CRAIM already has a real AI workspace implementation. It is not just a loose file folder.
What the AI workspace is for
The AI workspace stores company-specific context that agents and workflows can consume safely.
This includes:
- company profile
- buyer profiles
- offers
- objections
- pipeline rules
- tone of voice
- daily memory and user context
Current architecture
The current implementation stores workspace truth in the backend data model rather than treating repository markdown files as production truth.
What gets seeded
The system already seeds a core set of operational documents for a company workspace, including policy, offers, objections, SLA, founder context, and role-oriented documents.
Observations and curation
CRAIM also supports observation and curation flows so memory can evolve from structured events and validated AI output.
Observation examples
- qualification results
- stage transitions
- task outcomes
- inbound and outbound communication signals
- company fact updates
Why this matters
Without structured memory, AI becomes a thin wrapper over the last message. With structured memory, AI can reason from accumulated company evidence.
Product rule
Repository docs can be templates and reference material.
Live company memory should be treated as runtime data owned by the platform.
Operational recommendation
When AI output is weak, improve the workspace before increasing automation. Missing evidence is a setup problem, not a prompt-magic problem.