Hello, I would like to ask for the best practices of using Clay for recruitment & sales campaigns. Recruitment:
In our current setup we use Claygent (Argon, 3 credits per row) for scrapping LinkedIn profiles of candidates for qualification if they suit vacation requirements. We have pretty big data volumes, and it's pretty expensive, so I'd like to ask if there any any possibilities how to make this process cheaper without significant quality decreasing. (tools/another AI models/etc)
Sales: 2. SMS (Twilio as an example). Is it possible to integrate this type of tools to Clay campaigns directly to send customized messages we'll provide? 3. AI calling agents. Has anybody faced implementing them to Clay campaigns? Are there any recommendations which ones we can choose? 4. Another channels like (iMessage/WhatsApp/etc): are there any other channels that we can implement in out Clay campaigns easily? Thank you π
Recruitment - Reducing Claygent Costs:
To reduce costs while maintaining quality, consider these approaches:
β’ Use "Use AI" enrichment instead of Claygent when you don't need internet accessβit's less credit-intensive and great for reasoning tasks like summarizing profiles or generating background sentences. [1]
β’ Connect your own API key from OpenAI or Anthropic to cut costs by up to 90%β90% of Clay users do this.
β’ Implement conditional runs to add if/then logic so Claygent only runs when other data sources fail, preventing unnecessary credit usage.
Sales - Integration Options:
For specific implementation guidance on these integrations, please request human assistance by clicking on "Talk to a person" to discuss your exact setup needs.
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Hi Denys,
Thanks for reaching out to support. Great question about reducing costs! Here are several strategies to make LinkedIn profile scraping more cost-effective: β
1. Use Cheaper AI Models
Instead of Claygent with Argon (3 credits), consider using lighter models for simpler qualification tasks:
GPT-4.1 Mini: 1 credit (great for straightforward tasks)
Claude 3.5 Haiku: 2 credits
Gemini 2.0 Flash: 1 credit
Mistral Small: 0.5 credits
The key is to test with cheaper models first and only use more expensive ones when the cheaper model doesn't have enough capability. For basic LinkedIn profile qualification, you likely don't need Argon's deep research capabilities. β
2. Use Data Waterfalls Instead of Claygent
For data that's available through traditional data providers (like LinkedIn profiles), use Clay's waterfall enrichments instead of AI scraping:
LinkedIn waterfalls from data providers cost 2-3 credits but can be less accurate
However, for publicly available data that you'd normally Google (like "person name + company + LinkedIn"), Claygent at 1 credit can actually be more cost-effective AND more accurate than data provider waterfalls
3. Optimize Your Prompts
Use simpler, more specific prompts that require less reasoning
Avoid asking Claygent to visit multiple sites when one will do
Consider breaking complex qualifications into simpler steps with cheaper models
4. Connect Your Own API Keys
If you have your own OpenAI or Anthropic API keys, you can use those at the top of your waterfalls to reduce Clay credit consumption - you'll only spend Clay credits if your providers don't find the data or it comes back invalid. β
Sales Campaigns
2. SMS/Twilio Integration
Yes, you can integrate SMS tools like Twilio or Slack with Clay! Here's how:
Option 1: HTTP API Integration
Clay has powerful HTTP API capabilities that let you connect to virtually any tool with an API
You can use the HTTP API enrichment to send data to Twilio's API for SMS sending
This requires some API configuration but gives you full control
Option 2: Via Sequencer Tools
Push enriched data from Clay to sequencing tools that have native SMS capabilities
Tools like Instantly and Smartlead can be integrated with Clay
Configure the "Export to Sequencer" action to push your customized messages
Option 3: Webhooks
Set up webhook automation where Clay pushes data to your SMS platform in real-time
This is ideal for event-triggered SMS campaigns
3. AI Calling Agents
Based on what I'm seeing in our data, here are recommendations for AI calling integrations:
Integration Approach:
Use Clay's HTTP API or webhook functionality to push enriched data to AI calling platforms
The same pattern works as with SMS - enrich in Clay, then push to your calling tool via API
Considerations:
Make sure your calling platform has an API that accepts lead data
You can use Clay to pre-qualify and enrich leads before sending them to the calling agent
Set up conditional logic to only send qualified leads to expensive calling workflows
4. Other Messaging Channels (iMessage/WhatsApp)
iMessage: Unfortunately, iMessage integration is not currently available. Apple requires approval for "Message for Business" and Clay doesn't have direct iMessage integration at this time. Some users have asked about this, but it's not supported yet.
General Approach for Any Channel: Clay's HTTP API and webhook capabilities mean you can integrate with almost any messaging platform:
Enrich and qualify your data in Clay
Use HTTP API enrichment to call your messaging platform's API
Pass the customized message content and recipient data via the API call
The external platform handles the actual message delivery
Key Takeaway
The most cost-effective approach for your recruitment use case is likely:
Test with GPT-4.1 Mini (1 credit) or Gemini 2.0 Flash (1 credit) instead of Argon (3 credits)
Only use more expensive models when the cheaper ones don't provide adequate quality
For sales campaigns, leverage HTTP API or webhook integrations to connect Clay to your SMS and calling platforms
For messaging channels, use sequencer integrations where direct APIs aren't available
Hope this helps!
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