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Use case

Your ideal customer profile, screened against the entire web

An ICP is a paragraph of plain language: who buys, why, and what disqualifies.

We run that paragraph against 100M+ classified domains — every company that matches, with the qualifying evidence quoted — instead of whatever subset a data vendor's crawler happened to index.

100M+
classified domains
24.7M
business & finance sites
700+
industry categories

The account list is the strategy

Every GTM motion — ABM, territory design, capacity planning, partnerships — is downstream of one artifact: the list. In most organisations, that artifact is the least rigorous thing in the building.

The ICP is rich — the filter is poor

Someone writes a thoughtful profile: behaviors, commercial model, disqualifiers. Then someone translates it into the only available filters — industry codes, employee bands, revenue bands. The translation destroys most of the meaning.

Soft criteria have no field

"Manufacturers that run in-house service teams" becomes an industry code that includes thousands without service teams and excludes thousands with them. "Sites that monetize through marketplace listings" corresponds to no code at all.

Too big and too small at once

The resulting list is full of accounts that were never going to buy, and missing accounts that would. The team prospects a universe shaped by database schema, not by commercial reality.

The source pool silently ends

A licensed database contains what its vendor indexed — thinnest exactly where long-tail B2B lives. If your product sells to specialist workshops or regional operators, a meaningful share of your market has no profile anywhere.

ICP discovery removes both failure modes at once

Source: 100M+ classified domains — 99.99%+ of active internet usage
Instrument: your ICP text as plain language, not a code lookup
Output: every match with quoted evidence; every exclusion with a reason

Why real ICPs break firmographic tools

The profiles that actually predict revenue are behavioral and textual. None of them survive translation into standard filters.

The criteria that matter have no field

“Runs scheduled maintenance programs.” “Sells through distributors but supports end users directly.” “Publishes technical documentation.” These are the qualifiers your win-rate data points to — and they exist only as sentences on websites, never as columns in a schema.

Codes measure category, not behavior

Two companies share an industry code; one is a self-serve product business, the other a project shop with three customers. Your product fits exactly one of them. A code-based list treats them as identical and your conversion data pays for the difference.

The index ends before the market does

In our industrial specimen runs, a fifth or more of confirmed fits had homepages without the category's obvious keywords — companies a keyword-driven index misfiles or never surfaces. For long-tail B2B, the unindexed share of your market is not a rounding error.

How the screen actually runs

The same two-pass architecture we use for acquisition screening, pointed at a commercial question instead of an investment thesis.

1

Pass 1 — Triage

  • Starting pool: every domain in the relevant categories, not keyword search results
  • Classifies each site: live operating company, directory, parked, blog?
  • Filters non-scope entities cheaply before expensive analysis
  • Example: 25,000-domain triage → 17,300 live operating companies
2

Pass 2 — Deep extraction

  • Multi-page read: services, about, case studies, careers, partners
  • Answers your profile's specific qualifying questions
  • Every claim is a quote with a source page
  • ”Not visible” recorded rather than guessed — trust requires provenance
3

Scoring & ranking

  • Profile fit dominates the weighting; reachability and maturity shade the rank
  • Named disqualifiers zero a company out regardless of other scores
  • Weights configured with you before the run
  • Re-runs with an adjusted profile are included — language can be revised
The result is not a sample of your addressable market — it is the addressable market, enumerated.
Pair with TAM mapping for segmented counts, or gap analysis to measure what your current stack missed.

The signals that carry a commercial screen

Six of the fifteen extraction signals do most of the qualification work when the question is “will this company buy?” rather than “should we buy this company?”

Profile fit (principal score)

Your ICP text is the instrument: the extraction matches each company's stated services, customers, and commercial model against the profile's own wording — not a proxy code. Soft criteria become screenable: “operates its own field-service organization” is checkable from service pages and technician bios; criteria genuinely invisible on the web get flagged before the run.

Vertical specialization & end-market exposure

Who a company serves matters more than what it calls itself. End markets are extracted from case studies, named customer industries, and application pages — “serving municipal water systems since 1998” qualifies a company no industry code would surface, turned into a filterable field across the whole universe.

Digital-commercial maturity

Online quoting, customer portals, e-commerce, published pricing, self-service scheduling — the visible surface of how a company transacts. A digitally mature account enters a product-led funnel; a phone-and-fax operation needs field sales. Maturity markers are extracted as observed facts, not assumed correlations with company size.

Partner & channel ecosystem position

Named partnerships, distributor authorizations, integration listings, association memberships. Adjacency signals the warmest kind of cold account; deep certification into a rival ecosystem flags switching costs your forecast should know about. Partner pages are among the most reliably maintained on B2B sites.

Recurring-offering indicators

Service contracts, maintenance agreements, scheduled programs, subscription language. This signal separates accounts with economics compatible with your billing model from accounts that buy once and disappear — extracted from distinctive language like “preventive maintenance programs” and “annual service agreements.”

Hiring posture & functional investment

Open roles read from careers pages at screening time. A company hiring into the function your product serves has budget moving toward your conversation. Hiring posture also serves as an honesty check: a live careers page with dated postings confirms a functioning business.

What this looks like in practice

ICP universes have run across travel technology, data, software, industrial, and telecom categories for 300+ organisations — including a leading airline-metasearch platform and one of the largest European telecom operators.

Real denominators, not extrapolations

One industrial category sweep held 367,478 domains globally. Across ten published subverticals, eligible independent US companies ranged from 702 in precision machining down to 93 in surface finishing.

Hidden fits your competitors never found

Companies whose homepages lack the category's obvious keywords entirely — yet whose deeper pages evidence exactly the capability the profile wants. Your competitors' SDRs have never emailed them.

”Homepage lacks the obvious category keywords — keyword-driven databases would likely miss or misclassify this company.”Screening note, Hidden fit A-01 — automation & controls specimen set
Specimen report structure
8 top fits 5 keyword-missed fits 5 documented exclusions 2 insufficient-evidence flags

A list you can trust shows what it rejected and why. Request one and judge the evidence quality before anything is commissioned.

Where this sits in your data stack

Not a replacement for everything — a different layer answering a different question. The honest comparison:

Profile databasesIntent-data platformsFull-web ICP discovery
Question answeredWhat do we know about companies already indexed?Who is researching this topic right now?Which companies exist that match the profile at all?
Source poolThe vendor's curated indexTraffic panels across a monitored subset100M+ classified domains — the active web
Handles soft criteriaOnly if a field exists for themNo — topic-level onlyYes — the profile text is the filter
Evidence per accountFirmographic fieldsAnonymized signal scoresQuoted sentences with source pages
Long-tail coverageThins with company sizeLimited to monitored propertiesUniform — a 12-person shop is one more domain
Best used forEnrichment and workflowTiming signals on known accountsBuilding the universe those tools then operate on

The layers compose: run discovery first, enrich with your existing stack, let intent platforms time the outreach. See our guide on databases vs full-web mapping for the coverage arithmetic.

What ICP discovery does not do

Clear boundaries define where this method is strong and where you should spend elsewhere.

Reads websites, not minds

If a criterion is invisible on the public web — internal tooling, contract terms, budget cycles — we tell you during profile articulation, before money moves.

No contact data

The deliverable is qualified companies with evidence, structured for your CRM. Person-level enrichment is what your existing stack does; we hand it a better universe to enrich.

No buying intent

Existence and fit, yes; timing, no. Nothing in a website says an account will enter a buying cycle this quarter, and we do not dress correlation up as timing signal.

Web-visible market only

In B2B this is nearly everything, but a business with no web presence is invisible to us. We state the boundary rather than extrapolating across it.

What we refuse to sell: no “ready to sell” flags, no revenue or EBITDA guesses, no owner-age profiling, no distress detection — and no engagements in consumer-captive verticals. Read our standards; serious buyers tell us this page is why they trusted the rest.

From delivery to pipeline

Structured files built for CRM ingestion: one row per qualified account, with profile-fit scoring, extracted fields, and evidence quotes for first-touch personalization.

Route by fit tier

Load as a dedicated CRM segment. Top-tier to named-account sellers, mid-tier to sequenced outbound, digitally mature accounts into product-led flows.

Feed verdicts back

When reps disqualify accounts for a recurring reason, that reason becomes a profile revision. The included re-run applies it across the whole universe — your list sharpens each quarter.

Monitor the universe

Treat the universe as a living object: monitoring re-screens surface new entrants, pivots, and dead accounts on a schedule.

€4,900
Proof project
€9,900
Full universe
€18,000
Annual monitoring

Full terms on the pricing page. Every engagement begins with a same-day specimen.

Common questions

Contact databases answer “who works at companies we already know about?” This answers the prior question: which companies exist that match the profile at all. The output is account-level and evidence-backed — each inclusion carries the quoted language that qualified it. You then enrich those accounts with whatever contact tooling you already license. Bigger indexes help at the margin; they do not change the fact that an index is a subset, and your profile's long tail is where subsets fail.

A paragraph of honest plain language beats a spreadsheet of filters. State who the ideal customer is, the visible behaviors that qualify them, and the hard disqualifiers. During intake we stress-test each criterion for web visibility and tell you which ones the screen can evidence, which it can only approximate, and which are invisible — before the run, not after. Profiles routinely sharpen after the first output; re-runs on revised wording are included.

Yes. Every extracted field is a segmentation axis: end markets served, commercial model, digital maturity, ecosystem position, geography from stated footprints. Teams that need their own scheme — tiers defined by criteria unique to their motion — use custom taxonomy classification on the same universe, and the segment counts double as market sizing.

Extraction happens at screening time from live sites, so the deliverable reflects the web as it stood during the run, not a database snapshot of unknown age. Universes then age like any list — companies pivot, get acquired, go quiet — which is why monitoring re-screens exist. Annual monitoring runs from €18,000 per thesis and delivers structured deltas rather than a re-purchase.

The specimen material published on this site is industrial because acquisition screening built the engine there, but the classification layer spans 700+ categories across the whole web, and ICP engagements have run across travel technology, data, software, and telecom — the airline-metasearch universe was a global consumer-adjacent screen. If your ICP describes something companies say about themselves online, the category is workable; we confirm feasibility on your actual profile before any commitment.

See your ICP enumerated, not sampled

Send the profile as you would brief an analyst. We return a specimen the same day — qualified accounts with the evidence quoted, including the ones we refused to include.

Request the specimen report