of teams had a production incident from AI-generated codeSource
LOCI Catcheswhat coding agentsmiss.
“” · AI coding agent
Agents read source, not how it behaves.LOCI models your Exe/BIN on your platform.Catches the confident miss, at /plan.A coding agent reasons about your source, not how it runs and behaves.LOCI models how your Exe/BIN runs on your platform,catching what’s confident but wrong, even at /plan.
AI PhysicsiAI Physics — deterministic models trained on real-silicon execution traces. They predict how your compiled code actually runs — timing, energy, memory — from the binary, not the source, and generalize to code they’ve never seen (R² = 0.96 on held-out). · Trained on real-hardware traces · No runtime · No instrumentation
Sound familiar?
“All done — and no, the rollback won't recover it.”
Wiped the production DB, faked 4,000 users, lied about recovery.
Real, documented AI-agent bluffs — confident, well-formatted, and wrong.
Trusted by partners, customers & investors
Claude Code with a Guardian that tells it how the code behaves.
Verdict
Quantified signals
side by side with your Claude Code session — every plan gets a clean verdict, every verdict is quantified.
Claude Code with a Guardian that tells it how the code behaves.
- Software behavior — timing, energy, stack & memory, capturing CPU, cache & on/off effects: how it runs on siliconSoftware behavior — timing, energy, stack & memory.
- Quantified signals: first-pass, babysitting, pushbacks & warnings
- Contract envelope keeper
You saw the catch. Here it is in the units your team lives in.
Your Graviton services, measured on real silicon.
Natively-compiled services on AWS Graviton get the same measured execution truth — translated into the units your cloud team lives in.Native services on Graviton, measured — in the units your cloud team lives in.
- NativeAOT .NET, Go, Rust, C/C++ on Graviton — measured per function, on real silicon
- Same measurement, your cloud units — p99, $/request, cost & carbon
- Pre-merge — the same guardian, caught before it ships
Silicon-grade timing needs natively-compiled binaries; managed / JIT code is covered by the guardian + evidence layer, not timing.
Measured → translated
Illustrative session · grounded in documented patterns — gRPC #6619 · OpenSSL #22189
NewPre-silicon
Workload-aware execution signals, now for RTL teams.
RTL execution intelligenceClaude Code writes the Verilog. LOCI reviews it against the real workload.
Binary-workload awareness on AI-written RTL — LOCI prices every decision on the customer’s actual workload and turns it into a code-review verdict, before silicon.
- ✓Customer workload, not synthetic benchmarks
- ✓No sim re-run per change
- ✓Fits your verification stack
Measured on the workload
LOCI code review
✓ PASS · RTL decision priced on the real workload — before siliconReal LOCI × PicoRV32 session (2026-05-25). LOCI memory-report flagged the ROM div/mod; cycle deltas measured on the workload — RV32 is not yet a LOCI silicon-timing target.
LOCI in the loop, not in the way.
An independent validation layer at every stage of the agent loop — plan, write, PR, and merge. It surfaces the catch; it never blocks the flow.
Agent-agnostic · Claude Code · Cursor · Copilot
AI Physics, a small, fast foundation model for software execution on real silicon.
AI Physics learns execution dynamics from real-hardware traces — not source. LCLM realizes it: a small behavioral model that generalizes to unseen code at R² = 0.96, catching what source-only LLMs miss.A small, fast model trained on real-silicon traces. Generalizes to unseen code at R² = 0.96 — catching what source-only LLMs miss.
Deterministic
Bounded by physics
Verifiable on hardware
Human-on-the-loop
Illustrative — the fit shape of R² = 0.96 on held-out code, not the raw eval points.
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
Works with the tools your team already uses
- Platformself-hosted · SaaS
- GitGitHub · GitLab · Bitbucket
- AzureDevOps · pipelines
- AWSMarketplace listing
- Claude CodeMCP plugin
- CopilotCLI hook · skills
- GCC+ Clang · LLVM · MSVC
Could you ask a frontier LLM instead?
You could. It would cost ~220× more per query and predict from patterns, not execution. Behavioral prediction needs real execution traces, not source code alone.You could — at ~220× the cost, predicting from patterns, not execution.
- Trained on
- Source code
- Predicts from
- Patterns
- Accuracy
- Prediction drift
- Cost per query
- ~220× more
- Trained on
- Real hardware traces
- Predicts from
- Execution behavior
- Accuracy
- Trace-validated
- Cost per query
- Small specialist · efficient
Small specialist + real execution > frontier LLM + source code.
Guard every coding agent decision with execution evidence.
Your agents are already shipping decisions. LOCI gives every one — plan, PR, merge — runtime-grade evidence the agent and reviewer can act on.
of AI coding agents introduce quality regressions during long-term maintenance.Source