Opportunity & readiness
Clarify the decision, inspect the workflow and data, test feasibility, and leave with a prioritized path.
Assess the opportunity →Senior data science + AI consulting
Turn an important data problem into a decision your team can use.
Named senior professionals help organizations define the right problem, test it against evidence, and build a practical path to implementation—through a focused project, fractional capacity, or embedded support.
Start with the decision
The model is not the product.
The useful product is a better forecast, a safer release, a faster knowledge workflow, or a documented system your team can operate.
We begin with the business decision, baseline, available data, operating constraints, and ownership plan. If a simpler method is the better answer, the work should say so.
Core services
A compact path from uncertainty to working evidence.
Forecasting & predictive analytics
Build and evaluate decision support for demand, operations, customers, revenue, risk, or inventory.
Explore forecasting work →AI evaluation & governance
Define tests, baselines, failure modes, human controls, monitoring, and release evidence for AI systems.
Review an AI system →Knowledge & workflow systems
Design evaluated document, retrieval, search, and bounded automation workflows around source evidence and human ownership.
Explore knowledge workflows →AI/ML implementation
Plan the path from validated evidence to integration, monitoring, documentation, adoption, and client ownership.
Plan an implementation →Flexible by design
Buy the engagement shape that fits the decision.
A defined outcome may need a focused project. An evolving roadmap may need fractional leadership and execution. A team with a temporary capability gap may need embedded support.
Use the engagement guideProjectDefined question, scope, and handoff
FractionalRecurring senior capacity without a full-time hire
EmbeddedHands-on contribution inside an existing team
Local context that changes the work
Four markets. Four different decision environments.
TXHoustonOperations, forecasting, industrial and logistics decisions
CASacramentoResponsible AI, evaluation, traceability and program analytics
UTLindon + Utah CountyFractional data capacity, product, revenue and AI-feature evaluation
ARBentonville + NWASupplier, retail, inventory and recurring decision analytics
Working standard
Evidence before enthusiasm.
- DefineOutcome, owner, baseline, constraints
- TestData, method, failure modes, alternatives
- ImplementWorkflow, integration, controls, adoption
- Hand offDocumentation, monitoring, ownership
Open working methods
Inspect the reasoning before discussing the engagement.
Forecasting baseline lab
Compare simple forecasting baselines on transparent synthetic series and see why the benchmark must come before model complexity.
Run the benchmark →Knowledge-system evaluation
Translate “good answers” into testable retrieval, grounding, abstention, operations, and human-review criteria.
Inspect the framework →Project, fractional, or hire?
Compare ownership, continuity, uncertainty, management, and knowledge-transfer tradeoffs before choosing capacity.
Read the decision guide →Have a consequential data decision?
Bring the problem before choosing the technology.
Use the project brief to organize the outcome, available evidence, constraints, and preferred way of working.
Prepare a project brief