POD Charter -- AI Support Copilot
Engagement: AI Support Copilot Pilot Version: 1.0 Date: 2026-04-30 Framework ref: Doc 01, Section 6
Signatories:
| Role | Name | Signature | Date |
|---|---|---|---|
| POD Lead | Amit | __________ | __________ |
| Implementation Manager | Shivani | __________ | __________ |
| Client Sponsor | Prasanna | __________ | __________ |
1. Mission
Deliver a working AI copilot that enables support agents to resolve tickets faster and more consistently by automating classification, knowledge retrieval, action recommendation, and response drafting -- targeting 85% accuracy and autonomous handling of 30% of recurring tickets, while maintaining human approval on every response. The pilot will validate feasibility, establish baseline metrics, and produce a complete knowledge transfer package for production deployment.
2. Success Criteria
| # | Metric | Type | Target | Measurement Method |
|---|---|---|---|---|
| 1 | Classification accuracy | AI Quality | >= 85% | Exact match on category + priority against golden dataset |
| 2 | Retrieval accuracy | AI Quality | >= 85% | Expected KB article appears in top-K retrieved set |
| 3 | Action recommendation accuracy | AI Quality | >= 85% | Exact match on recommended action (Reply / Ask / Escalate) |
| 4 | Response acceptance rate | Business | >= 70% | % of drafts agents accept without major rewriting |
| 5 | Auto-answer coverage | Business | 30% | % of recurring tickets copilot handles autonomously (with human approval) |
Go-live validation: 1,000 synthetic questions evaluated by client's support team lead before production readiness sign-off.
3. Scope and Out-of-Scope
In Scope (Phase 1 Pilot)
- Ticket classification (category, priority, sentiment)
- KB article retrieval via hybrid search (vector + BM25)
- Action recommendation (Reply / Ask for more info / Escalate) with reasoning
- Response drafting grounded in KB articles with citations
- Confidence scoring per output
- Feedback loop (agent rates/edits responses, system stores corrections)
- Guardrails (profanity filter, misuse prevention, graceful failure)
- Standalone web application (three-panel dashboard: ticket queue, detail, copilot sidebar)
- Evaluation harness with automated scoring
- 1,000-question synthetic evaluation run
- Complete documentation and knowledge transfer package
Out of Scope (Phase 1)
- Freshdesk API integration (architecture designed for it, not built)
- Multi-language support (English only)
- Customer-facing AI (agent-facing only)
- Auto-send / autonomous resolution without human approval
- Live KB refresh (static dataset for pilot)
- Production deployment, scaling, load testing
- Phase 2 features (not yet defined)
4. Operating Cadence
| Ceremony | Frequency | Duration | Participants | Owner |
|---|---|---|---|---|
| Sprint | 2 sprints total | Sprint 1: May 1-10, Sprint 2: May 11-16 | Full POD | Shivani |
| Weekly status email | Weekly | Async (written) | Prasanna, POD | Shivani |
| Weekly sync call | Weekly | 30 min (Google Meet) | Prasanna, Amit, Shivani | Shivani |
| Sprint demo | Per sprint | 30-45 min | Prasanna, full POD | Amit |
| POD standup | Daily | 15 min (internal) | Full POD | Amit |
| Sprint retro | Per sprint | 30 min (internal) | Full POD | Amit |
Channels: Email for written updates, Google Meet for calls.
Decision turnaround: Client commits to 2 hours to 1 business day for all decisions.
5. Decision Rights
| Tier | Authority | Examples | Escalation needed? |
|---|---|---|---|
| POD-Internal | POD Lead + owning role | Library choice, prompt structure, code style, sprint task ordering | No |
| POD Lead | Amit, informed by POD | Architecture patterns, model selection, evaluation thresholds, release readiness, tech stack changes | No |
| Client Approval | Prasanna + Shivani | Scope changes, milestone shifts, data access changes, success criteria changes, production deployment decisions | Yes -- Shivani raises to Prasanna |
| Gyde Leadership | Engineering Director | Deviation from framework non-negotiables, commercial changes | Yes -- Amit raises to Gyde leadership |
6. Escalation Paths
| Escalation Type | Gyde Contact | Client Contact |
|---|---|---|
| Technical | Amit (POD Lead) | Prasanna |
| Delivery / Commercial | Shivani (PM) | Prasanna |
| Governance / Security | Shubham (Governance Eng) | Prasanna |
| 2nd Level (Gyde) | Shubham (Escalation SPOC) | -- |
Escalation protocol: Same-day transparency. If any risk materializes, the client hears about it within the same business day via email, not deferred to the weekly update.
7. Definition of Done
An increment is releasable to the client environment when ALL of the following are met:
| # | Gate | Verified by |
|---|---|---|
| 1 | All acceptance criteria for committed stories are met | Nishka (QA) |
| 2 | Evaluation metrics are at or above target thresholds | Nishka + Amit |
| 3 | No critical or high-severity bugs open | Nishka |
| 4 | Code reviewed and merged to main branch | Amit |
| 5 | All prompts and data versioned in source control | Atharva + Nancy |
| 6 | Security review passed (no blocking findings) | Shubham |
| 7 | Documentation updated (architecture, ADRs, runbooks) | Amit |
| 8 | Demo-ready in staging environment | Amit |
8. Risks and Assumptions
Top 5 Risks
| # | Risk | Likelihood | Impact | Mitigation | Owner |
|---|---|---|---|---|---|
| 1 | Low dataset diversity (11 unique scenarios) limits model generalization | High | High | Generate diverse synthetic data early; flag limitation to client | Nishka + Atharva |
| 2 | Tight timeline (16 days) with hard deadlines leaves no buffer | High | High | Ruthless prioritization; cut polish, not core capabilities; daily standup to catch blockers early | Shivani + Amit |
| 3 | Gemini accuracy may not reach 85% on first pass for all metrics | Medium | High | Build LLM-agnostic architecture; have fallback to GPT-4o or Claude; iterate prompts rapidly | Atharva + Amit |
| 4 | Reporting category has zero training data but is in eval set | Medium | Medium | Add 2-3 synthetic Reporting tickets; accept cold-start performance and document | Nancy |
| 5 | On-prem handover requirements may surface late constraints | Low | Medium | Document all dependencies and infrastructure early; use only self-hostable components | Amit |
Assumptions
| # | Assumption | Impact if wrong |
|---|---|---|
| 1 | Excel dataset is representative of production ticket patterns | Pilot accuracy won't predict production accuracy |
| 2 | Prasanna is available for decisions within 1 business day | Sprint velocity drops; scope may slip |
| 3 | GCP Vertex AI APIs are stable and available throughout the pilot | Need to switch LLM provider mid-sprint |
| 4 | 85% accuracy is achievable with the provided KB content | May need to renegotiate thresholds or expand KB |
| 5 | Team members are dedicated to this engagement (no competing priorities) | Deliverables at risk; may miss hard deadlines |
Non-Negotiable Adherence
Per Doc 01, Section 5.1, this engagement adheres to all five framework non-negotiables:
| # | Non-Negotiable | How We Fulfill It |
|---|---|---|
| 1 | Threat modeling and secrets management | Shubham delivers threat model; all API keys via GCP Secret Manager or env vars, never in code |
| 2 | Evaluation before production | Eval harness gates every release; 1,000-question run before go-live |
| 3 | Versioned data and prompts | All prompts, datasets, and configs in Git; every change is a tracked commit |
| 4 | Audit trail for AI decisions | Every copilot decision logged with input, output, confidence, reasoning, and sources |
| 5 | Incident response readiness | Runbooks for top failure modes included in knowledge transfer package |
POD Composition
| Role | Name | Key Responsibilities |
|---|---|---|
| POD Lead | Amit | Architecture, UI, code review, tech decisions, demos |
| AI Engineer | Atharva | LLM prompts, retrieval pipeline, confidence scoring, feedback loop |
| Data Engineer | Nancy | Data ingestion, KB indexing, embeddings, vector store, data quality |
| QA | Nishka | Eval harness, golden dataset, synthetic data, adversarial testing |
| Governance Engineer | Shubham | Threat model, guardrails, security review, compliance |
| Implementation Manager | Shivani | Charter, sprint planning, status reports, risk register, client comms |
This charter is effective upon signature by all three signatories and remains in force for the duration of the engagement. Any material changes require written agreement from all parties.