ThinksLabs develops physics-informed AI agents to automate and enhance electric grid planning and operations. Their platform digitalizes grid data to deliver high-accuracy power flow analysis and real-time state estimation. This enables utilities to achieve faster scenario testing, robust decision-making, and improved energy autonomy.
Funding
$5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Critical infrastructure systems, particularly in the energy sector, face increasing complexity and dynamic operating conditions that strain traditional human operational capabilities. Real-world data is often imperfect, making it difficult to run complex engineering mathematics and ensure reliable decision-making. Ad-hoc studies and a lack of continuous learning from in-field experience can lead to suboptimal asset utilization and increased operational risks.
Solution
ThinkLabs AI provides physics-informed AI digital twins to enhance operational efficiency and decision-making in complex engineering systems. Their flagship product, ThinkLabs Copilot, serves as a digital assistant that understands the underlying mathematics governing real-world phenomena. By training AI digital twins with physics-based models, Copilot delivers continuous, real-time situational awareness and automated analytics. The platform generates insights and solutions that assist and automate critical operational processes, augmenting operational dashboards and network views through natural language interactions. ThinkLabs AI's solution enables improved asset utilization, enhanced grid reliability, and better integration of renewable energy sources.
Target Audience
ThinkLabs AI targets critical infrastructure operators and planners within the energy sector, particularly those responsible for electrical grid management and renewable energy integration.
Features
- Physics-informed AI digital twins trained with real-world data, formulas, and guardrails
- Continuous power flow monitoring and state estimation for real-time situational awareness
- Automated data validation using AI to learn real-world models and validate source engineering data
- Optimal solution generation to address grid constraints and improve operations under complex conditions
- Asset utilization reporting to track infrastructure performance under dynamic operating conditions
- Edge intelligence for fast, resilient, and distributed intelligence
- AI-driven insights for congestion and voltage violation detection