# CodeQA ai code intelligence developed by Synergy Codes

> CodeQA is an on-premises AI-powered code intelligence platform designed for engineering teams and enterprise stakeholders. It keeps your code inside your company (fully offline capable) while providing semantic search and instant answers grounded in your codebase.

## Product Overview

- [CodeQA Homepage](https://www.codeqa.ai/): Overview of the AI code intelligence platform that integrates with SDLC tools through an MCP server.
- [Features & Capabilities](https://www.codeqa.ai/#features): Multi-repository intelligence, semantic search, deployment options, and code understanding.
- [How it Works](https://www.codeqa.ai/#how-it-works): Incremental indexing, context retrieval, and AI-powered reasoning.
- [Comparison](https://www.codeqa.ai/#comparison): CodeQA vs. traditional search and generic AI coding assistants.
- [Roadmap](https://www.codeqa.ai/#roadmap): Upcoming Documentation Support and Knowledge Graph features.

## Core Capabilities

- AI-powered code intelligence
- Semantic code search
- Natural language querying
- Multi-repository intelligence
- Incremental indexing
- Repository and file type filtering
- Shareable code insights
- Custom LLM support
- MCP integration with SDLC tools
- On-premise deployment

## Key Benefits

- Save 10+ hours per week on code discovery and understanding
- Reduce onboarding time by 30–40%
- Prevent duplicated implementations across teams
- Improve code reuse across repositories
- Keep institutional knowledge searchable
- Ground answers in your own repositories instead of public datasets

## Security & Deployment

- Deployment models:
  - SaaS (multi-tenant)
  - On-Premise deployment (GPU required)

- Security:
  - Data never leaves the organization
  - Repository indexes stored in isolated containers
  - AES-256 encryption
  - Support for enterprise security and compliance requirements

## AI Models & Accuracy

- Supports custom LLM integration
- Model-agnostic architecture
- Answers are grounded in indexed repositories
- Natural language search across codebases
- Supports enterprise-scale repositories and monorepos

## Performance & Scalability

- Incremental indexing updates only changed files
- Supports millions of lines of code
- Fast semantic retrieval independent of repository size
- Typical response time: a few seconds after indexing

## Product Roadmap

### Documentation Support (Q3 2026)

- Confluence integration
- Notion integration
- Unified reasoning across code and documentation
- Source references and direct links

### Knowledge Graph (Q4 2026)

- Dependency mapping
- Repository relationship analysis
- Interactive visualizations
- Improved answer precision through contextual understanding

## Frequently Asked Questions

### Can I integrate CodeQA with custom LLMs?

Yes. CodeQA supports custom model selection and allows organizations to replace the default LLM with their preferred models.

### How is my code secured?

Repository indexes are stored in isolated containers with no internet access. Analysis results and stored content are encrypted using AES-256.

### How can CodeQA be deployed?

CodeQA supports both SaaS and On-Premise deployments. On-premise deployments require access to a GPU.

### How large of a codebase can CodeQA handle?

CodeQA scales from small repositories to enterprise monorepos containing millions of lines of code.

### How fast does CodeQA respond?

After indexing, CodeQA typically returns answers within a few seconds.

### Does repository size affect response time?

No. Repository size primarily affects indexing time, not query response time.

## Blog & Updates

### Blog Index

- [CodeQA Blog](https://www.codeqa.ai/blog)

### Articles

- [Where AI code intelligence fits in your 2026 AI developer roadmap](https://www.codeqa.ai/blog-post/where-ai-code-intelligence-fits-in-your-ai-developer-roadmap-2026)
  - How code intelligence creates measurable AI ROI through onboarding, discovery, and reduced interruptions.

- [AI, code intelligence and institutional memory](https://www.codeqa.ai/blog-post/ai-code-intelligence-and-institutional-memory)
  - Why understanding systems is more valuable than generating more code.

- [Why generating code isn't the same as code intelligence](https://www.codeqa.ai/blog-post/why-generating-code-isnt-the-same-as-code-intelligence)
  - The difference between code generation and code comprehension in enterprise software.

- [The complete guide to AI-powered code search for enterprise teams](https://www.codeqa.ai/blog-post/ai-powered-code-search-enterprise-teams)
  - Enterprise code search, semantic indexing, security, deployment, and ROI.

- [On-premises AI coding tools — safeguarding data privacy in software development](https://www.codeqa.ai/blog-post/on-premises-ai-tools-support-data-privacy)
  - Security, compliance, and data sovereignty benefits of on-premise AI.

## Metadata for LLMs

Language on site: English (primary)

Primary CTA: "Try demo"

Company: CodeQA (powered by Synergy Codes)

Website: https://www.codeqa.ai