Discovery is the front of the funnel before the funnel: the work of finding companies that could buy from you but are not yet in any list, any CRM, any spreadsheet. Every other GTM job operates on accounts someone already knows about. Discovery is where the accounts come from. Data infrastructure for discovery is the set of sources and methods that surface companies by what they are, what they run, who they resemble and what they are doing, resolved to canonical records, qualified against an ideal customer profile, and delivered as a list the rest of the machine can work.
The term is having a moment because the ways to find a company have multiplied. Firmographic databases were the only method for a decade; now there is search by plain-language description, search by technology footprint, similarity to accounts you have already won, local search for businesses with a storefront, and discovery through signals - the company that just posted a role, raised a round or appeared in a news story. Each method surfaces a different slice, and the interesting accounts are often the ones only one method finds. Autonomous AI agents make it practical to run all of them against one objective and merge the results. The product page for this job is Data Infrastructure for Discovery.
What data infrastructure for discovery means
A company can be found by several different questions. What does it do? What does it run? Who does it look like? Where is it? What just happened to it? Infrastructure for discovery is the ability to ask all five of licensed sources, get back canonical company records rather than name strings, deduplicate across the answers, fill the fields needed to judge fit, and persist the result as a list that grows as new companies appear. The output is a market you can see rather than a market you assume.
The difference from prospecting is scope. Prospecting selects from a known universe and finds the people; discovery builds the universe. A team that skips discovery works the same few thousand accounts every quarter and wonders why the pipeline plateaus. A team with discovery infrastructure sees the two hundred companies that entered its market this month. The lookalike accounts explainer covers one of the five methods in depth; this guide covers the whole set.
The data jobs inside discovery
Identify. Run the discovery methods against the objective: a plain-language description of the kind of company, a technology or set of technologies, a seed list of best customers to find lookalikes for, a geography and category for local businesses, and a signal filter for companies in motion. Each method returns candidates; the union is the raw discovery set. Enrich. Resolve every candidate to a canonical domain and company record, then fill the fields the ICP needs to judge it - size, industry, technologies, funding stage, location - through a waterfall of licensed sources, since discovery methods return thin records by design. Verify. Collapse duplicates across methods, detect subsidiaries and parents, confirm the company is operating, and drop anything already in the CRM as a customer, competitor or open opportunity. Score. Evaluate the ICP on the enriched record so the discovered set is ranked by fit, and attach the discovery method as a field so you learn which methods find the accounts that convert. Deliver. Persist the set as a refreshable list, push the qualified rows into the CRM as new accounts for approval, and keep the watch running so new entrants arrive weekly.
The data layers and the fields discovery runs on
| Layer | Fields that matter | Discovery use |
|---|---|---|
| Company data | Canonical name and domain, description, industry and sub-industry, employee count, revenue band, HQ and locations, founding year, technologies in use, parent and subsidiaries, similarity vectors to seed accounts | Description search, technology search, lookalikes, local search, ICP evaluation |
| Person data | Founder and leadership names and titles, department headcounts, key role holders | Confirming a company is real and staffed; seeding the buying committee later |
| Signals | New job postings, funding round and date, news mentions, technology adoption, new partnerships, incorporation and launch events | Signal-led discovery of companies in motion or newly formed |
| Verification | Duplicate flag across methods, parent-subsidiary resolution, operating status, CRM presence flag, ICP fit tier and reason | A clean, qualified discovery set |
| Delivery | Discovery method and date, list membership, CRM account ID after approval, watch cadence | A growing list and a stream of new entrants |
Local search is the layer most B2B teams underuse. A field-sales team selling to dental practices, gyms or independent retailers needs discovery by category and geography with an address, hours and a phone number, which is a different source family from the databases built for software companies. Infrastructure that includes it opens markets the firmographic databases barely cover. The company and person data guide describes the source families side by side.
Autonomous agents versus discovering by hand
By hand, discovery is a research project: someone spends a week with a database, a search engine, a few directories and a spreadsheet, produces a list of eight hundred companies, and the project ends. Six months later the market has changed and the list has not. An autonomous AI agent runs discovery as a standing objective across all five methods, resolves and qualifies what it finds, and reports the net-new entrants every week.
| Step | By hand | Run by an autonomous agent |
|---|---|---|
| Methods | One database filter, plus whatever a search engine turns up | Description, technology, lookalike, local and signal discovery run together and merged |
| Identity | Name strings in a spreadsheet; duplicates and subsidiaries inflate the count | Every candidate resolved to a canonical domain and record; hierarchy known |
| Qualification | Judged from the name and a glance at the site | Enriched through licensed sources, ICP evaluated, tier and reason stored |
| CRM overlap | Discovered later, when a rep calls a customer | Customers, competitors and open opportunities removed before delivery |
| Delivery | Spreadsheet handed over; imported once | Persistent list; qualified rows pushed into the CRM for one approval |
| Afterwards | The project ends | Weekly watch surfaces new entrants with the method that found them |
The compounding effect is the point. A standing discovery watch turns market coverage from a snapshot into a curve that rises every week, and the method field on each row turns the discovery budget into something that can be tuned on evidence.
A practical pattern is to run discovery in two passes. The first pass is broad and cheap: description and technology search across the whole market, resolved and deduplicated but only lightly enriched, to establish the size of the universe. The second pass is narrow and deliberate: the candidates that pass a coarse fit filter get the full waterfall, the ICP evaluation and the buying committee, so finder credits go to accounts that have already earned them. An agent runs both passes as one mission and reports the funnel between them, which is the number that tells you whether the objective was written well.
The metrics that show it is working
The figures below are illustrative examples for a B2B team discovering a mid-market software segment across North America and Western Europe.
140net-new qualified accounts surfaced per week by the standing watch (example)38%of discovered candidates surviving enrichment and the ICP evaluation (example)2.2xmarket coverage after a quarter versus the original database export (example)$0.85cost per qualified discovered account, misses and duplicates excluded (example)
Net-new qualified accounts per week is the throughput number, and it should be measured after deduplication against the CRM. Qualification rate tells you whether the discovery objectives are precise; a very high rate usually means the methods are too narrow, a very low one that they are too broad. Market coverage compares the accounts you can see to an estimate of the total addressable market, and it should climb. Cost per qualified discovery keeps the methods honest against each other.
How AstroFabric does it
AstroFabric runs discovery as a mission across every method in its catalog. discover_companies searches by plain-language description and by firmographic and technographic filters; companies_using_tech finds the installed base of any technology; similar_companies expands from a seed list of your best customers; local_business_search finds storefront businesses by category and geography with address and phone; and hiring_signals, funding_events and company_news surface companies in motion that no static filter would return. Every candidate resolves through company_to_domain and company_lookup, and company_relationships settles parent and subsidiary questions.
list_create holds the discovered set, list_enrich fills the fields the ICP needs across licensed sources, list_score ranks by fit and records the reason, and list_hygiene collapses duplicates and removes accounts already in the CRM as customers, competitors or opportunities. crm_upsert_contacts pushes the qualified accounts in for one approval, watch_companies with create_schedule keeps the watch running weekly, and signals_feed delivers the new entrants where your team already talks. Enrichment is charged only when a source answers, discovery misses cost nothing, plans start at $49 per month and a verified contact is a few credits. The landing page for this job is Data Infrastructure for Discovery.
Frequently asked questions
What is data infrastructure for discovery?
The sources, resolution and qualification that surface companies not yet in any of your lists: search by description, technology, similarity, location and signals, resolved to canonical records, enriched, tested against the ICP and delivered as a persistent list that keeps growing as new companies appear in the market.
How is discovery different from prospecting?
Prospecting selects accounts from a universe you already have and finds the people at them. Discovery builds the universe, finding companies that are not in the CRM or any purchased list. Discovery feeds prospecting; a team without it works the same accounts every quarter and plateaus.
Which discovery method finds the best accounts?
It varies by market, which is why the method is stored on every row. Lookalikes of won accounts often convert best; technology-based discovery finds accounts competitors miss; signal-led discovery finds accounts in motion; local search opens categories databases barely cover. Running all five and comparing outcomes settles it.
How does an agent avoid duplicates across methods?
Every candidate is resolved to a canonical domain and company record before it counts, parents and subsidiaries are linked, and the set is deduplicated against itself and against the CRM. The delivered list contains each real company once, with the methods that found it recorded.
What does discovery cost?
Plans start at $49 per month and usage is credits. Discovery queries that return nothing cost nothing, enrichment is charged only when a licensed source answers, and a verified contact is a few credits. Credit ceilings enforced before spend keep a market-wide discovery run inside its budget.
Sources
- U.S. Census Bureau - Business Formation Statistics (the public baseline for new-company discovery)
- U.S. Bureau of Labor Statistics - Job Openings and Labor Turnover Survey (hiring as a discovery signal)
Every playbook on this blog ships as a runnable mission.
Open a workspace and the playbook library is waiting - describe the outcome and the agents carry it end to end, on your plan's monthly credits.