LOCI Catcheswhat you, and coding agentsmiss.
Pre-run execution prediction for compiled software.
Plan, code, test, fix, repeat — every loop costs a cycle.
LOCI predicts inside the loop, so you fix before it costs you.
Powered by AI PhysicsiAI Physics — deterministic models trained on real-world workloads and platforms. They predict how your compiled code actually runs — timing, energy, memory, software behavior — from the binary, not the source, and generalize to binaries they have never seen — up to R² = 0.96 on unseen code.
Trusted by partners, customers & investors
Fewer loops. Higher first-pass quality.
Deadlines, power, memory, paths — the loop tells you a cycle too late.
LOCI predicts at /plan, at code write, in CI/CD. Fix before the cycle is spent.
- Fewer iterations
- 6 caught pre-merge — found before the loop spent a cycle
- Less agent back-and-forth
- 21 of 27 auto-verified — the agent stopped asking you to check
- Higher first-pass quality
- 77% first-pass clean — landed right the first time
Testing still has the final word. It just stops being where you find out.
CONTRACT ENVELOPE · cost vs budget
│ budget · bars past it are over
Real measured session · BLE_TI on TI CC2674P10
Static analysis stops at the source. A profiler stops at the run you took. Neither tells you what will happen.
| Static analysisbefore · source | Coding agentbefore · source | Profilersafter run | Testing / Simafter run | Observabilityafter run | LOCIbefore · binary | |
|---|---|---|---|---|---|---|
| Timing | ||||||
| Execution time, per function and block | — | — | — | — | — | ● |
| Worst-case path (WCET), not observed average | — | — | — | — | — | ● |
| Memory | ||||||
| Worst-case stack depth | — | — | — | — | — | ● |
| Heap and buffer pressure | — | — | — | — | — | ● |
| Power | ||||||
| Energy per call, power draw | — | — | — | — | — | ● |
| Method | ||||||
| Control flow and call-graph reach | ● | — | — | — | — | ● |
| Symbolic execution, with time | — | — | — | — | — | — |
Surface the risks before you spend the time.
See LOCI across different engineering domains.
Same execution-aware guardian. Same interaction. Pick a domain.
Your Graviton services, measured on real silicon.
Natively-compiled services on AWS Graviton, measured in the units your cloud team lives in — p99, $/request, cost & carbon.
- NativeAOT .NET, Go, Rust, C/C++ — measured per function
- Caught pre-merge by the same guardian
- No instrumentation — runs from the binary
Illustrative · grounded in documented patterns — gRPC #6619 · OpenSSL #22189
Same gate. Five different ways to be wrong.
Each domain leans on a different gate, and every gate here is green. None of these is visible in the source.
Automotive · ECU
agent: “simplify the CAN frame parser”
unit tests ✓ · review ✓ · HIL slot next week
handler worst case runs past the frame window
WCET exceeds the arrival period — frames dropped under burst, not under test
Security · crypto
agent: “tighter constant-time compare on the auth path”
sast ✓ · sca ✓ · crypto review ✓
constant-time property lost
timing variance correlates with secret-bit count · CWE-208 pattern
Cloud · service
agent: “cleaner buffer handling in the encrypt path”
load test green at tested concurrency
allocation introduced on the hot path
per-request alloc/free moves p99 well past its budget under load
Robotics · control
agent: “offload the filter to the accelerator”
simulation ✓ · review ✓
CPU waits on the handoff, buffers held across the wait
control-loop jitter widens and in-flight work is unbounded
Battery · IoT
agent: “poll the sensor more often for accuracy”
review ✓ · power last measured a release ago
energy per cycle rises against a fixed budget
duty cycle breaches the power envelope before it breaches timing
Representative patterns, not measured sessions. Each is a mechanism LOCI models — worst-case timing, variance, allocation on a hot path, energy per cycle — on a supported target.
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
Predicted 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
- TargetsJetson · Orin · aarch64 · Cortex-M · TriCore
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.
of AI coding agents introduce quality regressions during long-term maintenance.Source