Find Companies Using a Technology by Name

To find every company using a given technology, call the PredictLeads Discover Technology Detections endpoint with the technology’s fuzzy name, for example “Snowflake.” Right now that query returns 37,312 companies, drawn from the 54,000 technologies PredictLeads tracks. You do not need the exact technology ID to start; a name is enough. This makes it simple to find companies that are using a technology with just a single search.

This is the seed step of a discovery workflow. For GTM teams it is competitive displacement and integration targeting. For marketing agencies it is an on-demand technographic segment you can spin up for any client campaign. In cases where agencies need to find companies employing a specific technology for their campaign, this workflow is invaluable.

Table of Contents

How do you find companies by technology?

Pass a fuzzy technology name and read back the list of companies where that technology was detected, ordered by when it was first seen. Through the PredictLeads MCP server, the prompt is plain language: This provides you with an efficient way to find companies using a technology relevant to your needs.

Using the PredictLeads MCP server, list companies detected using Snowflake.
Return company name and domain, and note how recently each was first seen.

The equivalent endpoint call:

GET https://predictleads.com/api/v3/discover/technologies/{technology_id_or_fuzzy_name - in this case snowflake}/technology_detections

If you want an exact match rather than a fuzzy one, look up the technology in the Technologies dataset first and pass its ID. Fuzzy names are faster to start with and handle spelling and casing differences. Either approach lets you find companies using a technology, whether your search is broad or precise.

What does a technology detection include?

Each detection is more than a yes or no. It carries the context you need to score and prioritize a company: When you find companies using a technology, the detection data provides the story behind their adoption.

first_seen_at and last_seen_at, so you can tell a fresh adopter from a long-time user
a confidence score and a source count, so you can weight strong detections over weak ones
where it was seen: subpages, job openings, DNS records, and reviews
department_onet_codes, the O*NET codes of the departments where the technology showed up
location data for the detection, so you can filter by geography

The department codes matter because they connect this dataset to hiring. If Snowflake shows up against a data-science department code, that same company is a candidate for the hiring query in the next post. In effect, this workflow helps you find companies using a technology and understand their department-level context.

How do you focus on recent adopters?

A company that adopted a technology last week is a better trigger than one that has run it for years. Filter by first-seen date to keep only recent adopters. This approach is particularly useful when you need to find companies who recently started using a technology for targeted outreach.

discover_technology_technology_detections(
  technology_id_or_fuzzy_name: "Snowflake",
  first_seen_at_from: "2026-04-23"
)

Recency turns a static list into a timing signal. New adopters are still choosing complementary tools, still hiring to run the new stack, and still open to a conversation.

How is this different from a static technographics export?

A downloaded technographics list starts decaying the moment you receive it. Companies adopt and drop tools, and by the next quarter a meaningful share of the file is wrong. Because Discover Technology Detections is queried live, the list reflects the current state every time, and the first-seen date gives you a recency signal a flat export cannot. So, if you want to consistently find companies using a technology right now, the live queries are indispensable.

How do you expand the list with lookalikes?

Once you have companies on a technology, widen the net with the Similar Companies dataset. Take a strong match and ask for companies like it. For example, companies similar to Elastic come back as Sumo Logic, SolarWinds, Cribl, Datadog, and New Relic, a clean map of the observability space. This helps scale your efforts to find not just companies using a technology, but organizations likely to adopt it as well.

company_similar_companies(company_id_or_domain: "elastic.co")

Chaining detection then lookalike gives you both the companies that already run the technology and the adjacent companies that probably should, which is a larger and still relevant universe.

What can GTM teams and agencies do with this?

Competitive displacement. Find every company running a rival’s product, then lead with a migration angle. The ability to find companies using a technology means you can pivot your strategy fast against competitor stacks.
Integration targeting. Sell to companies that already run a technology your product complements.
Agency segments. Build a technographic account list for a client’s ABM campaign in minutes, then refresh it whenever the campaign needs new names.
Partner sourcing. Map an ecosystem by finding every company on a platform you integrate with. GTM teams can thus find companies using a technology and create new partnership opportunities.

How do you score a technology detection?

Not every detection deserves equal weight. Two attributes let you rank them. The source count tells you how many independent places the technology was seen, so a detection backed by a job posting, a subpage, and a DNS record is stronger than one seen once. The confidence score summarizes that evidence into a single value you can threshold on. Through scoring these attributes, you prioritize which companies using a technology you should focus your efforts on.

A behind_firewall flag marks technologies inferred from hiring rather than a public page, which is useful context when you decide how hard to lean on a detection. For a target list, a simple rule works well: require a minimum source count for outbound, and treat single-source detections as softer signals worth a lighter touch. In summary, scoring helps refine your effort to find companies using a technology with the strongest signals.

Frequently Asked Questions

How do I find companies using a specific technology?

Call the Discover Technology Detections endpoint with the technology’s fuzzy name. It returns the companies where that technology was detected, ordered by when it was first seen.

Do I need the exact technology ID?

No. A fuzzy name such as Snowflake works. You can also pass a technology ID from the Technologies dataset if you want an exact match.

Can I find only companies that recently adopted a technology?

Yes. Use the first-seen date filter to return only detections first seen after a date you choose, which surfaces recent adopters as a timing signal.

How many technologies does PredictLeads track?

PredictLeads tracks about 50,000+ technologies, so most tools in a modern stack can be used as a discovery signal.

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