Decision & risk modeling
Simulation and optimization tools for decisions with uncertainty: pricing, hedging, cost exposure, portfolio tradeoffs, capacity planning, and scenario analysis.
Established data science and analytics partner
Decision intelligence for complex business questions.
DynaSolve builds causal measurement, forecasting, risk models, AI-enabled analytics systems, and modern data products for organizations that need to turn messy data into confident decisions.
2009founded to bring enterprise-grade tools to growing businesses
4core lanes: strategy, modeling, data products, implementation
0→1prototype, validate, and ship decision systems
Dallasserving clients remotely and across the U.S.
Focused services for data-heavy decisions.
Most decisions are causal questions: what will actually change if we act? DynaSolve is intentionally narrower than a full-service agency — the emphasis is on work where analytical depth, business context, and production engineering meet.
Forecasting & Bayesian analytics
Models that represent uncertainty honestly and translate sparse, messy, or incomplete data into usable forecasts and probability-weighted outcomes.
Causal inference & experimentation
Measure what actually drives results, not just what correlates. Experiments, holdouts, and quasi-experimental methods to estimate true impact — with marketing mix modeling, attribution, and incrementality as the marketing case.
Data products & dashboards
Internal tools that make analysis repeatable: decision dashboards, executive reporting, model interfaces, and lightweight applications for teams.
Modern data infrastructure
Pragmatic pipelines and data foundations that support analytics work without turning every project into a platform rewrite.
AI strategy & agentic workflows
Use AI where it changes throughput or quality: research workflows, analytics assistants, document intelligence, software acceleration, and operating-model design.
Models are only valuable when they change a decision.
Representative work.
These are the kinds of systems DynaSolve is built to deliver: narrow enough to be useful, rigorous enough to trust, and practical enough to run.
Energy risk and hedge optimization
Decision tools for comparing fixed and indexed energy exposure, modeling usage and rate uncertainty, and communicating downside risk with clear metrics.
Incrementality & causal measurement
Experiments, holdouts, and Bayesian models that separate true incremental impact from correlation — connecting marketing and operational spend to outcomes you can act on.
Operational analytics applications
Custom dashboards and data apps for finance, inventory, executive reporting, and recurring analysis that needs to move beyond spreadsheets.
Machine learning and scoring engines
Predictive systems for credit risk, customer behavior, pricing, demand, and operational signals with transparent validation and deployment plans.
How the work moves.
A small, senior-led engagement model keeps the work focused. The output is not just analysis; it is a usable decision asset.
Step 01
Frame the decision
Define the business question, the action being supported, the available data, and the cost of being wrong.
Step 02
Build the analytical core
Develop the model, pipeline, dashboard, or workflow in a way that is testable, explainable, and appropriate for the decision.
Step 03
Operationalize the result
Package the work so stakeholders can use it: documentation, validation, a clear interface, and a path to maintenance.
About DynaSolve.
DynaSolve was established in 2009 to help growing businesses use the kinds of analytical tools that were once available only to large enterprises.
Today, the focus is slimmer and sharper: applied data science, AI-enabled workflows, risk modeling, forecasting, marketing analytics, and decision systems that help teams act with more clarity.
Founder-ledSenior technical strategy, modeling, and implementation from Jay Baker.
Business-firstStart with the decision, then choose the model, metric, or system.
Production-mindedBuild assets that can be rerun, audited, maintained, and extended.
Analytically rigorousUse uncertainty, validation, and clear tradeoffs instead of false precision.
- ✓Deep modeling plus practical engineering. Statistical rigor matters, but so do APIs, data pipelines, deployment, and maintainability.
- ✓Plain-English decision support. The deliverable should explain what changed, why it matters, and what to do next.
- ✓Useful scale. Build only the infrastructure needed to make the decision system reliable and repeatable.
- ✓Modern AI without the hype. Use AI to improve research, analysis, and software throughput where it has a measurable job to do.
Have a data-heavy decision that needs a sharper answer?
Send a short note about the decision, the data you have, and what would change if the analysis worked.
Email: info@dynasolve.com
Location: Dallas–Plano, Texas