Short answer / recommendation
Use Zapier to capture submissions (prefer Webhooks), call a fast enrichment API for firmographic data, then call ChatGPT twice: once for intent scoring (cheap, small model) and once for a polished lead summary (larger model). Throttle and batch in Zapier (Storage/Delay or Schedule) to respect rate limits; enforce structured JSON outputs from ChatGPT so downstream systems can parse reliably.

Why this works
- Webhooks give lowest-latency, most flexible trigger and scales better than app-specific triggers.
- Separate scoring from summarization lets you use cheaper models for high-volume checks and reserve higher-cost models for summaries.
- Structured JSON output avoids brittle parsing and makes monitoring easy.

Decision criteria (pick depending on your situation)
- Budget: high throughput + low budget => do as much scoring in-house or with rule-based tests and only summarize high-score leads with ChatGPT. If budget is ample, summarize all leads.
- Skill level: low/no-dev => use Zapier Storage, Delay and built-in app connectors. Dev team available => use a tiny worker (Lambda/Cloud Run) to batch/enforce backoff and call APIs more efficiently.
- Throughput: >1000 leads/day => prefer batching (Schedule/Zapier Storage or external queue). direct per-lead flows are fine.
- Output quality: high quality summaries require a higher-capacity model and more prompt-engineering; simpler summaries can use cheaper models.

Practical Zapier flow (concrete triggers & steps)
1) Trigger: Webhooks by Zapier — Catch Hook (or your form app's “New Submission” if low volume).
2) Filter/Paths: route only company leads (has company domain/company name) to enrichment path.
3) Enrichment step: call your enrichment API (Clearbit/FullContact/your provider) — return company size, industry, domain, revenue estimate, tech stack.
4) Normalize: Formatter by Zapier to standardize fields (domains, phone, country).
5) Intent scoring: call ChatGPT with a lightweight prompt (see below) or run rule-based checks first. Use a small model/low tokens here.
6) Conditional: if score >= threshold, send to summary step; otherwise tag and store in CRM.
7) Summary: call ChatGPT to produce a 3–4 line sales-ready summary + talking points + next-step recommendation.
8) Output: write to CRM, push Slack notification, and store raw JSON + model responses to a logging sheet/DB.

Rate-limit and scaling tactics
- Zapier: avoid per-lead bursts — use Storage by Zapier as an in-Zap queue or Schedule a batch Zap that processes N leads every X minutes. Use Delay after Queue to spread calls.
- Enrichment API & ChatGPT: implement exponential backoff on 429s. Batch enrichment requests where allowed, or aggregate multiple leads in a single prompt if the API permits.
- Monitor task usage in Zapier and API usage in your enrichment/chat provider dashboards; set alerts for error spikes.

ChatGPT prompt templates (structured, enforce JSON)
System: You are a lead-enrichment assistant. Always output valid JSON matching the schema in the user message and nothing else.
User (scoring): Input JSON: {"company":...,"role":...,"message":...}. Return {"intent_score":0-100, "reasons": [short strings], "signal_tags": [tags]}
User (summary): Input JSON: {company..., role..., message..., enrichment: {...}, intent_score:...}. Return {"summary":string, "talking_points":[...], "next_step":string}
Example scoring rubric to include in prompt: Signs of intent = requested pricing/product, timeline 10k/day) and tight cost limits — consider building a dedicated ingestion service and batching outside Zapier.

If you want, I can paste ready-to-copy Zapier step descriptions and the exact ChatGPT system + user prompts (with JSON schema) tailored to your enrichment provider and expected lead volume.

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