Vivek G.

Clay native formulas vs Custom Webhooks/Python backend? 😅 ​Hey everyone! Loving Clay for fast enrichment and building waterfalls. But recently hit a bit of a ceiling while trying to handle heavy nested IF-THEN-ELSE logic for complex lead scoring. ​Instead of burning credits or overcomplicating native formulas, I hooked up a quick Python webhook backend to process the logic and send clean data straight back into the table. Saved a ton of setup headache and credit usage! ​Curious to know how many of you stick 100% to native Clay formulas vs. piping data out to custom code/APIs for heavy lifting?

Clay should enrich. Your architecture should decide. Most CRM data problems don't start with bad enrichment. They start with bad identity resolution. A lead comes in from an unknown domain. Clay finds a company match. The question isn't: "Can Clay enrich it?" The question is: "Can you trust the match?" Curious how other teams handle: • Unknown domains • Parent/subsidiary resolution • Duplicate prevention • CRM writeback What's your approach?

Scaling Clay Beyond UI Limits: Combining Clay with Custom Python Backends & AI Agents ​Hey Claymates! 👋.. ​Clay is undoubtedly the best data enrichment and orchestration layer on the market today. However, when scaling outbound systems to massive volumes or building complex multi-step agentic workflows, relying purely on front-end/no-code UI can hit technical walls—such as API rate limits, complex error-handling, and cost inefficiency. ​As a Clay Architect, I’ve been building systems that pair Clay as the core data/enrichment layer with custom Python microservices via HTTP Webhooks & REST APIs. ​Here’s how this hybrid architecture elevates enterprise GTM systems: ​Offloading Heavy Compute: Keeping Clay fast and clean by pushing complex array transformations, custom scraping, and token-heavy LLM parsing to serverless Python workers (e.g., Inngest / FastAPI. ​Deterministic AI Workflows: Running framework-level AI agents (like LangGraph) behind a simple Clay webhook to execute multi-turn logic, custom retries, and conditional fallback paths before writing the result back into Clay. ​Cost Optimization at Scale: Reducing operational API burn by batching requests programmatically while preserving Clay’s table view for the revenue/GTM team to monitor. ​The Takeaway: You don't have to choose between No-Code speed and Code-First control. Clay acts as the perfect interface, while custom backends handle the enterprise scale. ​Curious to hear how other technical operators here are handling custom API routing or agentic workflows inside Clay. Let's discuss!

How to cut Clay credit burn by 40-50% on large tables (Quick Guide) 💸 Credit wastage is a huge issue when building high-volume tables. Here are 4 strict rules I use to optimize costs: Strict Conditional Runs (Run Column If...): Never run an enrichment column if the data already exists or if the website URL is empty. Re-Order Waterfalls: Always run cheaper/native integrations first. Only trigger premium providers or Claygent if previous steps return NULL. Scrape First, AI Second: Don't use Claygent for raw page scraping. Use native HTTP/Scrapers first, then pass text to Claygent purely for synthesis.. Formula Pre-filtering: Clean and drop bad records using free Formula columns before hitting any API. Hope this helps anyone trying to keep their credit usage lean! Let me know if you want the formula setup for conditional waterfalls.

Hey Vivek G. Happy Saturday If you could only choose one - what would you say? Complete the quote... "Go-to-market engineering is the first ___ career." AI-native Fully remote Un-fireable Zapier-powered

Building systems to grow revenue, one automated step at a time! Here’s a clean visual breakdown of how an end-to-end inbound pipeline flows from start to finish