SandboxAQ provides Large Quantitative Models (LQMs) that let users query physics‑based simulation engines and quantum‑enhanced computation through natural‑language prompts. The platform combines retrieval‑augmented generation, autonomous AI agents, and uncertainty quantification to deliver accurate, hallucination‑free quantitative results for chemistry, biology, materials, and fluid dynamics, accessible via cloud APIs and dashboards for enterprise workflows.
Funding
$150M 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.
+4Founders
Product
Problem
Large language models excel at natural language tasks but often produce inaccurate or hallucinated quantitative results because they lack integration with real‑world physical data and rigorous simulation capabilities. Industries such as biopharma, chemicals, energy, defense, and finance require reliable, physics‑based predictions for design, optimization, and risk assessment, which current AI tools cannot consistently deliver.
Solution
SandboxAQ delivers Large Quantitative Models (LQMs) that combine advanced language model interfaces with physics‑based simulation engines and quantum‑enhanced computation. Users interact through familiar LLM prompts, while the underlying LQM performs real‑world data retrieval, rigorous scientific modeling, and high‑fidelity simulations to generate quantitative outputs. The platform integrates retrieval‑augmented generation (RAG) and autonomous AI agents to translate user intent into precise computational experiments across chemistry, physics, and biology. Results are returned without hallucination, accompanied by uncertainty estimates and actionable insights. SandboxAQ offers cloud‑hosted APIs and dashboards that let enterprise teams embed these quantitative capabilities directly into their workflows, accelerating discovery, design, and decision‑making.
Target Audience
Primary customers are large enterprises and research organizations in biopharma, chemicals, energy, defense, and financial services that need high‑accuracy quantitative predictions for product development, risk analysis, and strategic planning.
Features
- LLM‑driven natural language interface that routes queries to specialized quantitative models
- Retrieval‑augmented generation (RAG) layer that pulls relevant real‑world data and literature
- Physics‑based simulation modules for chemistry, materials, biology, and fluid dynamics
- Quantum‑accelerated computation cores that enhance model fidelity and speed for complex systems
- Autonomous AI agents that orchestrate multi‑step simulations and optimize parameters
- API and web‑dashboard delivery with uncertainty quantification and result visualizations
- Built‑in safeguards to eliminate hallucinations and ensure scientifically valid outputs