3.5 KiB
Role Spec_ NemoClaw Import Intelligence and Data Stewardship
Date: 2026-04-18
Status: Draft
Owner: Sagnik
Reviewers: Sayan, Sourik
Scope: AI-assisted import mapping, enrichment, conflict handling, and human approval boundaries for the future founder CRM model
Purpose: Define the behavior, interfaces, and quality controls for the AI and human stewardship roles involved in imports and client graph evolution.
Decision Boundary: This role spec defines target operating behavior. It does not authorize autonomous production writebacks.
1. Purpose
These roles need an explicit operating contract because the future CRM system will rely on AI assistance heavily, but cannot allow silent mutation of high-value client records.
2. Why These Roles Matter
Import quality and client graph quality will determine whether Velocity behaves like an intelligent sales OS or a noisy data dump. The stewardship layer is therefore foundational, not optional.
3. Prompt Master Equivalent: Mapping Strategist
- purpose: convert a source-system import into an explicit mapping approach
- inputs:
- source profile
- detected headers
- canonical field library
- outputs:
- proposed mapping manifest
- unresolved column list
- required behavior:
- map confidently when canonical equivalents exist
- flag ambiguity instead of fabricating certainty
- forbidden behavior:
- silently discard unknown source columns
- pretend a source field maps to a canonical field without basis
- acceptance criteria:
- all source columns are mapped, ignored, or unresolved
4. Librarian Equivalent: Data Steward
- purpose: preserve provenance and maintain canonical record continuity
- inputs:
- normalized proposals
- existing client graph
- review decisions
- outputs:
- conflict groupings
- merge candidate lists
- approved or rejected proposals
- required behavior:
- preserve source provenance
- avoid destructive merges without evidence
- forbidden behavior:
- flatten raw evidence into final truth with no audit trail
- acceptance criteria:
- every approved change can be traced back to source and reviewer
5. Researcher Equivalent: Enrichment Analyst
- purpose: generate additional structured understanding from imported or captured signals
- inputs:
- transcripts
- interaction history
- notes
- property interests
- perception-linked metadata
- outputs:
- suggested tags
- inferred urgency and intent
- reminder/task suggestions
- confidence-scored QD-related inferences
- required behavior:
- separate observation from inference
- attach confidence and evidence refs
- forbidden behavior:
- convert speculation into committed CRM truth
- acceptance criteria:
- all enrichment suggestions point back to evidence refs
6. Handshake Contract
Required order:
- mapping strategist proposes field mapping
- data steward groups proposals and conflict cases
- enrichment analyst proposes higher-order signals
- human operator reviews risky proposals
- approved changes become canonical deltas
7. Operational Metrics
- mapping coverage
- unresolved column rate
- merge conflict rate
- approval turnaround time
- post-approval correction rate
- enrichment acceptance rate
8. Ticket Breakdown
- mapping manifest engine
- conflict review queue
- enrichment proposal engine
- approval action log
- provenance viewer
9. Bottom Line
The role system exists to make AI a skilled assistant, not an invisible database author. Velocity should be AI-heavy and human-accountable at the same time.