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AI Infrastructure for the Future of Software Engineering

Foundational AI Infrastructure

Build in Secure & Scalable Development Environments
Scalable VMs available on demand with robust connections like GitHub repositories and SSH secured data stores
Standardize Containers & Streamline Work with Blueprints
Construct SDEs to match every task or agent, from configuration settings to program packages

Public & Custom Benchmarks

Public Benchmarks Beyond SWE-Bench
Runloop provides automated benchmarking tools to evaluate AI agents on real-world coding tasks, ensuring measurable progress and increased reliability
Custom Defined Code Scenarios & Scoring Functions
Compound proprietary advantages by constructing custom benchmarks to refine agent's performance on your priorities

Self-Improving Code Agents

Supervised and Reinforcement Fine Tuning
Leverage the data produced by benchmarks to perform Supervised Fine-Tuning and Reinforcement Learning Fine-Tuning with Runloop's naive capabilities
AI Research in Production
Realize the benefits of the latest AI research without the delays and overhead of in-house solutions

The complete platform for building, testing, and scaling AI-powered software engineering products.

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// Features

The Building Blocks for AI-Powered Developer Tools

Everything you need to build reliable, production-ready AI development tools.

Want to learn more about Runloop?

Explore our developer docs to see what's possible.

Explore Docs

Want to learn more about Runloop?

Explore our developer docs to see what's possible.

Explore Docs
//USE CASES

Solutions for Every Phase of AI-Driven Software Engineering

Discover how Runloop empowers teams of every stage to build, test, and optimize AI solutions for software engineering.

AI Native Startup

AI-first startup is developing sophisticated coding assistants & can't afford to build and maintain extensive infrastructure while racing to market.

High-Performance Infrastructure

The team can instantly deploy isolated code execution environments without managing containers or VMs, giving engineers immediate access to scalable compute resources.

Contextual Code Analysis

The AI is powered with deep semantic analysis, enabling it to parse complex, multi-file projects and deliver highly relevant, accurate coding recommendations.

Custom Benchmarking

The startup can measure AI performance against both industry standards and internal KPIs, tracking critical metrics like solution accuracy, response time, and code quality to drive continuous improvement.

Mid-Size Company Leveraging Expertise in Vertical AI Application

An enterprise focused on refining its AI applications can reduce operational expenses and stay focused on core expertise by eliminating infrastructure overhead.

Eliminate Infrastructure Overhead

The company can rapidly prototype and test AI-assisted coding features without the need to build and maintain infrastructure from scratch.

Scale Efficiently

The solution allows effortless scaling up or down based on application needs, without the complexity of orchestration.

Continuously Improve

By measuring the accuracy and relevance of its AI coding agents, the company can iteratively refine performance—compounding the power of its domain expertise over time.

Fortune 500 Company Optimizing Internal Coding Agents

A major enterprise optimizing internal AI coding agents can unlock a virtuous cycle of continuous performance refinement with secure, scalable infrastructure.

SOC2 Compliant Environments

The company can test coding agents in secure, isolated DevBoxes that meet strict organizational compliance and security standards.

Sophisticated Benchmarking

AI performance is measured against custom metrics tailored to the company's specific code patterns and quality requirements—without ever exposing the proprietary codebase.

Easy Enterprise Integration

The solution connects seamlessly with existing development tools, CI/CD pipelines, and security frameworks, minimizing friction and maximizing impact.

// Programming Languages

Run AI-Generated Code in Production

Secure, scalable development environments ready in milliseconds.

Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
Python Environment

Complete Python development environment

Core Tools
> Python 3.x runtime
> pip, conda package managers
> venv environment management
Development Tools
> Pytest test framework
> black code formatter
> mypy type checking
• Enterprise security • Native debugging
 • Enterprise security • Native debugging
 • Enterprise security • Native debugging
Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
TypeScript Environment

Complete TypeScript development environment

Core Tools
> Node.js runtime
> npm, yarn package managers
> TypeScript compiler
Development Tools
> jest testing framework
> eslint linter
> prettier formatter
• Enterprise security • Native debugging
 • Enterprise security • Instant scaling • Native debugging • Full system access • 
Enterprise security • Instant scaling • Native debugging • Full system access 
Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
Java Environment

Complete Java development environment

Core Tools
> JDK environment
> maven, gradle build tools
> jar packaging support
Development Tools
> junit test framework
> checkstyle linter
> debugger integration
• Enterprise security • Native debugging
 • Enterprise security • Instant scaling • Native debugging • Full system access • 
Enterprise security • Instant scaling • Native debugging • Full system access • 
Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
C++ Environment

Complete C++ development environment

Core Tools
> gcc/clang compilers
> cmake build system
> package managers (conan/vcpkg)
Development Tools
> gtest/catch2 testing
> clang-format
> debugging tools
• Enterprise security • Native debugging
 • Enterprise security • Instant scaling • Native debugging • Full system access • 
Enterprise security • Instant scaling • Native debugging • Full system access • 
Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
Go Environment

Complete Go development environment

Core Tools
> Go toolchain
> module support
> dependency management
Development Tools
> go test framework
> golangci-lint
> delve debugger
• Enterprise security • Native debugging
// Use Cases

The Platform for AI-Driven Software Engineering Tools

Explore the types of AI-powered developer tools you can build

AI Pair Programming Assistant

Your company is creating an AI that provides real-time coding suggestions and assistance.

High-Performance Infrastructure

Ensure your AI responds rapidly to user inputs.

Contextual Code Analysis

Utilize deep code understanding for relevant recommendations.

Suggestion Quality Metrics

Evaluate the helpfulness and accuracy of your AI-generated code snippets and advice.

Code editor displaying a JavaScript function checking for null and undefined values in user data. Below, a question asks why undefined !== null, with an AI bot explaining their distinct meanings.
Code snippet showing a calculation for lastLoginTime in TypeScript, with an AI-bot comment explaining an error related to daylight saving time inaccuracies and providing a suggested fix.

AI-Enhanced Code Review System

Your product streamlines code reviews using AI to identify issues and suggest improvements.

Parallel Processing Capabilities

Analyze multiple pull requests concurrently, enhancing scalability.

Customizable Evaluation Criteria

Adapt your AI's review standards to different coding guidelines.

Review Quality Assessments

Measure the accuracy and relevance of your AI-generated comments.

Intelligent Test Generation Platform

You're developing an AI solution that automatically generates comprehensive test coverage.

Language-Agnostic Environments

Deploy your AI across various programming languages.

Development Tool Integrations

Leverage IDE and language server connections for precise code analysis.

Test Coverage Evaluations

Quantify the comprehensiveness and effectiveness of your AI-generated tests.

Graph labeled 'Coverage Over Time,' showing test coverage increasing across six test runs, with an 89% completion rate highlighted at the top right. Below the graph, test statistics display 368 total tests, 322 passed, and 46 failed.

Scale your AI Infrastructure
solution faster.

Stop building infrastructure. Start building your AI engineering product.

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