We Make AI Coding AgentsExecution-Aware
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.
- 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
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.
fn-level signal as you type
all 5 signals, whole program
paths your suite never reaches
blocks if signal exceeds baseline
Applied Execution Reasoning
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
Proof, Not Promises
LOCI is applied to production-grade open-source projects like OpenSSL and LLaMA.cpp. Our results are inspectable, explainable, and verifiable.
Start Grounding Your Code
Integrate execution reasoning into your workflow in minutes