production-ready through
Independent evaluation and training for the AI agent ecosystem. Real-world complexity through simulation environments where agents face multi-hour tasks.
Talk to FounderLarge-scale RL datasets with tuned difficulty distributions. Cheat-proof reward functions. Teach skills scarce in public data (e.g. dependency hell, distributed system debugging).
Measure quality and uncover blind spots. Pick optimal models, tune prompts in a fast-changing world. Benchmark against competitors. Win deals and deliver on performance promises.
Independent verification of what actually works. Design processes based on real capabilities, not marketing hype. ROI-driven deployment decisions. Move from FOMO to measurable P&L impact.
Explore our research on AI agents, benchmarking, and evaluation
A lot of vendors pitch AI SRE. We tested 14 models across 11 programming languages; even the best ones struggle with instrumenting code with the leading open-source standard, OpenTelemetry.
Prompts are specs, not code. This influences git workflows for vibe coding: tracking LLM prompts in GitHub repositories, managing commit messages, and debugging non-deterministic AI outputs.
AI reasoning models like DeepSeek-R1, agentic coding tools like Claude Code, and image generation with Nano Banana Pro set daily software engineering standards.
The Quesma database gateway IP has been acquired by Hydrolix to ensure continued support.
Read the announcement.