open source · ai infrastructure
Infrastructure for the
age of intelligent systems
The wave of capable foundation models has created a new problem: the infrastructure to actually use them well doesn't exist yet. Not at the level that matters — benchmarking, adaptation, and the runtime systems that let models operate autonomously in the real world. That's what we're building.
Open source, at every layer of the stack
Aevyra is building the missing layer between foundation models and production — the infrastructure that makes them actually work.
Everything is open-source. No wrappers, no shortcuts.
See the projects →From the Blog
Recent Projects
Verdict
LLM benchmarking. Run your prompts across any model, score responses with pluggable metrics, and get a side-by-side comparison. The foundation for model selection, prompt engineering, and knowing whether your fine-tuning is actually working.
Reflex
Agentic prompt optimization. Reflex takes your dataset and prompt, runs evals, diagnoses why scores are falling short, and rewrites the prompt — iterating until it converges.
Fine-tuning Infrastructure
Closing the loop between evaluation and training. Good fine-tuning requires good data curation, careful eval design, and knowing when to stop — none of which scales with manual iteration. A pipeline that automates the cycle: evaluate, curate, train, repeat.