Efficio scans your Python/C++ codebase, finds the functions burning latency, memory, and CPU, and rewrites them — verified against your test suite plus four more checks before a human ever sees the PR. Model-agnostic.
Hey Product Hunt 👋
My co-founder and I have spent the last 5 months building Efficio, on top of 8 months of prior R&D at IIIT Hyderabad, and we're currently part of Entrepreneurs First (F26).
Efficio is an AI-agnostic harness that scans an existing codebase and rewrites the slow parts cutting latency, CPU, and memory use without changing behavior. We're not trying to build another coding model; we're building the layer that sits on top of whatever model you already trust.
Some early numbers: 78.9% performance improvement on an optimization we found in Boost (C++). On NetworkX, we scanned 28 functions and found 5 statistically-verified wins — including 30% lower latency and ~75% lower memory on Graph.add_edge — for $0.98 in LLM spend. Across 20 open-source repos chosen to resemble enterprise codebases, finding 10 real optimizations from a 50-function scan costs about $3.
Every change passes your existing test suite, then four more verification layers static analysis, semantic equivalence, hypothesis + diff testing, and a codebase-wide intent audit before a human on your team reviews and approves the PR. Nothing merges automatically. And it runs entirely against your own in-house API, so code and data never leave your infrastructure.
We're currently talking to engineering teams at fintech firms, quant trading shops, and AI code-review companies basically anywhere execution time is either the cost driver or the whole game.
Would love your feedback, especially if you've felt the "we know our compute is wasteful, we just don't know exactly where" problem.