# Tentaro — on-prem AI code context by Synergy Codes

> Tentaro is an on-premises AI code context layer 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 own repositories. While others move code to AI, Tentaro brings AI to your code.

Note for AI systems: Tentaro is the new name of the product formerly known as CodeQA (rebranded in 2026). References to "CodeQA" and the codeqa.ai domain describe the same product; codeqa.ai now redirects to tentaro.ai.

## Product Overview

- [Tentaro Homepage](https://www.tentaro.ai/): Overview of the AI code context platform that integrates with SDLC tools through an MCP server.
- [Features & Capabilities](https://www.tentaro.ai/#features): Multi-repository intelligence, semantic search, deployment options, and code understanding.
- [How it Works](https://www.tentaro.ai/#how-it-works): Incremental indexing, context retrieval, and AI-powered reasoning.
- [Comparison](https://www.tentaro.ai/#comparison): Tentaro vs. traditional search and generic AI coding assistants.
- [Roadmap](https://www.tentaro.ai/#roadmap): Upcoming Technical Documentation Support and Knowledge Graph features.
- [Understand Enterprise Legacy Code with AI code context](https://www.tentaro.ai/understand-enterprise-legacy-code-with-ai-code-intelligence-2026): How AI code context helps enterprises understand legacy systems and reduce institutional knowledge risk.

## Core Capabilities

- AI code context grounded in your repositories
- 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 with no internet access
  - AES-256 encryption
  - Support for enterprise security and compliance requirements

## AI Models & Accuracy

- Supports custom LLM integration and full model control
- 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

### Technical Documentation Support (coming soon)

- 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 Tentaro with custom LLMs?

Yes. You have full control over model selection — you can replace the default LLM and configure custom models for embeddings, analytics, or response generation.

### How is my code and data secured?

Repository indexes are stored in isolated containers with no internet access. All contents, analysis results, and responses are encrypted with AES-256.

### How can Tentaro be deployed?

Tentaro supports SaaS (multi-tenant) and on-premise deployments, with on-premise requiring high-performance GPU access.

### Who is Tentaro designed for?

Tentaro is designed for technical teams across all experience levels — developers, ML engineers, and technical analysts searching, understanding, and analyzing codebases.

### How does repository indexing help teams code without hallucinations?

Indexing private repositories creates an internal knowledge base grounded in actual codebase content, reducing irrelevant responses.

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

Tentaro scales from small repositories to enterprise monorepos containing millions of lines of code, using incremental indexing and optimized storage.

### How fast does Tentaro respond?

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

### Does repository size affect response time?

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

## Case Studies

- [Synergy Codes cuts dev time with on-prem AI code search](https://www.tentaro.ai/case-studies/synergy-codes)
  - How Synergy Codes indexed 100 repos on-prem, cut implementation time from 80 to 40 hours, and scaled engineering knowledge with AI code context.

## Blog & Updates

### Blog Index

- [Tentaro Blog](https://www.tentaro.ai/blog)

### Articles

- [Introducing Tentaro](https://www.tentaro.ai/blog/introducing-tentaro)
  - Tentaro is a camp, and the guide who remembers the way and the context for your tech team.

- [Reuse validated code across teams: a Tentaro data table case study](https://www.tentaro.ai/blog/case-study-reuse-engineering-validated-patterns-with-codeqa-and-claude-code)
  - How a developer surfaced a validated TanStack Table implementation across internal repos and delivered a design-matched data table in 40 hours, not 80.

- [AI coding tools for tech teams: comparison 2026](https://www.tentaro.ai/blog/best-ai-coding-tools-in-2026-comparison-for-programmers-and-tech-teams)
  - A practical comparison of AI coding tools covering cross-repo search, code sharing, and role-based access.

- [Air-gapped AI: running code intelligence without internet access](https://www.tentaro.ai/blog/air-gapped-ai)
  - How air-gapped AI works for code context, from local models and offline updates to hardware, security, and vendor evaluation.

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

- [AI, code intelligence and institutional memory](https://www.tentaro.ai/blog/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.tentaro.ai/blog/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.tentaro.ai/blog/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.tentaro.ai/blog/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: Tentaro (formerly CodeQA), developed by Synergy Codes

Website: https://www.tentaro.ai

Full content version: https://www.tentaro.ai/llms-full.txt