Your competitors publish their strategy every week — in the categories they occupy, the technologies they deploy, the buyers their content courts and the brands they mention. This platform reads it systematically: competitor discovery across 102 million classified domains, stack comparisons, and audience segmentation that decodes exactly whose ICP a rival is writing for.
Product marketing keeps a battlecard for the three competitors sales mentions most. Meanwhile the category quietly fills with companies nobody is tracking, a rival repositions toward your best segment, and the first hard evidence arrives as a lost deal. Manual competitor monitoring — someone skimming blogs on Friday afternoons — does not scale and leaves no structured record to compare quarter against quarter.
The rivals that hurt you next year are rarely on today's battlecard. Discovery by keyword search finds whoever does the best SEO, not whoever is actually closest to your category.
Build-vs-buy choices, vendor dependencies and go-to-market tooling are all visible in a site's technology fingerprint — if anyone is looking at it systematically.
A rival moving upmarket telegraphs it in content months before the pricing page changes. Unstructured reading cannot detect that drift; classified snapshots can.
Ad-hoc competitor reviews produce slide decks, not datasets. Without a consistent schema, this quarter's findings cannot be diffed against last quarter's.
Each layer produces structured, repeatable output — so competitive intelligence becomes a dataset you query and diff, not a deck you rebuild.
The competitor finder surfaces domains classified into the same categories as yours across the 102M-domain database, filtered by traffic tier so you see the rivals with real audiences first — including the ones not yet on anyone's battlecard.
The technology detector fingerprints what each competitor runs — commerce platform, analytics, marketing automation, A/B tooling. Run it across the set via the technology slice and vendor-adoption patterns in your category become a table.
POST /api/audience/segment.php against a competitor's blog or landing pages
returns b2b_signals: target roles, company size and industry focus, mapped to the
persona taxonomy. It answers the strategic question directly:
who are they writing for — and is it your buyer?
The same response's content_context.brand_mentions reveals which vendors a
competitor names — partners, integrations, or you. Pair with the
sentiment analyzer to log how their framing of the
category (and of you) trends over time.
A real (trimmed) audience-segmentation response for a security vendor's blog. Note what a positioning analyst gets for free: named target roles, the company-size band they are courting, the industries they prioritize, and the brands they mention.
import requests
resp = requests.post(
"https://www.websitecategorizationapi.com/api/audience/segment.php",
data={
"query": "https://www.crowdstrike.com/en-us/blog/",
"api_key": API_KEY,
},
timeout=120,
)
profile = resp.json()["audience_segmentation"]
battlecard.update(
competitor="crowdstrike.com",
icp_roles=profile["b2b_signals"]["target_role"],
icp_size=profile["b2b_signals"]["target_company_size"],
mentions=profile["content_context"]["brand_mentions"],
)
{
"url": "https://www.crowdstrike.com/en-us/blog/",
"audience_segmentation": {
"b2b_signals": {
"is_b2b_content": true,
"confidence": 0.9,
"target_role": ["IT Manager", "Security Analyst", "CISO"],
"target_company_size": "mid-market",
"industry_focus": ["Technology", "Finance", "Healthcare"]
},
"purchase_intent": [
{"category": "cybersecurity solutions", "confidence": 0.8}
],
"content_context": {
"content_type": "blog_article",
"writing_style": "professional",
"reading_level": "advanced",
"brand_mentions": ["CrowdStrike"],
"sentiment": "neutral"
},
"iab_categories": [
"Technology > Cybersecurity",
"Business > IT Services"
]
},
"status": 200
}
Run monthly across each competitor's key pages and diff:
when target_company_size moves from "mid-market" to "enterprise", you have caught a
repositioning in the data before it reaches their pricing page.
Combining the layers yields a comparison grid that regenerates itself (figures illustrative).
| Signal | You | Competitor A | Competitor B |
|---|---|---|---|
| Primary category | Tech > DevOps Tooling | Tech > DevOps Tooling | Tech > Enterprise Software |
| ICP roles targeted | Platform engineer, SRE | Platform engineer, CTO | CIO, IT Manager |
| Company size courted | Mid-market | Mid-market → enterprise | Enterprise |
| Traffic tier | Tier 2 | Tier 2 | Tier 1 |
| Marketing stack | HubSpot, GA4 | Marketo, Drift, 6sense | Adobe stack |
| Mentions you? | — | Yes, comparison pages | No |
The middle column is the story: Competitor A's content has started courting enterprise buyers and their stack acquired an ABM vendor — two independent signals of an upmarket move, each machine-collected and dated.
Similarity works on classification, not keywords: candidates share your category assignments in the domain database, then get ranked by category-overlap depth and filtered by traffic tier, company size or business model. You choose how tight the net is.
The platform reads publicly served pages — the same material any analyst or prospect sees in a browser. It does not access gated content or private systems; it makes reading the public record systematic instead of anecdotal.
Schedule the API calls. A monthly job re-segments each rival's key pages, re-fingerprints their stack, and diffs against the stored profile — changes land in Slack or your BI tool. The real-time engine means a breaking question ("who is this new vendor in our deal?") is answered in seconds, not next cycle.
Name a rival and we will send back their profile — categories, technology stack, audience segmentation of their key pages and discovered lookalikes — in the same format the API produces.
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