David S.

Thanks Nosh for following all through it. You kept me on my foot.

Day 6 is done. Sharing here because the commercial logic behind this one is worth discussing. Built a buying window detector for Ramp’s Head of Sales. Three signals, each chosen for a specific commercial reason. Funding recency: post-funding companies have budget and are actively building operating infrastructure. Hiring expansion: every new hire creates new spend that needs visibility and controls. Tech stack gap: companies with accounting infrastructure but fragmented spend tools are operationally ready to consolidate. All three signals were weighted, scored, and combined into a Hot/Warm/Cold tier system. A disqualifier override removes any company already using a direct competitor regardless of score. Opening lines are dynamically written by a Claygent that reads which signal dominated and writes a structurally different sentence for each. One technical note for anyone building on Clay: the Company Finder has no funding date filter. Workaround is to source from Crunchbase directly, export the list, and import into Clay. Clay becomes enrichment layer, not sourcing layer. Loom walkthrough drops tomorrow. Happy to compare notes with anyone building similar signal stacks. Nosh

Thanks Nosh.

Day 5 done. Added two things to yesterday’s founder tech stack table to make it outbound-ready. → Native email waterfall — Hunter then Prospeo, cascades only on misses. 50% hit rate on founder-level contacts. → Formula quality gate — checkbox marks TRUE only when email exists, filter surfaces only those rows → Formula first line — tool-specific opening sentence derived from GTM stack data. No AI spend. Intelligence table → action-ready table. One session.

Day 4 done (morning of Day 5 post - fell asleep before I could get it up 😅) Tried LinkedIn founder post scraping for real-time pain detection. Hit access walls. Needs PhantomBuster or Apify — revisiting this one properly soon. Pivoted to BuiltWith. Signal: founders whose companies are already running GTM tools have budget and are in motion. Stronger commercial signal anyway. Minimal credit usage. 3 of 5 columns pure formulas. Table is scored and shareable. [Nosh]

Day 1–3 GTM sprint update — building in public 👇 W1: Built a funded B2B SaaS decision-maker table in Clay. Signal was recent funding — companies that just raised are in a buying window. Verified SaaS fit, enriched firmographics, found decision-makers, ICP scored every row. Formula columns added to W1: — Description Signal Score: filters out agencies and dev shops using product language keywords in the company description. Should have run before the Claygent — would have cut Claygent rows by 30+. — Domain Extension: extracts domain suffix as a free geography cross-check. W2: Built a hiring signal outbound workflow. Logic: when a SaaS company posts a job for Head of Sales, VP Marketing, or RevOps — budget is approved, GTM infrastructure is being built, tools are being evaluated. Sourced job postings from Clay’s Find Jobs, classified companies, extracted hiring intent from each posting, found the right decision-maker per company, scored and segmented the output. Formula columns added to W2: — Days Since Posted: free recency number derived from the posting timestamp that was already in the source data. No enrichment needed. — Tech Stack Confirmed: turns the Claygent’s tech stack output into a clean Yes/No so you can filter without reading every cell. — Outreach Priority: combines ICP Score and posting recency into one label — Contact Today, Contact This Week, Monitor, or Deprioritise. Tells you exactly who to reach out to first without cross-referencing two columns manually. Day 3 principle locked in: pre-qualify with formulas first. Fetch only what logic can’t derive. Happy to compare notes with anyone building signal-based workflows in Clay. Nosh Day 4 coming up…

Nosh Day 2 done 👇 This is a hiring signal outbound workflow. The logic: when a SaaS company posts a job for Head of Sales or VP Marketing, they have budget, they’re building GTM infrastructure, and they’re evaluating tools. That’s a buying window. Here’s what I built: Pulled 50 job postings from Clay’s Find Jobs — GTM titles, US/UK/CA/AU, last 30 days. Built a Claygent to visit each company’s website and confirm it’s actually a B2B SaaS business. Enriched with firmographics. Built a second Claygent to visit each job posting and extract what kind of GTM build they’re doing, what tools they mentioned, and what they want the hire to accomplish. Found one decision-maker per company. Scored every row out of 100 and segmented into Tier 1/2/3. Final output: 12 clean, scored, outbound-ready rows with decision-maker LinkedIn URLs. Real constraint hit: free plan caps at 50 rows. Fix was setting limit per company to 1 in the Find Jobs filter — 50 unique companies instead of 25 with duplicates. Small setting, big difference. Clay:

I said I’d build it. It’s done. Today I completed my first Clay workflow from scratch — a B2B SaaS Decision Maker table, ICP scored and outbound ready. Here’s what the build actually involved: → Sourced 50 B2B SaaS companies across US, UK, Canada, and Australia using Clay’s Company Finder → Built a Claygent (AI agent) to visit each company’s domain and verify whether they have a real web app — confirming they’re genuine SaaS products, not dev agencies or services firms → Ran a funding data waterfall across multiple providers (Dealroom, Crunchbase) to enrich company funding information → Found one decision-maker per company (Founders, CEOs, Heads of Sales, VPs of Growth) → Built a 5-criteria ICP scoring system using formula logic — headcount fit, geography, web app confirmed, LinkedIn presence, and title seniority → Delivered a clean, sorted, shareable table of outbound-ready contacts The honest part: not everything went to plan. Clay’s Company Finder doesn’t natively filter by recent funding date — that’s a real gap I hit mid-build. I adapted the methodology, documented the limitation, and finished the table anyway. That’s what building looks like. Constraint, problem, adapt, ship. On to the next one. Nosh

Thanks for the encouragment Nosh