Now Grounding Coding Agents
LOCI enabling LLMs execution awareness
LLMs can be overconfident. With LOCI, they reason over compiled executables to generate and review code with timing, regression, and power awareness - without running the software.
Ground & verify coding agents
Higher First-Pass Accuracy.
Token Usage
40% Lower
Without LOCI
~48K tokens
With LOCI
~29K tokens
First-Pass Accuracy
2.1x Higher
Without LOCI
38%
With LOCI
81%
Iteration Cycles
60% Fewer
Without LOCI
With LOCI
On GIT, Validate before testing
Code review with evidence. Save costs, time, and resources while avoiding failures in testing cycles.
Maintain Code Beyond the Demo
As AI accelerates development, the challenge shifts from building models to running them reliably in production. LOCI bridges this gap by grounding LLM-generated code in execution behavior, surfacing risks and inefficiencies before the next production gate.
What changes in production
- Performance stability & infrastructure load
- Integration risk across AI components
- Long-term maintainability of generated code
- Execution-aware signals into CI/CD workflows
Earlier Insight. Fewer Surprises.
LOCI fits naturally into your existing pipeline.
No Cabale needed.
No instrumentation required.
No profilers required.
No runtime overhead added.
No new pipelines to set up.
What LOCI Flags
LOCI identifies execution risks that static analysis misses and testing catches too late.
Performance Regressions
Hardware Inefficiency
Power & Cost
High-Risk Paths
Highlight rare but expensive branches and worst-case control flow paths.
KPI Validation
LLM Grounding
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)
TERMINAL
$ git clone git@github.com:”your project”
$ cd your project
$ git checkout –track …
$ claude mcp add “loci url”
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
Technology Under the Hood
We reason between compiled code and real CPU and GPU execution behavior, including worst case path, rare and tail paths that attackers can exploit.
1
Binary Decomposition
LOCI works directly on compiled binaries, analyzing execution units, basic blocks, and instruction sequences, no source code required.
2
Execution Modeling
We apply models trained on real CPU/GPU traces to capture branching behavior, memory pressure, and scheduling interactions.
3
Bounded Prediction
Unlike LLMs, which generate unconstrained text and may hallucinate, LOCI predicts bounded execution-time values on real measurements, eliminating the possibility of fabricated outputs.
> Execution Context
Architecture
x86_64 / NVIDIA Ampere
Optimization
-O3 (Release)
Input State
Bounded (Project Config)
Model Confidence
99.8%
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