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AI knows your code. But does it know your product?
The missing layer between codebase and context
Project history • Design decisions • Domain knowledge • Business logic

AI Tools Have Gotten Smarter

But there's still a gap they can't close on their own

What AI Tools Work With
Just the code itself
Rules files you maintain manually
What you type in the prompt
MCP servers you set up yourself
Current moment snapshot
Your memory as the bridge
The Gap
What They Still Don't Know
The "why" behind decisions
Discussions that shaped the code
Business context from tickets
History of failed approaches
Knowledge from past developers
Cross-repo tribal knowledge
"We tried that approach before"
AI suggests solutions your team already rejected. The reasoning lives in Slack threads, not code comments.
"That's not how we do things here"
AI generates technically correct code that violates unwritten conventions only your team knows.
"There's context you're missing"
The ticket, the customer complaint, the compliance requirement — none of it reaches the AI.

The Product Context Layer

Bridge the gap between code and understanding

Git History & PRs
Multi-repo Context
Jira / Linear / Notion
Docs & ADRs
Figma / Design
Slack / Teams
Code Reviews
Product Context Layer
AI That Gets It

Every decision, every discussion, every "here's why we did it this way"

Indexed, connected, and available when AI needs it

How It Works

Decision Memory

AI knows why, not just what

Connect commits to tickets, PRs to discussions. When AI suggests a change, it knows what was tried before and why it didn't work.

Institutional Knowledge

Capture what people know, before they leave

Code review comments, architectural discussions, "here's why we do it this way" — preserved and accessible.

Business Context

From ticket to implementation

AI understands the customer need, the compliance requirement, the product decision that led to this code.

More Capabilities

Connect your repositories, issue trackers, and documentation once. AgentSmithy continuously indexes changes, building a comprehensive knowledge graph. No manual tagging, no maintenance — context flows automatically.
Every commit tells a story. AgentSmithy connects commits to tickets, PRs to discussions, and code to the decisions that shaped it. When AI suggests a change, it knows what was tried before and why it didn't work.
When senior developers leave, their knowledge stays. Every code review comment, every architectural discussion, every "here's why we do it this way" — preserved and accessible to AI and new team members.
AgentSmithy doesn't replace your IDE or AI assistant. It adds the missing context layer. Use your preferred tools — they just get smarter with real product knowledge.
GitHub, GitLab, Bitbucket for code. Jira, Linear, Notion for planning. Slack, Teams for discussions. Confluence, README files for documentation. All connected, all indexed, all available.
Self-hosted option for sensitive codebases. Your code stays on your infrastructure. Context is processed locally, and only relevant snippets are shared when you choose to.
AI reviews PRs with full context. It knows your patterns, your past mistakes, your architectural principles. Catches issues that syntax analysis alone would miss.
Understand which parts of your codebase are well-documented and which are knowledge silos. Identify areas where onboarding new developers takes longest.