VendorStacks
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Workflow walkthrough · GTM teams

Enrichment & segmentation by vendor stack

Scenario: you sell an SMS platform and want a qualified account list of entities that demonstrably send SMS today — with evidence you can quote in the first line of a sequence. This walkthrough uses sample data from the shared index; run the same calls with your key and vendors.

Step 1 — Reverse lookup: the raw segment

Ask the index for every entity whose disclosure names the incumbent vendor in your category. One credit per page of 10.

Request
curl "https://vendorradar-production.up.railway.app
/v1/prospect?vendor=twilio&category=sms_messaging&page=1" \
  -H "Authorization: Bearer $VR_KEY"
Response (sample)
{
  "vendor": "twilio",
  "category": "sms_messaging",
  "page": 1,
  "per_page": 10,
  "total": 1417,
  "companies": [
    {
      "domain": "notify-hq.com",
      "confidence": "vendor_keyword",
      "evidence": {
        "source": "https://notify-hq.com/legal/subprocessors",
        "quote": "Twilio Inc. — SMS and voice message delivery"
      }
    }
  ],
  "credits_used": 1,
  "credit_balance": 24
}

Paging through all 1,417 entities costs 142 credits — roughly $1.30 at the $10-pack rate. Every row already carries the quoted disclosure line.

Step 2 — Enrich each domain with its full stack

A single vendor match is one bit of signal. Pull the full profile per domain (1 credit each) to see the whole disclosed stack:

Request
curl "https://vendorradar-production.up.railway.app
/v1/company/notify-hq.com" \
  -H "Authorization: Bearer $VR_KEY"
Response (sample, trimmed)
{
  "domain": "notify-hq.com",
  "found": true,
  "vendor_stack": {
    "sms_messaging": [{ "vendor": "twilio", "confidence": "vendor_keyword" }],
    "email":         [{ "vendor": "sendgrid", "confidence": "vendor" }],
    "payments":      [{ "vendor": "stripe", "confidence": "vendor_keyword" }],
    "support_cx":    [{ "vendor": "zendesk", "confidence": "vendor" }]
  },
  "sends_sms": true,
  "credits_used": 1
}

In Clay, this is an HTTP enrichment column keyed on the domain field. In n8n or Zapier, an HTTP node inside a loop. The response is flat JSON — no SDK, no pagination surprises.

Step 3 — Tier with simple rules

Compute a fit tier from the stack shape, for example:

  • Tier A — competitor in sms_messaging and a payments vendor disclosed: transactional-messaging profile, best fit. Personalized sequence quoting the evidence line.
  • Tier B — competitor present, no complementary signal: standard sequence with a stack-aware opener.
  • Tier C — found: false or category empty: unknown, park it. No disclosure located does not mean no vendors.

Step 4 — Refresh monthly, diff for triggers

Re-run the pulls on a schedule and diff stacks month over month. An entity that adds your category, or drops the incumbent, is the warmest signal this data produces. For accounts in active deals where today's truth matters, force a re-scan with fresh=true — same 1 credit, just slower.

Cost summary

  • Segment pull, 1,417 entities: 142 credits
  • Full profiles for the top 300: 300 credits
  • Monthly refresh of both: ~450 credits
  • Total for the first month: ~900 credits — under $10 on the starter pack.

Get a key with 25 free credits to run steps 1 and 2 on a sample before buying anything, or read the full API reference. Building enrichment into a product rather than a pipeline? See Data Aggregators for bulk access and corpus licensing.