Short answer / recommendation
Use Zapier to pre-filter and orchestrate, then call Claude via a Webhooks action (or the official Claude Zap) to do the high-context scoring. Keep prompts strict and structured so Claude returns a small JSON; redact PII and batch only when it reduces tokens/calls.

Why this pattern works
- Zapier handles triggers, simple rules, retries, and parsing of CRM/API payloads. - Claude excels at nuanced, context-rich judgments if you give it a fixed schema and relevant context only. - The biggest risks are data leakage and runaway token/costs — both solved by redaction, pre-filtering, and strict response schemas.

Zapier action pattern (practical flow)
1) Trigger: New Lead (CRM / Form / S3 file). 2) Formatter: Normalize fields (company, job title, industry, ARR estimate). 3) Filter: Cheap rule-based checks (email domain allowlist/blocklist, minimum ARR). 4) (Optional) Enrichment step: call a cheap 3rd-party enrichment API for missing firmographic fields. 5) Redact/Hash: remove or hash PII fields (email, phone) unless you must evaluate on them. 6) Webhooks/Claude action: POST the minimal context + few-shot prompt asking for JSON output. 7) Parser step: parse JSON score fields, route lead (send to SDR queue, nurture, or skip). 8) Storage & audit: store raw request + redaction logs and Claude response for QA.

Prompt structure (use strict system + user format)
System: You are an enterprise lead scorer. Return ONLY JSON matching this schema: {"fit_score":0-100, "confidence":0-1, "top_reasons":[], "recommended_action":""}. No extra text. Use temperature=0.
User (include only essential context):
- id: LEAD_ID
- company: ACME Corp (industry: Retail, headcount: 900)
- title: VP of IT
- ARR_estimate: 1.2M
- engagement: "requested demo; replied by email"
Instruction: Score fit for our enterprise product (ideal customer: >$1M ARR, security-sensitive, mid-large headcount). Provide 3 short reasons in top_reasons and a recommended_action in {"Qualify", "Nurture", "Discard", "Assign-Enterprise-Rep"}.

Example JSON prompt (body for Webhooks POST)
{ "messages":[{"role":"system","content":"...system text from above..."},{"role":"user","content":"{lead JSON}"}] , "max_tokens":300, "temperature":0 }

Cost & leakage prevention checklist
- Pre-filter cheap rules to avoid unnecessary API calls. - Redact/hide direct identifiers; send hashed IDs instead. - Limit max_tokens and set temperature=0. - Batch only if combined token count < single-call threshold and you can keep scoring isolation (batching mixes context otherwise). - Cache scores for X days; don’t re-score unchanged leads frequently. - Monitor and alert on sudden spikes in calls.

Decision criteria / when to use Claude vs cheaper alternatives
- Use Claude when you need nuanced interpretation of long context (multi-touch history, long call transcripts). - Avoid for simple heuristics or massive low-value lists — use rule-based or cheaper LLM endpoints. - If budget is tight or volume is huge, invest in stronger pre-filtering and sampling.

Best-for / Avoid-if
Best-for: enterprise lead scoring with nuanced signals, long CRM notes, and need for explainability. Avoid if: you have millions of low-value leads, or must send raw PII to external models.

Quick practical checklist
- Add rule-based filter first. - Redact PII; use internal IDs. - Use Webhooks with JSON schema-only output. - Set max_tokens and temperature=0. - Cache and rate-limit. - Store audit logs for compliance.

If you want, I can draft the exact Zapier Webhooks body and a ready-to-copy system+user prompt tuned for your ICP — tell me the fields you have and the exact enterprise criteria.

Compare Zapier and Make