Companies found by what they do, what they run, who they resemble and where they are: signal searches, technology searches, lookalikes of your best customers and local business searches, resolved to domains and scored against your ICP. The infrastructure under discovery, run by autonomous AI agents.
Firmographic and signal search, technology search, lookalikes from reference accounts and local business search, combinable in one mission.
Hiring, funding, technology adoption and change, news, relationships, intent, growth and leadership moves as search criteria.
A handful of best customers expand to hundreds of lookalikes on size, stack, industry and signals.
Every company found carries a fit score and the reasons, so discovery returns a ranking instead of a pile.
Industry plus headcount plus country returns eleven thousand results. The filter cannot see that one of them just posted three RevOps roles and another just replaced its CRM, so the list is a phone book.
The team knows its best customers and cannot say what they have in common in a form a database can search. "Companies like Northwind" stays a conversation instead of a query.
Every dental practice in Austin, as names and phone numbers, with no website, no owner and no way to tell the four-location group from the single chair.
Companies by industry, size, geography, funding stage and growth, combined with what happened recently: roles posted, rounds raised, products launched, leaders hired and topics researched.
Every company running a given product or category, and the ones that adopted or dropped it in a window, so you can find the users of a complementary tool or the recent switchers from a competitor.
A handful of reference accounts expanded to the companies that resemble them on industry, size band, stack, funding and signals, with the resemblance explained on each row.
Businesses of a category in a city or radius with ratings, review counts, hours and websites, resolved to a domain and enriched so the multi-location operator stands out from the single site.
One sentence: "SaaS companies in DACH with 50 to 500 people that run a modern CRM and posted a RevOps role this month". The workspace ICP fills anything you leave out.
Agents run the firmographic, signal, technology, lookalike and local searches the objective calls for, in parallel, against licensed indexes.
Results merged on canonical domain, duplicates collapsed, ambiguous entities resolved with the rest of the row as context.
Firmographics, stack, funding and the latest signal attached to every company so the list is usable without a second tool.
Fit against the ICP with reasons; customers, competitors and the suppression list separated out.
A saved list that refreshes on a schedule, exported as CSV or pushed into the CRM after approval, with contacts added when you want them.
Companies that dropped a competing product in the last 90 days, resolved and enriched, with the replacement identified where a source has it, so the conversation starts with the change they just made.
Every company in your ICP running the platform your product integrates with, sized and scored, so partner-led outreach has a universe instead of a hunch.
The twenty most recent closed-won accounts expanded to their nearest neighbors on stack, size and signals, deduplicated against the CRM and scored.
Every business of a category across a metro area, grouped by operator, filtered to groups with three or more locations and a website, with the owner found where a licensed source has one.
| By hand | With AstroFabric | |
|---|---|---|
| Query | Industry, size, country | Plus stack, signals, lookalikes and location, in one sentence |
| Results | Eleven thousand rows in alphabetical order | A few hundred, scored with reasons |
| Lookalikes | A feeling about the best customers | Nearest neighbors on stack, size and signals, explained |
| Duplicates | The same company under three names | Merged on canonical domain |
| Local | A maps export with phone numbers | Resolved domains, operators grouped, owners found |
| Afterward | A CSV that ages | A list that refreshes and pushes to the CRM on approval |
Companies that dropped a named competitor recently, resolved, enriched and scored, the likely owner found, delivered as a refreshable list.
The last twenty closed-won accounts expanded to their nearest neighbors, deduplicated against the CRM, scored and pushed to Attio after approval.
Every business of a category in a metro area, grouped by operator, filtered to groups with a website and three or more sites, owners found where available.
Three of a hundred. The full playbook library ships in every workspace, and anything you can describe becomes a mission.
Discovery, resolution, enrichment and scoring run inside the credit ceiling with no gate. A push into the CRM is staged for a person to approve exactly as previewed.
Customers, open opportunities, competitors and the workspace suppression list are separated before a discovery list is delivered.
The search that found the company, the signal that qualified it and the fit reasons ride with each row, so the list can be defended in a pipeline review.
Company data comes from licensed providers. The agents never scrape member networks.
The layer that turns a description of who you sell to into a ranked list of companies: firmographic and signal search, technology search, lookalikes of reference accounts and local business search, merged on domain, enriched, scored against the ICP and saved as a list that refreshes. Autonomous agents run it.
Industry, headcount, growth, geography, funding stage and recency, revenue range, technologies in use or recently adopted or dropped, open roles by function, hiring velocity, news, leadership changes, buying-intent topics, similarity to named companies, and for local businesses category, radius, rating and location count.
Give the agents a handful of reference accounts, your best customers or your latest wins, and they find the companies nearest to them on industry, size band, stack, funding and signals. Each result explains the resemblance, and the ICP weights decide which dimensions count most.
Yes. Search for every company running a product or a category, or only those that adopted or dropped it inside a window. That finds the install base of a partner, the users of a complementary tool or the recent switchers from a competitor.
Yes. Local search runs by category and location worldwide, returning ratings, review counts, hours and websites, and the agents resolve each result to a domain and group multiple locations under one operator where the data supports it. Results carry the address and phone from the listing.
Yes. Ask for the buying committee and the agents find named people by title and seniority at each company, verify emails through a waterfall of licensed sources and add them to the same list. A lookup that finds nothing costs nothing.
Finding and enriching a company is a few credits per row, scoring and deduplication are free, and a verified contact is a few credits when you ask for people. Rates show before the run and plans start at $49 per month.
Describe the companies. A resolved, scored list comes back in minutes and refreshes on the schedule you set.