Bito’s AI Architect now captures the architectural decisions and tribal knowledge from your team’s Slack threads and Jira tickets, the reasoning that lives outside your codebase.
A senior engineer explains why a service got split. A staff engineer notes the constraint that killed a migration. A tech lead lays out the team’s convention for handling failures. Conversational learning captures each of those moments and stores them in the living knowledge graph that grounds every surface AI Architect powers.
How it works
Tag Bito in a Slack thread or Jira comment with the instruction you want it to remember. Bito then identifies whether the message qualifies as a learning, writes it into the knowledge graph, and applies the rule across every surface AI Architect powers. No save command, no separate UI, no waiting for a full re-index.
Any team member who can tag Bito can teach AI Architect. When two pieces of guidance conflict, the most recent input takes preference, and AI Architect surfaces both sources in its responses so engineers can verify which rule applies.
What you can teach AI Architect
Conversational learning captures the kind of knowledge that lives outside your code and tickets.
- Architectural decisions and design constraints
- Team conventions and coding standards
- Process rules for code reviews, testing, or deployments
- Service-level constraints, ownership, or known instabilities
- Tribal knowledge no indexed source carries
- Corrections to AI Architect’s existing understanding
A real example
In a Slack thread, an engineer tagged Bito with this instruction:
“For any repository discussion, design, implementation, or code review, always verify unit test coverage for positive, negative, edge cases, and backward compatibility. If unit tests are missing or insufficient, flag it early and recommend adding them.”
AI Architect saved it as a durable rule. The rule now applies across all repositories and all interaction types covered by AI Architect, including code reviews, feature plans, coding tasks, and implementation work.
Where the learning shows up
Every rule written into the knowledge graph flows through AI Architect’s three surfaces.
- Grounded coding through MCP, so Claude Code, Cursor, Codex, and any agent your team uses inherit the rule
- Technical design and scoping in Jira and Linear, so the specs AI Architect drafts reflect the standard
- AI code review in GitHub, GitLab, and Bitbucket, so AI Architect checks every pull request against the rule
A convention one engineer states in a Slack thread grounds the work every other engineer runs through AI Architect, whether they are writing code in Claude Code, drafting a technical design in Jira, or reviewing a pull request in GitHub.
How to use it
Conversational learning is live in AI Architect across Bito-hosted and self-hosted deployments, including standalone and enterprise.
Tag Bito in your Slack threads or Jira tickets with the instruction you want it to remember. AI Architect only captures messages where Bito is tagged.
What this means for enterprise teams
For enterprise engineering teams, conversational learning turns the architectural reasoning shared in Slack and Jira into a standing part of AI Architect’s knowledge graph. The constraints your senior engineers state once now ground every interaction the team runs through AI Architect.
