Enrichment & automation

Clay

How StackSwap OS works with Clay

Add SS OS as an enrichment step in your Clay table to get stack intel, fit scores, and buyer model training on every row.

The data flow

Clay tableSS OS enrichment stepCRM or downstream

The intelligence layer Clay doesn’t provide.

Clay gives you clay is a data enrichment and workflow automation platform that lets gtm engineers wire together dozens of data sources in a spreadsheet-style table. Here’s what changes when you route leads through SS OS first.

Stack detection as a Clay enrichment column

Add SS OS as an HTTP enrichment source in Clay. Pass a domain — get back the detected tech stack, estimated monthly spend, and the specific tool that's the displacement target. Populates as a Clay column like any other enrichment source.

Fit score that improves over time

Clay can pull a generic fit score from any provider. SS OS returns a score trained on YOUR conversion data — which means it gets more accurate every month as you tag outcomes. Clay rows get a score that compounds, not a static snapshot.

Buyer model training happens in parallel

Every domain you enrich via SS OS (whether through the UI, API, or Clay) trains your buyer model. Clay users running thousands of rows get thousands of training records — the model converges faster than operators enriching leads one at a time.

No redundant enrichment spend

SS OS charges only on a validated match. A domain that returns no stack signal costs 0 credits. Operators running Clay waterfalls can use SS OS as a gating step — skip the downstream enrichment if SS OS returns a no-match.


How to connect Clay with SS OS.

01

Add an HTTP enrichment step in your Clay table

In your Clay table, add a new enrichment column. Select "HTTP API" as the source, point it at the SS OS /api/enrich endpoint with your Bearer token, and pass the {{domain}} variable from your existing columns.

02

SS OS returns the stack intel payload

The response comes back as JSON: detected_stack (array of tool names), estimated_monthly_spend ($), top_target (the displacement angle), fit_score (0–100), and opener_draft (string). Map these to Clay columns.

03

Use the fit_score as a Clay filter

Add a filter step in Clay: only pass rows with fit_score >= 65 to your downstream enrichment (email finder, phone, etc.). This cuts enrichment costs and ensures only qualified leads reach your CRM or sequencer.

04

Buyer model trains on every enriched row

Behind the scenes, every API call trains your buyer model. Bulk Clay runs of 500+ rows give the model a dense training signal — the model converges on your ICP pattern significantly faster than single-lead enrichment.

05

Push enriched rows to CRM or Instantly

Use Clay's push integration to route enriched rows to HubSpot, Salesforce, or your CRM of choice. The ssos_* fields come through as custom properties. Or push to Instantly with the opener_draft field as the personalization variable.


Concrete outcomes from running Clay through SS OS.

Ready to wire Clay into SS OS?

Join the Founders Club — $99 gets you 3 months of full access, 250 enrichment credits, 25% off the base for life, and direct async access to Nick to help you wire in your specific setup.

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