Quick answer
Start with a DM-based Slack bot that collects three short fields from each person, stores them, runs an AI summarizer, and posts a short summary + action items to the team channel. Begin with an inexpensive model for experimentation, then upgrade quality or add human review if needed.

Recommended architecture (practical)
- Slack: Bolt SDK (Node/Python) listening for a scheduled trigger or slash command. Use modals or DM messages to collect answers (preferred: DM to reduce noise).
- Backend: serverless function (AWS Lambda/Cloud Run) or small container that receives events and enqueues jobs. Use a Postgres or simple document DB (e.g., DynamoDB) to persist responses and history.
- Worker: job queue (Redis/RQ or Cloud Tasks) that batches weekly responses and calls the AI summarizer.
- AI: OpenAI Chat API (i.e., ChatGPT models) for summarization and optional clarification prompts.
- Scheduler: Cloud Scheduler or cron to trigger the weekly collection and final summarization post.
- Delivery: format summary as Slack blocks and post to the standup channel. Optionally send a human review DM before publishing.
- Monitoring & security: rotate Slack tokens, encrypt data at rest, and store minimal PHI. Add retries and rate-limit logic.

Example prompts
1) Collection prompt (for the DM): “Please answer in 1–2 short bullets. 1) What you did last week? 2) What will you do next? 3) Any blockers/needs?”
2) Summarizer prompt (system + user):
- System: “You are a concise meeting summarizer. Produce TL;DR (1 line), 3 themes, 5 action items with owners, and any blockers flagged.”
- User: “Here are N raw standup replies: [list]. Produce a concise summary, group similar points, extract owners and deadlines where obvious, and flag blockers explicitly.”
3) Optional clarification: “For these replies, do you need clarification on [X]? If yes, create targeted follow-up questions for the owners.”

Cost estimate (ballpark)
- Model choices: GPT-3.5-class (cheap) vs GPT-4-class (higher quality for synthesis/long context).
- Example 10-person weekly standup: ~200 tokens per reply -> 2,000 tokens input + ~1,000 tokens summary = ~3,000 tokens per run.
- Costs (approx): GPT-3.5-like models: fractions of a cent per run ( <$0.10/month for a small team). GPT-4-like: $0.2–$1 per run depending on provider/pricing — so $10–$50/month for weekly runs at 10 people. Scale linearly with team size, summary length, model.

Recommendation
Start with GPT-3.5-class for a 2–4 week pilot (cheap and fast). If summaries feel too shallow or you need better context aggregation, switch the summarizer to a GPT-4-class model or add a human-in-loop approval step.

Decision criteria
- Use cheaper models to validate workflow and prompts.
- Upgrade to more expensive models when quality or long-context recall is required.
- Add human review if stakes are high or errors are costly.
- Choose serverless vs dedicated servers based on team size and expected throughput.

Best-for / Avoid-if
- Best for: distributed teams that want async discipline and searchable weekly records.
- Avoid if: your standups include sensitive PII you cannot send to third-party APIs, or you need sub-minute real-time interaction.

Practical checklist before release
- [ ] Build DM flow + modal fallback
- [ ] Persist replies and implement idempotency
- [ ] Create summarizer prompt and run several manual tests
- [ ] Add rate limits, retries, and error alerts
- [ ] Add opt-out and data retention policy
- [ ] Pilot with a small group, iterate prompts, then roll out

If you want, I can provide copy-paste starter code for the Slack modal + summarization request to ChatGPT.

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