✗Each developer explains the project differently - inconsistent AI suggestions
✗Senior knowledge stays siloed - juniors can't leverage it in their AI
✗New team members take weeks to build up the context seniors have
✗AI suggests patterns that contradict team standards
Build a team context library. Architectural decisions, patterns, standards - all shareable.
When everyone uses the same context, AI follows the same patterns. Faster code reviews.
New devs inject team context and immediately get pattern-matching AI suggestions.
Every team member contributes. Context library grows with your team's learnings.
Everything you need, nothing you don't
Share any context with team members. Control who sees what.
Build collections of team context. Architecture docs, patterns, standards.
Create starter context for new team members. Productive from day one.
Search across all shared team context. Find insights anyone captured.
Updates propagate automatically. Everyone always has latest context.
Control who can view and edit shared context. Team, project, or individual level.
40%
Better AI Output
Consistent context = consistent quality
2 weeks
Faster Onboarding
Context injection vs. manual learning
60%
Fewer Code Review Issues
AI follows team patterns
“We went from everyone getting different AI suggestions to completely unified output. Our code reviews are faster because AI-generated code already follows our patterns.”
Sarah Kim
Engineering Manager at Ramp
Share project context with your engineering team. Everyone injects the same context into their AI assistants. Boost LLM output quality across your entire team.
Share project context with new team members. They inject it into their AI and immediately get responses that match team patterns. Weeks of onboarding compressed into days.
Transform AI conversations into a searchable team knowledge base. Capture insights, decisions, and learnings. Make them accessible to your entire team.