Turn data into forecasts you can act on
We design, build, and operationalize predictive models that reduce lead time, improve decision quality, and stand up to audit.
What We Build
Forecasts and risk scores that drive next best actions in the workflow
Propensity and churn models to target retention and growth
Anomaly detection for fraud, quality, and operations
Capacity and demand planning to improve staffing and inventory
Uplift modeling to guide offers and interventions
We start with high value, lower risk use cases, then expand as controls mature.
Engagement Model
Orientation, one to two weeks
Define the business question, value levers, decision owners, and success metrics. Confirm risk tier and required approvals.
1
Build, thirty to ninety days
Data discovery and feature pipeline. Model selection and training. Backtesting, shadow runs, and policy alignment. Release to a target group with monitoring and audit evidence in place.
2
Scale, next ninety days
Harden and extend. Add observability and role-based access. Expand to adjacent use cases and additional groups. Publish playbooks and training.
3
Transfer, ongoing
Enable your teams to operate and enhance the solution. Establish review rhythms and light audits.
4
Data and Modeling Approach
Data readiness: lineage, quality checks, and clear owners
Features: modular feature store for reuse across cases
Models: classic ML, gradient boosting, time series, or LLM derived signals were useful
Policy to practice: thresholds, reason codes, and human oversight where the risk is material
MLOps: versioning, CI and CD for models, rollback, and reproducibility
Deliverables
Working predictive service integrated with your tools
Feature pipelines and model repository
Evaluation dashboard for accuracy, calibration, and lift
Governance pack with risk tiering, approvals, and audit trail
Enablement kit, quick references, and office hours
Quality and Risk Controls
Backtesting, out of sample validation, and stability checks
Bias and harm screens tailored to the decision
Decision capture with explanations and samples
Monitoring for drift, anomalies, cost, and latency
Evidence packs with datasets used, model configs, and change history
High Value Use Cases by Domain
Financial services: delinquency prediction, collections prioritization, fraud, and AML signals
Healthcare: no show and readmission risk, care pathway next steps, prior authorization triage
Operations: demand and capacity planning, part, and asset failure prediction
Customer and marketing: churn, next best offer, lifetime value, and segmentation
Back office: invoice exception prediction, case routing, SLA risk
Metrics We Target
Lift over baseline and business impact per decision
Lead time to first value
Objective completion and variance to plan
Cost per prediction and latency
Adoption and sustained usage by role
Technology and Platforms
We are vendor neutral and integrate with your stack. We work across major clouds, data warehouses, orchestration frameworks, and observability tools. We design for portability, so you avoid lock-in as the market evolves.
Pricing and Formats
Lighthouse build: fixed scope, single use case, thirty to ninety days
Scale package: additional use cases or new groups, adds observability and change operations
Advisory add on executive checkpoints, policy, and architecture reviews
Commercial terms can be aligned under MSA, SOW, and NDA.
FAQs
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We include data discovery, quick cleanup, and targeted connectors. We deliver value without waiting for perfect data.
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Yes. We are vendor neutral and design for portability.
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We use appropriate context screens, reason codes, and require human oversight for material decisions.
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Yes. We build inside your systems of record and collaboration tools.