The high-performance AI coding assistant designed for large-scale enterprise codebases.
Augment is a vertically integrated AI coding platform engineered to address the limitations of generic LLMs in complex software environments. Built by experts from Google, Microsoft, and VMware, Augment utilizes a proprietary inference engine and advanced Retrieval-Augmented Generation (RAG) to index entire enterprise-scale repositories. This enables the assistant to provide code completions, refactors, and explanations that are strictly grounded in a company's unique internal libraries and architectural patterns. By 2026, Augment has positioned itself as the premier alternative to GitHub Copilot for organizations prioritizing data sovereignty and low-latency performance. Its technical stack focuses on 'Deep Context,' moving beyond simple file-level analysis to understand cross-service dependencies and semantic relationships. The platform is designed to operate seamlessly within popular IDEs while providing enterprise-grade security features, including SOC2 Type II compliance and robust data isolation protocols, ensuring that sensitive intellectual property is never leaked or used for training public models.
Proprietary vector indexing that maps dependencies across thousands of files simultaneously.
Accelerate development with AI-powered code suggestions and integrated security scanning across the SDLC.
The leading terminal-based AI pair programmer for high-velocity software engineering.
Accelerate development cycles with context-aware AI code generation and deep refactoring logic.
State-of-the-Art Mixture-of-Experts Coding Intelligence at 1/10th the Cost of GPT-4.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Custom inference engine optimized for NVIDIA H100/A100 clusters to provide sub-100ms response times.
Natural language querying of code intent rather than just keyword matching.
Configurable data exclusion zones and guaranteed non-training on customer data.
Automatically switches between small, fast models for completion and large models for complex refactors.
Generates tests that mock internal dependencies discovered during indexing.
Centralized index that shares learned patterns across all developers in an organization.
Moving from a Python 2.7 monolith to Microservices without breaking global dependencies.
Registry Updated:2/7/2026
A new hire needs to understand a complex internal SDK.
Migrating from AWS SDK v2 to v3 across 400 repositories.