Higher first-pass quality,optimized codebefore it runs.
Deadlines, power, memory, paths — the loop tells you too late.
LOCI predicts at /plan, as you code, and in CI/CD.
Fix it before the cycle is spent.
Powered by AI PhysicsAI Physics — deterministic models, bounded by the physics of the CPU/GPU.
Trusted by partners, customers and investors
How it works
Execution cost vs budget. ROI from the first session.
Every LOCI catch is a decision made from data instead of engineering judgement: no setting up timing measurements, adding logs, attaching a debugger or reproducing on a bench to find out. The cockpit counts that avoided work as engineer hours.
10.0 h
this case · 0 pushbacks × 5.5 h + 8 checks × 1.25 h
roscon-loci-demo · 8 runs
5.0 h
per working day, per engineer — measured rate
10.0 h over 2 active days
Your team
50 h
per sprint
$5,000
325 h
per quarter
$32,500
$32,500
engineering time back / quarter
325 h × $100/h
Supervision tax = pushbacks × 5.5 h + checks × 1.25 h (LOCI cockpit weights). A projection: assumes the team keeps LOCI’s catch rate from this project (8 catches in 2 days, 2026-11-09 – 2026-11-10).
CONTRACT ENVELOPE · cost vs budget
│ budget · bars past it are over
One engine, every domain
See LOCI across different engineering domains.
Same execution-aware guardian. Same interaction. Pick a domain.
In CI
10,294 functions checked. One comment on the PR.
The LOCI GitHub Action on a real upstream SPDK change: base against change, every function priced, the nine that moved explained — posted on the pull request before anyone reviews it.
GitHub Actions · auroralabs-loci/spdk PR #12 · job 2m 47s · 22 modified · 1 new · 1 removed
The engine
AI Physics, a small, fast foundation model for software execution on real silicon.
AI Physics is a software execution model learned from real workloads and platform traces — not source. LOCI’s model, LCLM, realizes it, generalizing to new, unseen binaries at up to R² = 0.96 — it predicts what code will do, not just what it says.A small, fast model trained on real-silicon traces. Generalizes to new, unseen binaries at up to R² = 0.96 — catching what source-only LLMs miss.
Deterministic
Bounded by physicsiEvery prediction is a measurable physical quantity — cycles, ns, energy — checkable by running the binary on real hardware. It can't drift into invented numbers the way free-form text can.
Verifiable on hardware
Human-on-the-loop
- patents
- 0+
- patents
- real-platform training
- 0 yrs
- real-platform training
- verified on real hardware
- Silicon
- verified on real hardware
Not a GPT wrapper — trained on execution, not text.
Read more: AI Physics for Physical AIPredicted vs measured on new, unseen binaries · matched to the real eval · up to R² = 0.96 · MAPE ≈ 8%.
Works with the tools your team already uses
- Platformself-hosted · SaaS
- GitGitHub · GitLab · Bitbucket
- AzureDevOps · pipelines
- AWSMarketplace listing
- Claude CodeMCP plugin
- GCC+ Clang · LLVM · MSVC
- LanguagesC · C++ · Rust · Go · Cython
Safety-critical DNA
Built to the standards your compliance team already trusts.
8 years shipping into automotive and industrial systems. LOCI inherits the rigor.
ASPICE Level 2
Automotive software process maturity
ISO 26262 / ASIL-B
Functional safety for automotive
ISO 21434
Cybersecurity engineering for road vehicles
Autonomous Vehicles
Production AV programs · ISO 21448 / SOTIF aligned
ISO 27001
Information security management
120+ Patents
Binary analysis & execution modeling
Guard every coding agent decision with execution evidence.
Your coding agents are already shipping decisions. LOCI gives every one — plan, PR, merge — runtime-grade evidence the coding agent and reviewer can act on.
75% of AI coding agents introduce quality regressions during long-term maintenance. Source