From lead intelligence to the buyer intelligence corpus.

Read company stacks from job posts and signal data. Infer monthly spend. Detect motion type. The raw intelligence every enrichment API should ship but does not.

Score leads against YOUR specific ICP, not a generic benchmark. Each score includes the why: what makes this a now-buy, what angle opens the door.

Mark who converted. The buyer model retrains on your outcomes each month, becoming progressively sharper — Month 1 vs Month 6 are different models.

Push leads in from any source (Clay, PDL, your CRM, your data warehouse). Pull scored results to anywhere your motion runs. Full REST/JSON docs at /apis-and-mcps.

$99 for 3 months full access + 25% off the base for life + 500 enrichment credits. Lock in a founding rate before July 1.

Follow a scored lead through your motion: reached → replied → qualified → stage progression. See what % of high-fit leads actually convert.

Slice your pipeline by the signals StackSwap OS detected: company size, stack composition, spend range, buyer motion type. See which cohorts move fastest.

Tag outcomes (won, lost, stalled) and let the model decompose the pattern: which stacks / spend ranges / company sizes close fastest for you.

Pull your trained model into your CRM, sequencer, or ad platform. Use it to prioritize leads natively in the tools you already own.

Detect when a company is actively searching for your category. Buy-side intent from job posts (hiring for a role that buys your tool), content consumption (reading your benchmarks), competitive mentions.

See which companies are checking out your site. Match to the accounts you should reach next. Integrate RB2B or similar to close the loop.

When a high-fit account + buying intent signals + a win-condition aligns, surface it immediately so sales can reach first.

Pipeline by stage + cohort, win rates by buyer signal, CAC by motion, MRR trend. Pulls from your CRM + StackSwap OS scoring + your outcome tags.

See which content surfaces high-intent buyers. Track from top-of-funnel impression (blog post read) through conversion. Justify content spend.

Wire Google Ads / LinkedIn / Meta spend to outcomes. See which channels + audiences produce the highest-fit leads and fastest close rates.

At scale, StackSwap OS enriches thousands of companies and trains thousands of buyer models. Aggregate the patterns: what stacks convert fastest, what spend ranges, what buyer motions close.

Your win rate by company size, motion type, stack maturity — anonymized, aggregated, benchmarked against peers. Learn where you win, where you lose.

The corpus itself becomes the product layer. Every new customer benefits from the patterns learned by all previous customers. Compounding moat.