treat it as engineering, not overhead.
Develop and validate models.
Systematic experimentation across model architectures, feature engineering approaches, and hyperparameter configurations. We evaluate multiple approaches and benchmark them against each other on held-out data the model has never seen. Validation simulates production conditions, not optimistic random splits.
Deploy to production.
Models run as services: APIs, streaming processors, or batch jobs integrated into your operational systems. Containerized, version-controlled, and deployed with the same rigor as production software. Architecture designed for your latency requirements, throughput needs, and infrastructure constraints.
Monitor and retrain.
Models degrade. Data distributions shift. Equipment gets replaced. We deploy monitoring that tracks prediction accuracy, data drift, feature distributions, and model performance continuously. Automated alerts fire when performance drops. Retraining pipelines rebuild models on fresh data on a defined schedule or triggered by drift detection.
The stack we reach for.
Tools earn their place by being the right fit for the system, not by being fashionable. We select per-project based on requirements, latency, cost, and your existing infrastructure.
Production engineering,
not research theater.
Production-first engineering.
Every model we build is designed for deployment from the start. Data pipelines are automated, not manual. Inference is served via APIs, not notebooks. Monitoring is built in, not bolted on. The model is a component in a production system, not a standalone experiment.
Domain expertise that matters.
ML models don't exist in a vacuum. A predictive maintenance model for a CNC machine requires understanding of machining operations. A demand forecast for a seasonal retailer requires understanding of promotional dynamics. Our engineers have built models in manufacturing, energy, finance, healthcare, and logistics.
Honest about what ML can and can't do.
Sometimes the data isn't sufficient. Sometimes a simple rule-based system outperforms a complex model. Sometimes the business process needs to change before ML can add value. We tell you when ML is the right tool and when it isn't, before you invest in building something that won't deliver.
End-to-end ownership.
Data engineering, feature development, model training, deployment, integration, monitoring, and retraining. One team owns the entire lifecycle. No handoffs between a data science team that builds models and an engineering team that deploys them. That gap is where most ML projects fail.
Straight answers.
It depends on the problem. Tabular classification and regression models can work well with thousands of labeled examples. Time-series anomaly detection often needs 3-6 months of operational data to establish baseline patterns. Deep learning approaches typically require more data than traditional ML. We assess data sufficiency during the scoping phase and tell you honestly if you have enough — or what you need to collect before starting.
We define accuracy targets during scoping based on your business requirements and validate against held-out data. A predictive maintenance model might target 85%+ recall with under 5% false positive rate. A demand forecasting model might target MAPE under 15%. We report performance transparently, including where the model struggles, and set expectations based on your data quality and problem complexity.
Yes. We build on whatever you have: SQL databases, cloud data warehouses, on-premise historians, CSV exports, API endpoints. If your infrastructure has gaps that limit ML effectiveness, we'll flag them and can help address them. We don't require you to migrate to a specific platform as a prerequisite.
Monitoring catches it. Automated drift detection identifies when input data distributions shift or prediction accuracy drops. Depending on the severity, the system triggers an alert, initiates automated retraining, or falls back to a previous model version. We design the degradation response during architecture, not after the model is already performing poorly.
Focused single-model projects (one prediction target, clean data, clear integration point): 6-10 weeks. Multi-model systems with data engineering prerequisites: 10-16 weeks. Ongoing ML programs with multiple models and continuous improvement are structured as retainer engagements with regular delivery milestones.
Both, depending on what delivers the best result. For common tasks like document classification or sentiment analysis, fine-tuning pre-trained models is often the fastest path to production accuracy. For domain-specific predictions like equipment failure or process optimization, custom models trained on your data are usually necessary. We choose the approach that maximizes performance for your budget and timeline.
Put your data to work.
If you're sitting on operational data that could predict failures, forecast demand, or optimize processes — but isn't — tell us about it. We'll scope what's realistic.