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LOCI

by Aurora Labs
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Now Grounding Coding Agents

We Make AI Coding Agents Execution-Aware

40%

Token Usage

2.1x

First-Pass Accuracy

60%

Iteration Cycles

Introducing LOCI — the Execution-first layer for AI coding agents' planning and reasoning. Our proprietary vertical models for BIN files boost first-pass accuracy, cut token burn, and eliminate time lost to overconfident AI patches.

As you code - No instrumentation. No runtime required.

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Save Time, Tokens & Money

Higher First-Pass Accuracy.

One small feature

Claude Code: “Spending ~2,000 tokens upfront on execution-aware analysis saved, 8 minutes, ~14,000 tokens of rework and discussion. 7x return.”

Scaled across a sprint

5 devs

× 20 changes

~1.4M

tokens saved

~33 hrs

reclaimed / sprint

  • Feature is written on the first attempt.
  • Save testing cycles to unveil true bugs.
  • First pass. No babysitting.
Control the Impact

AI changes code. You control the impact.

Ask LOCI to show how every change affects execution — so teams catch performance, power, and security risks before merge and test.
View Documentation
Live with MCP

Ground & verify coding agents

SW complexity causes loss of context. Execution aware model provides a north star, showing how SW runs in production.
View Documentation
Live on Github

On GIT, Validate before testing

Code review with evidence. Save costs, time, and resources while avoiding failures in testing cycles.

See LOCI On GitHub
Workflow

Earlier Insight. Fewer Surprises.

Code
Build
LOCI
Test
Merge

LOCI fits naturally into your existing pipeline.

No Cable needed.

No instrumentation required.

No profilers required.

No runtime overhead added.

No new pipelines to set up.

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Capabilities

What LOCI Flags

LOCI identifies execution risks that static analysis misses and testing catches too late.

Critical

Performance Regressions

Detect latency spikes and throughput degradation before deployment.

Hardware Inefficiency

Flag excessive instruction costs, GPU kernel divergence, and cache-unfriendly patterns.

Power & Cost

Identify power spikes and execution paths that stress thermal limits or increase cloud costs.

High-Risk Paths

Highlight rare but expensive branches and worst-case control flow paths.

Automated

KPI Validation

Automatically enforce project budgets for latency, memory, and utilization.
New

LLM Grounding

Provide execution-aware signals to Cursor, Copilot, and Gemini to prevent bad code generation.
Integrations

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)
View Documentation

TERMINAL

$ git clone git@github.com:”your project”
$ cd your project
$ git checkout –track …
$ claude mcp add “loci url”

Use Cases

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

Deep Dive

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%

Use Cases

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

Next Step

Start Grounding Your Code

Integrate execution reasoning into your workflow in minutes

Get In Touch. We'd love to show you LOCI.

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