Data science &
predictive analytics
Turn historical operations, weather and business data into forecasts, early-warning rules and reports that help teams anticipate change.
Applied AI, scientific computing and enterprise data systems. We connect research with engineering to help organizations forecast, optimize and act.
AI & digital transformation for PetroChina Explore oil & gas digital and geoscience projects.
Explore the focusA background in computer science and applied mathematics, with experience in predictive modeling, optimization and AI systems.
MicroMind Technologies LLC is an applied AI and scientific computing company registered in New Mexico, USA. Our focus spans energy intelligence, public-sector AI and cloud data services.
Three connected disciplines bring scientific research, algorithm development and engineering delivery into one practical approach.
Turn historical operations, weather and business data into forecasts, early-warning rules and reports that help teams anticipate change.
Connect AI models, business workflows and cloud databases in data systems designed to support enterprise operating decisions.
Model capacity, demand and operating constraints to compare energy, resource and traffic plans under real business conditions.
A useful model starts with context: energy supply and demand, a coordinated transport network, or the data behind an enterprise.

Combine power-load and renewable-output forecasting with storage and production planning. Compare operating choices against capacity, demand and business constraints.

Support traffic operations through data analysis, signal-timing models and corridor coordination. Evaluate scenarios using delay, queues and travel-time measures.

Build enterprise data foundations through database migration, performance tuning and operational support. Connect those systems with AI models and business workflows.
MicroMind subcontracting experience spans Washington State traffic-signal optimization and AWS Middle East database-repair work associated with Amazon’s Seattle headquarters.

MicroMind undertook part of the subcontracted technical work for a University of Washington-led traffic-signal optimization project in Greenwood, involving traffic-data analysis, AI algorithms and signal timing.

MicroMind undertook part of the outsourced AWS Middle East database-repair work associated with Amazon’s Seattle headquarters, covering data repair, anomalous-data handling and technical support.
Team members' PetroChina project experience spans engineering-data extraction, fracturing-program generation, procurement and supply decisions, and Changqing Oilfield geoscience research.
An engineering-design data extraction platform supporting the extraction and organization of information from oil & gas design documents.
An automatic pumping-program generation system for hydraulic-fracturing operations, bringing program preparation into a digital engineering workflow.
An intelligent fracturing-proppant supply decision system under the Basel III framework, focused on supply and decision support in oil & gas engineering.
Procurement-modeling software and a green TCOe decision system for fracturing power supply, supporting procurement and power-supply decisions.
An automatic static/flowing-pressure report-generation module supporting oil & gas engineering data workflows and report preparation.
Four research engagements covering shale-oil reservoir characteristics, prospective-area prediction, accumulation patterns and integrated gas-reservoir modeling.
Presented as team members' project experience. Project titles and scope are drawn from the supplied professional record.
Discuss an energy projectLed development, adaptation and release of 80+ native apps across oil & gas monitoring, chemicals, rare earths and connected hardware.
Public-sector platform development for fire-service archive management in Shaanxi.
Enterprise-service mini-program development connecting industry services with mobile workflows.
Start with the decision, the available data and the conditions that matter. Validate the approach before moving it into an operational workflow.
Define the business question, stakeholders, data and success criteria.
Explore data, select methods and test assumptions against operating constraints.
Turn validated models into applications, reports or usable workflows.
Review results and applicability, then refine models and operating plans.
Tell us about your goals and data. Let's explore where AI, predictive analytics or optimization can make a practical difference.