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Signum.AI — AI-interpreted competitor pricing-page change corpus: 24/7 crawls of public pricing pages capturing plans, rates, add-ons, usage limits, currencies, promotions and geographic price tests, retained as before/after diffs with source URLs — plus, as the more interesting byproduct, a model-labelled stream that separates meaningful commercial changes (price, plan, limit, promo) from cosmetic copy edits.. Small but specialist: ~6 employees, ~830K raised (seed), ~3M revenue proxy, New York. Differentiated by AI-level change interpretation (noise suppression, geo-test and promo detection,…
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AI-interpreted competitor pricing-page change corpus: 24/7 crawls of public pricing pages capturing plans, rates, add-ons, usage limits, currencies, promotions and geographic price tests, retained as before/after diffs with source URLs — plus, as the more interesting byproduct, a model-labelled stream that separates meaningful commercial changes (price, plan, limit, promo) from cosmetic copy edits.
Scale Customer-scopedFrom 5 yearsCoverage Information Technology · Communication Services
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Signum.AI — AI-interpreted competitor pricing-page change corpus: 24/7 crawls of public pricing pages capturing plans, rates, add-ons, usage limits, currencies, promotions and geographic price tests, retained as before/after diffs with source URLs — plus, as the more interesting byproduct, a model-labelled stream that separates meaningful commercial changes (price, plan, limit, promo) from cosmetic copy edits.. Small but specialist: ~6 employees, ~830K raised (seed), ~3M revenue proxy, New York. Differentiated by AI-level change interpretation (noise suppression, geo-test and promo detection,…
Signum.AI offers (Alternative, Fundamental, Sentiment) — Customer-scoped, not a fixed owned corpus: each account monitors 5-500+ competitor pricing URLs, crawled 24/7. Est. ~1K-100K monitored pages across the account base; at a daily-or-faster cadence that is roughly ~1M-50M page observations per year, with only the classified-meaningful subset surfaced as change events..
Near-real-time competitor repricing triggers; promotional-campaign and geo-pricing-experiment detection; SaaS packaging-shift tracking; and — the strongest non-obvious use — training/evaluating LLM change-classification and information-extraction models on the meaningful-vs-noise diff labels, which are far harder to obtain than raw snapshots because they require adjudicated negative examples (edits that look like changes but are not).
The data is with 5 years of history.
Coverage spans US; Application Software, Internet Services; alternative, fundamental, sentiment; equities.
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