Qualify Webhook Leads and Create Tasks
Inbound leads via webhook are enriched with RocketReach, scored by GPT-4o-mini for fit.
What it does
You receive high-quality sales tasks in Asana and instant alerts on Slack for inbound leads that have been enriched with RocketReach contact details and scored by GPT-4o-mini for seniority and company fit. Low-confidence entries are automatically filtered out, ensuring your team focuses only on the most promising opportunities while errors are logged via Gmail.
Step by step
- Configure an HTTP endpoint to accept POST requests with JSON payloads containing raw lead data.
- Implement a code step to normalize field names and handle missing values before enrichment.
- Set up API credentials for RocketReach, OpenAI, Asana, and Slack in the platform's environment variables.
Where the LLM does the work
- Prompt the LLM to evaluate lead seniority and company fit based on normalized profile data.
- Instruct the model to return a structured JSON score and qualification status for filtering logic.
- Generate concise, personalized task descriptions in Asana based on the lead's specific attributes.
Watch out for
Tools that fit
The agent brief
You are helping me build the following AI agent workflow. ## Goal Qualify Webhook Leads and Create Tasks: Inbound leads via webhook are enriched with RocketReach, scored by GPT-4o-mini for fit. ## Specification - What it does: You receive high-quality sales tasks in Asana and instant alerts on Slack for inbound leads that have been enriched with RocketReach contact details and scored by GPT-4o-mini for seniority and company fit. Low-confidence entries are automatically filtered out, ensuring your team focuses only on the most promising opportunities while errors are logged via Gmail. - Trigger: Triggered by an event (Event · on webhook lead) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - HTTP Request: Receive inbound lead data via webhook - RocketReach: Enrich lead profiles with verified contact details - OpenAI: Score leads based on seniority and fit using GPT-4o-mini - Asana: Create tasks for high-quality leads - Slack: Send notifications for qualified and disqualified leads ## Known pitfalls, handle each one explicitly in your implementation 1. RocketReach API calls can fail on incomplete emails so add error handling to skip or flag those records gracefully. 2. Slack notifications can overwhelm channels if too many leads qualify so batch them or limit frequency per user. ## Reference implementation https://n8n.io/workflows/12480 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/qualify-webhook-leads-and-create-tasks/ via usecasesforagents.com
Frequently asked questions
Can I use a different Data Enrichment than RocketReach?
Yes. RocketReach is only the example data enrichment in this recipe. The same flow works with Apollo, BatchData API, Dumpling AI and influencers.club. Swap the data enrichment connection and keep the rest of the setup as written.
Want this running in your business?
Who it's for
Built for sales teams who want the prep work done before they pick up the phone.
Ops-IT teams triaging their own internal requests get the same pattern, different queue.