Landbase
Toolkit · Name to Domain
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T3 · Tool

Name to Domain

Takes a free-text company name — from a CRM, a sponsor list, a regulatory filing — and returns its canonical domain. Trivial-looking, surprisingly hard at scale.

Examples

How customers use it

01
Resolving partner and reseller target lists
Hand over a CSV of company names with no URLs from a reseller or partner. Name to Domain canonicalizes each name against the Landbase graph so the list can be deduplicated, enriched, and routed to the right account team.
02
Turning company names into URLs to enrich
Drop in a CRM export of 12,000 company names with blank or stale website fields. Name to Domain returns the canonical domain for every row so the rest of the Landbase data can be attached downstream.
03
Turning regulatory filings into prospect lists
Pull a list of companies from a regulatory body — SEC, FDA, or FAA filings, names only. Name to Domain returns canonical domains so those names become a working prospect list ready for enrichment and qualification.
04
Cleaning vendor and supplier lists
Receive a 4,000-name vendor or supplier list from procurement with patchy URLs. Name to Domain normalizes the list so each supplier can be resolved against the Landbase graph and enriched with the full attribute pack.
Pairs with
T4Match T5Enrich T6AI Qualification T7Local Business Crawl
Found in
CRM Refresh
Under the hood+
How it's used

The first step on any dataset where the source gives a name but not a URL. CRM exports, partner lists, sponsor lists, conference attendees, regulatory filings. Without it, every downstream agent has nothing to attach to.

Why it matters

The domain is the join key for everything downstream. A high resolution rate means downstream tools can operate; a low rate is itself the diagnostic — it tells you the source data is thin and the dataset may not be worth processing.

How it works
  • Graph disambiguation — against the 200M-company Landbase graph.
  • Web research — live aggregation for long-tail and thin-presence companies.
  • Confidence scoring — every candidate gets a score; fallback heuristics for the hard cases.
  • Bulk parallelization — production-scale throughput on large lists.