Now Grounding Coding Agents
We Make AI Coding Agents Execution-Aware
40%
Token Usage
2.1x
First-Pass Accuracy
60%
Iteration Cycles
The execution layer for planning and thinking by AI coding agents. Powered by models for BIN files, trained on real workloads. Higher first-pass accuracy. Reducing back & forth.
As you code - No instrumentation. No runtime required.
Higher First-Pass Accuracy.
One small feature
Claude Code: “Spending ~2,000 tokens upfront on execution-aware analysis saved ~14,000 tokens of rework and discussion. 7x return.”
Scaled across a sprint
How we calculate
5 devs × 20 AI-assisted changes
= 100 changes / sprint
Each change: ~14K tokens saved
vs. unguided LLM context
100 × 14K = ~1.4M tokens
tokens → time → ~$5K saved
Daily window capacity
Teams on flat-rate AI plans hit the
daily token cap by ~3pm.
Fewer wasted tokens per task =
2.2× more real work in the same
window - coding until 6pm, not
locked out at 3.
5 devs
× 20 changes
~1.4M
tokens saved
~33 hrs
reclaimed / sprint
2.2×
daily window used
~$5K
saved / sprint
- Save testing cycles to unveil true bugs.
- First pass. No babysitting.
- Don't let the cap stop the work.
- Real numbers from a BLE project — not synthesized data.
Vibe coding at scale will break master.
PR with evidence. No runtime. No instrumentation.
LOCI lets you review AI agent changes and control the impact, predicting execution behavior directly from the binary, before anything runs.
AI PR review impact
How we calculate
10 AI PRs × 4 devs reviewing = 40 review
sessions / sprint
Each: ~2 hrs of manual execution tracing
eliminated by LOCI
40 × 2 hrs = ~80 hrs
80 hrs × $75/hr = ~$6K saved
10 AI PRs
× 4 devs
~2 hrs
saved / PR review
~80 hrs
reclaimed / sprint
~$6K
saved / sprint
Quick Start
Works Where You Work
Integrate in minutes via MCP and APIs. No new pipelines, no new dashboards to learn.
- GitHub & GitHub Marketplace Native
- CI/CD Pipeline Integration (Actions, GitLab, Jenkins)
- IDE Support (VS Code, JetBrains)
- LLM Agent Grounding (Cursor, Claude Code)
No disruption to your workflow.
LOCI sits alongside your existing pipeline, no new build steps, no instrumentation, no profilers. Plug it in at any stage and execution signals start immediately.
LOCI SIGNAL LAYER
Plug in at
one stage
or
the full pipeline
Code
incremental .so
fn-level signal as you type
Build
full binary pass
all 5 signals, whole program
Test
tail & edge cases
paths your suite never reaches
Merge
full binary pass
all 5 signals, whole program
Each stage is independently useful — or run the full layer for continuous coverage.
No instrumentation required.
No runtime overhead added.
No profilers to set up.
No new build steps.
No changes to your CI.
No code changes needed.
Works Where You Work
From AI infrastructure to automotive SDV and IoT
Grounding LLM Agents
Teams increasingly rely on LLM coding agents such as Cursor, Claude Code, Gemini, and GitHub Copilot. Without execution context, these tools can generate code that looks correct but behaves poorly at runtime.
• Constrains generation within real execution limits
• Prevents performance-regressing suggestions
• Guides optimization decisions with execution truth
Security-Critical Execution Paths
Many correctness and security risks depend on how code executes, not just what it does. LOCI highlights risky execution behavior early without replacing existing security tools.
• Correctness depends on rare control-flow paths
• Memory access patterns are unsafe or fragile
• Changes introduce risky execution behavior
Automotive Safety
For automotive and safety-critical systems, predictability and availability matter. LOCI helps surface execution risks early — before integration and vehicle-level validation.
• Understand worst-case and tail execution paths
• Identify execution variability and contention
• Analyze change impact on system availability
Optimization & Cost
Stop guessing where to optimize. LOCI identifies hot execution paths, inefficient instruction sequences, and memory bottlenecks, helping you reduce cloud compute costs and energy consumption.
• Hot execution paths
• Inefficient instruction sequences
• High-cost memory access patterns
Proof, Not Promises
LOCI is applied to production-grade open-source projects like OpenSSL and LLaMA.cpp. Our results are inspectable, explainable, and verifiable.
OpenSSL
LLaMA.cpp
FreeRTOS
CUDA
Start Grounding Your Code
Integrate execution reasoning into your workflow in minutes