INT21
INT21 builds self-improving AI systems that optimize the software stack beneath modern AI models. Its Inference Engine Factory uses cloud-native swarms of autonomous agents to explore parallel configuration paths, validate performance against real hardware, and carry generational knowledge forward, enabling engineering capacity to scale with compute rather than headcount.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Modern AI models require highly optimized inference engines to meet performance targets, but traditional engineering approaches are bottlenecked by manual tuning and limited headcount. As models grow in complexity and deployment constraints multiply, teams struggle to explore the vast configuration space needed to achieve optimal serving metrics on specific hardware.
Solution
INT21 builds self-improving AI systems that optimize the software stack beneath modern AI models. Their Inference Engine Factory uses cloud-native swarms of thousands of autonomous agents to explore parallel configuration paths, validate performance against real hardware, and carry forward generational knowledge. Experts set direction, constraints, and acceptance criteria while autonomous agent swarms execute, evaluate, and retain the work, allowing engineering capacity to scale with compute rather than headcount. The factory produces continuously refined inference engines that meet specific deployment constraints and serving metrics, with results and failures becoming the next generation's starting point.
Target Audience
Primary customers are AI product teams and infrastructure operators who need high-performance inference engines optimized for specific models, deployment constraints, and hardware targets without scaling engineering headcount.
Features
- Cloud-native swarm control plane that elastically scales thousands of autonomous agents around available compute
- Parallel engine search where specialized agents explore configuration and optimization paths simultaneously
- Generational memory system that carries forward results, failures, and strategies into each subsequent iteration
- Workload-level objective definition starting from the model, deployment constraints, and the serving metric that matters
- Live system model with shared direction ensuring every agent works against the same constraints and acceptance criteria
- Production of native inference engines across 7 model types (language, image, video, speech, music, transcription, vision) and 3 GPU families, with 20 engines demonstrated on real hardware