LOCI Catcheswhat coding agentsmiss.
Fast AI → thinks ahead
Coding agents write code. LOCI thinks ahead.
LOCI knows what the code will do before it runs.
Performance • Power • Memory • System behavior
Predict → Warn → Guide
Less babysitting • Fewer regressions
Powered by 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
Trusted by partners, customers & investors
Give your coding agent a second brain.
Coding agents know how to write code.
LOCI knows what happens when that code runs.
Predict
What will happen when this code runs?
Warn
Guide
AI writes the code. LOCI predicts the consequences.
Less babysitting. Fewer regressions. Higher first-pass accuracy.
No runtime. No instrumentation. Just a pre-merge verdict.
Predict the outcome, before you merge.
LOCI sits in the loop with your coding agent — it predicts what the change will do on the target machine, the coding agent fixes it, and you merge with confidence.
Coding Agent
Writes code
LOCI Guardian
Predicts what will happen
- Slower execution?
- More power?
- More memory?
- System side effects?
- Breaks team limits?
Coding Agent
Improves the implementation
Merge
with confidence
From finding problems late, to fixing them before merge.
Without LOCI
- AI writes code.
- Developers test it.
- Problems are found later.
- Teams babysit coding agents.
With LOCI
- AI writes code.
- LOCI predicts the outcome immediately.
- The coding agent fixes itself.
- Teams review less, ship with more confidence.
LOCI turns coding agents from
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.
Illustrative · 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.
- ✓BIN workload, not synthetic benchmarks
- ✓No sim re-run per change
- ✓Fits your verification stack
Real 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 Claude Code agentic 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. LOCI’s model, 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 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
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 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