Production-grade ML that
improves decisions,
reduces risk.
Most organizations have data and AI ideas — few have ML systems that run reliably and generate measurable value. We build end-to-end AI/ML solutions: from problem framing and feature engineering through model deployment, monitoring, and adoption.
Three ML problems we solve
before the first line of code.
98% of ML projects never reach production. The cause is almost never the algorithm — it's the absence of MLOps, data pipelines that weren't built for inference, and models that were validated on the wrong cohort. We build for deployment from day one.
A model with 94% accuracy is worthless if users don't understand what it's telling them or why. We design the decision UX, explainability layer, and adoption program that turns a model output into a workflow action — with human override and feedback capture built in.
Deployed models degrade silently. Without drift monitoring, a model that scored 91% on training day may be running at 74% accuracy six months later — with no alert, no audit trail, and no retraining trigger. We implement model governance proportional to decision risk.
AI/ML systems that fit your workflow —
not just a model file.
End-to-end delivery: problem framing, feature engineering, training pipelines, deployment, monitoring, and the decision interfaces your teams actually use.
Build classification and regression models that score churn, propensity, risk, and next-best-action — validated against business decisions, not just held-out test sets.
- Churn, propensity, and risk scoring
- Utilization and capacity prediction
- Claims and denial likelihood
- Next-best-action recommendations
Deploy operational and financial forecasting systems with explainable drivers, confidence intervals, and scenario analysis — so planners can act on uncertainty, not just point estimates.
- Demand planning and inventory optimization
- Call volume and staffing forecasts
- Revenue, spend, and cash forecasting
- Seasonality and event impact modeling
Identify unusual behavior, prioritize investigations, and surface explainable evidence — calibrated to operational thresholds so analysts receive actionable alerts, not noise.
- Outlier detection and alert triage
- Network and entity risk scoring
- Behavioral drift and threshold tuning
- Explainable anomaly evidence and reason codes
Combine ML predictions with optimization and business rules to produce decisions — not just scores. Scheduling, routing, allocation, and inventory policies that respect real-world constraints.
- Scheduling, routing, and resource allocation
- Inventory and replenishment policies
- Workforce planning optimization
- Scenario simulation and sensitivity analysis
Classify documents, extract entities, route work intelligently, and surface structured intelligence from contracts, clinical notes, policies, and correspondence — without manual review at scale.
- Document classification and tagging
- Named entity extraction and matching
- Summarization for casework and compliance
- Policy and clause detection
Scores are only useful when embedded in the workflow that drives action. We build the API services, work queues, dashboards, and human override interfaces that close the loop from prediction to decision.
- REST/batch API inference services
- Work queues with priority scoring
- Power BI and app-embedded predictions
- Human override and feedback capture
A repeatable pathway
to reliable ML outcomes.
We don't start with a model. We start with the decision — then engineer everything backward from there.
- Define the decision, the users, and the workflow
- Set measurable KPIs and operational thresholds
- Determine oversight, risk tier, and governance needs
- Align on "good enough" before writing code
- Data readiness assessment and gap analysis
- Feature engineering and candidate catalog
- Label strategy and ground truth definition
- Train/test split design and leakage prevention
- Model CI/CD and environment controls
- Drift monitoring, performance tracking, alerting
- Human feedback capture and retraining triggers
- Governance runbooks and lifecycle documentation
Six disciplines. Every engagement
draws on the right combination.
Implementation-grade artifacts
for real ML deployment.
Common deliverables across AI/ML engagements — tailored to your timeline, risk level, and regulatory environment. We build for handover, not dependency.
AI that lasts requires
engineering discipline.
Engagements where Data Products moved organizations from AI aspiration to production-grade ML with measurable outcomes.
A life sciences organization needed to operationalize AI across their analytics portfolio. We delivered the governance framework, AI prioritization model, and phased implementation roadmap that moved them from scattered prototypes to a structured, governed ML program with validated use-case sequencing and ROI milestones.
Built a governed analytics and prediction layer on top of UKG ERP, replacing manual HR reporting with ML-informed workforce planning — including utilization forecasting and anomaly detection for compensation patterns.
ML adoption is a human problem as much as a technical one. This engagement addressed the organizational side — building the AI literacy programs that made clinical and operational teams confident users of ML-driven tools.
Select the ML outcome
you need most.
Pick the use case closest to your immediate need. We scope each engagement specifically — from a 1–2 week discovery sprint to a full ML program.
Starting with: Forecasting & demand planning
Book a sessionQuestions we answer
before the first call.
What's the fastest way to know if our ML use case is feasible?
How do you avoid models that look good in testing but fail in production?
Do you build only the model or the full ML system?
Can you support regulated industries like healthcare and financial services?
What does ROI measurement look like for ML?
What industries do you serve with AI and machine learning?
Chicago-based · National reach · Senior-led delivery
Bring the decision
you want to improve.
We'll respond with a recommended approach, data requirements, and a realistic implementation path — whether that's a 1-week discovery sprint or a full ML program.
Book a free ML consult