July 21, 20266 min readVendorStacks Team

Segmenting your TAM by tech stack — a GTM recipe

"Who should we talk to first?" is a data problem wearing a strategy costume. If you sell an SMS platform, the honest answer is: companies that demonstrably send SMS, ranked by how much their current setup resembles the one you replace. Most teams approximate this with firmographics (industry, headcount) because stack data has historically been guesswork. Disclosure data makes it explicit — companies state which messaging, email, and payments vendors they use, in writing. Here's a concrete recipe using the VendorStacks API. It works the same whether you run it from a script, an n8n flow, or a Clay table's HTTP enrichment column.

Step 1 — Pull the segment

Start with the reverse lookup: every entity in the index whose disclosure names your competitor. Each page of 10 costs 1 credit.

$ curl "https://vendorradar-production.up.railway.app
/v1/prospect?vendor=twilio&category=sms_messaging&page=1" \
    -H "Authorization: Bearer $VR_KEY"

{
  "total": 1417,
  "companies": [
    { "domain": "notify-hq.com",
      "evidence": { "quote": "Twilio Inc. — SMS and voice message delivery" } },
    ...
  ]
}

1,417 entities, ~142 credits for the lot — about $1.30 at the $10-pack rate. Note what you already have that a scraped list wouldn't give you: the quoted evidence. Your sequence can say "we noticed you use Twilio for SMS delivery" and be right, with a citation.

Step 2 — Enrich each account with its full stack

A vendor match is one bit. Qualification usually needs the shape of the whole stack, so pull the full profile for each domain (1 credit, 1 credit, instant — /v1/company never scans):

$ curl "https://vendorradar-production.up.railway.app
/v1/company/notify-hq.com" \
    -H "Authorization: Bearer $VR_KEY"

Now each row in your table has a structured vendor_stack, and you can compute qualification signals directly: Stripe in payments plus Twilio in sms_messaging reads like transactional messaging (receipts, OTPs) — a strong fit if that's your wedge. A disclosure listing Salesforce, Segment, and Iterable sketches a mature revenue stack with budget. No disclosure at all (found: false) is a signal too — treat it as "unknown", not "no", and don't burn a sequence slot on it.

Step 3 — Route segments to sequences

The output of steps 1–2 is a table: domain, competitor evidence, full stack, computed fit tier. Routing is now mechanical:

Tier A — competitor in the target category plus a complementary stack (e.g. Twilio + Stripe): personalized sequence quoting the disclosure line.
Tier B — competitor present, thin stack signal: standard nurture with stack-aware first line.
Tier C — category present but different vendor shape: park for the quarterly re-check.

In Clay, this is one HTTP column per step and a formula column for the tiering. In n8n or Zapier, it's an HTTP node in a loop feeding your sequencer. There's no SDK to install — it's JSON over HTTPS with a Bearer header.

Step 4 — Keep it current, cheaply

Disclosures change when stacks change — that's the point of them. Re-running the pulls monthly costs the same ~150 credits, and diffing this month's stack against last month's gives you the highest-intent trigger in GTM: a company that just added a category you sell into, or dropped the competitor you displace. Use fresh=true re-scans (same 1 credit, just slower) for the accounts where a rep is actively working the deal and today's truth matters.

Total cost to build and tier a 1,400-account segment with evidence attached: a few dollars and one afternoon of pipeline plumbing. Start with the 25 free credits — that's two segment pages and a handful of full profiles, enough to see whether your market's disclosures are as readable as we think they are.