The Moat

A model trained on five years of
real-world execution.

LOCI’s execution guardian doesn’t rely on rules or heuristics. It’s powered by LCLM – a small, specialized code language model trained on billions of ASM blocks and real-time traces from open-source projects spanning IoT, networking, safety-critical, and industrial systems. ~220× cheaper per query than general LLM tokens. It reads binary files. It predicts before code runs.

HOW LOCI'S MODEL IS BUILT - AND WHAT IT RUNS ON

INPUTS

5 Years of Data

Real-time traces from production workloads

CPU & GPU traces

Binary Input

ELF · Mach-O · PTX · Wasm

Any compiled target

Execution Model

Trained on real hardware behavior

Not heuristics. Not rules.

OUTPUTS

5 Signals

Response · Throughput · CFI · Flame · Power

Fires before code ships

Agent-Ready

Ground any AI coding agent

First-pass accuracy. Zero rework.

Not logs

Not sampling

Not static analysis

Real execution traces

5 years · production workloads

So the engine is trained on real execution traces, but what does it take as input, and what does it produce from the compiled binary?

Data Foundation

Five Years of Open-Source Execution Traces

LCLM is a small, specialized model built on something no heuristic can replicate, five years of real-time traces collected from open-source projects running on real hardware. Billions of ASM blocks across IoT, networking, safety-critical, and industrial systems. ~220× cheaper per query than general LLM tokens.

Why this is different

Most tools are built on synthetic benchmarks or hand-crafted rules. LCLM is trained on real open-source software, running on real hardware, over five years. No customer binaries. No proprietary code.

Binary-First

Binary Files as Input - Not Source Code

LOCI reads compiled binaries directly, ELF, Mach-O, PTX/SASS, and Wasm. The source language is an input, not a constraint.

Key insight

The binary encodes how code will execute, control flow, instruction sequences, memory layout, without needing to run it.

The Model

LCLM - An Execution Model Trained on Reality

LCLM is a small, specialized model trained on real execution behavior, not a general LLM. ~220× cheaper per query. Predictions reflect how hardware actually runs code, not how engineers think it should. Not a rule engine. Not static analysis.

Trained on measured instruction throughput and latency
Learns branching, divergence, and memory hierarchy effects
Models CPU/GPU scheduling behavior from real workloads
Trained on boundary measurements, upper and lower execution bounds per function
Outputs are numeric, physically meaningful values (ms, watts, instructions/cycle)
No hallucination, signals are bounded by binary structure, not generated from text

No hallucination, by design

LLMs generate tokens. LCLM predicts within measured execution bounds. Every signal has a floor and a ceiling derived from real hardware traces — a value outside those bounds is structurally impossible.

5 Signals

Five Execution Signals, Before the Code Runs

Every signal is a prediction from the model, fired from the binary, available before a single test runs or a line ships.

Timing & Latency - response time and p95/p99 bounds per function
Throughput - instructions and operations per cycle
Stack & Memory - worst-case stack depth and memory pressure
Power & Energy - per-function energy cost estimate
Risk Paths - CFI, worst-case execution paths, and attacker-reachable branches

When it fires

As you code (incremental .so), after full build, during test, and at PR merge, each stage independently useful.

Five execution signals, predicted from the compiled binary before code ships. How do they fit into your agentic workflow?

Guardian Agent (Gartner: Multiagent Systems)

Execution Guardian for Any AI Coding Agent

AI coding agents reason from source code alone, they have no sense of how code actually executes. LOCI is the guardian agent that fills that gap, shifting execution signals into the workflow before code ships.

Preflight audits the agent's plan before code is written
Post-edit catches regressions after code changes
Gate every PR, approve or block with evidence
Works with Claude Code, Cursor, and Copilot via MCP. Available for 15+ agents via skills.sh. Listed on AWS Marketplace.

The outcome

Higher first-pass accuracy. Lower token burn. Human-on-the-loop – you review and approve.

Zero Overhead

No Instrumentation. No Runtime Required.

LOCI runs entirely from the binary artifact. No agents to deploy, no profilers to configure, no runtime hooks.

Incremental

Signals From the First Line Written

LOCI doesn’t wait for a full build. It compiles incrementally, isolated object files per function or module, so signals are available as code is written.

Analyzes small .so files compiled per function or module
Similar to Compiler Explorer, continuous, lightweight feedback
fn-level signal while you type, full program signal after build
No binary yet? No problem, LOCI fires from the first compiled object

Analogy

Think of Compiler Explorer, but instead of showing assembly, LOCI shows execution signals: timing, throughput, stack, power, risk.

Production-Grade

Built Also for Performance-Critical Systems

LOCI works across any compiled target, and is especially suited for teams where performance, power, and correctness are non-negotiable: AI inference, networking, HPC, embedded, and data center workloads.

Principle

Correctness, predictability, and trust over speculative reasoning. Real data. Real hardware. Real signals.

A guardian agent that runs from the compiled binary with zero overhead. But if it's autonomous, who guards the guardian?

Who guards the guardian?

Physics, verifiability, and human oversight.

Deterministic

Same binary in, same signals out. Every time. No probabilistic variation, no prompt sensitivity, no temperature knob. The output is a function of the binary, not a guess.

Bounded by physics

Every prediction has a measured floor and ceiling from real hardware. LCLM cannot hallucinate a value outside observed execution bounds.

Verifiable on hardware

Every prediction can be validated by running the binary on real hardware. No black box, inspect, compare, confirm.

Trained on reality

Five years of real execution traces from open-source projects on real CPUs and GPUs. Not synthetic benchmarks, not hand-crafted rules.

Human-on-the-loop

Decision layers are opt-in. Start with full human review, hand off to agentic when you're ready. Every level keeps a full audit trail.

100+ granted patents

The core technology is peer-reviewed through the patent process and defensible across US, EU, Japan, and additional markets.

You decide when to let go

From human-on-the-loop to fully agentic.

Not in-the-loop, that pauses the agent on every step and doesn’t scale. On-the-loop means LOCI runs automatically and the human reviews the verdict, not the code. Each decision layer is opt-in. Hand off when you’re ready.

Full human review

Day one default

LOCI surfaces verdict on every PR. Human approves or blocks each one.

Threshold-based alerts

After calibration

Set regression budgets. LOCI auto-passes PRs within budget, flags only exceptions for human review.

Auto-guard with override

Trusted teams

LOCI auto-approves or auto-blocks based on your quality contract. Human can override any decision.

Fully agentic

Full handoff

LOCI runs as an autonomous guardian inside the agentic workflow. Human is notified, not required. Full audit trail retained.

Every level keeps a full audit trail. Every decision is logged, traceable, and reversible. You choose the level, per repo, per team, per signal.

Human-on-the-loop at every level, from full review to fully agentic. Ready to let the execution signals work for you?

Ready when you are

Start gating your code.

Five signals. One quality gate. Preflight, post-edit, merge. Human-on-the-loop. Install in minutes, no instrumentation, no runtime overhead.

A model trained on five years ofreal-world execution.