How to Find Websites Using a Specific Technology (for Sales & Outreach)

Most technology-detection guides answer the question "what is this one website built with?" This guide flips it: which websites use a given technology? That reversal is the foundation of technology-based prospecting — the practice of finding companies to sell to, partner with or research based on the tools they already run. If you sell a Shopify app, you want a list of Shopify stores; if you offer Webflow development, you want Webflow sites. Here are four reliable ways to build that list, and how to turn it into outreach that actually lands.

If you are new to detection itself, the companion guide how to find out what a website is built with covers the underlying signals; here we use them in reverse.

Why find sites by technology?

Targeting by technology is one of the highest-signal ways to build a list, because the technology is the qualification. A generic list of "e-commerce businesses" mixes platforms, sizes and needs; a list of "stores on Shopify Plus running a reviews app but no subscription tool" is a precise market you can speak to directly. The common uses are:

• Sales prospecting — find companies that use a platform your product extends or replaces.
• Partnerships and integrations — find potential partners on a complementary stack.
• Market and competitive research — measure how widely a technology is adopted, and by whom.
• Recruiting and talent mapping — find companies building with a stack you hire for.

In every case, the technology signal narrows a vast universe down to the slice that matters.

Method 1: Use a technology index (fastest)

The quickest route is a tool that has already detected technologies across many sites and lets you search that index in reverse. StackOptic, BuiltWith and Wappalyzer all support "which sites use X?" queries and let you export the results. You pick a technology — a platform, a framework, an analytics tool, a payment provider — and get back the sites known to use it, often with filters for category, region or co-used technologies. This is the path to take when you need scale and do not want to assemble the list by hand. StackOptic, for example, lets you filter the sites it has analysed by technology and by audit scores, then export the matches as a lead list.

Method 2: Search-engine footprints (free, small scale)

Most technologies leave a footprint — a distinctive string that appears in the page or its URLs — and you can find users of that technology by searching for the footprint. A platform that serves assets from a unique domain, or that uses a recognisable URL path, can be surfaced with standard search operators that restrict results to pages containing that marker. This approach is free and surprisingly effective for a handful of qualified examples, though it is slower and less complete than an index, and search engines limit how deep you can go. It is ideal when you need ten good prospects, not ten thousand.

Method 3: Public web datasets (advanced)

For large-scale or research use, open datasets let you query the web's technology landscape directly. Common Crawl publishes periodic crawls of billions of pages, and the HTTP Archive analyses millions of sites and publishes the results (including technology detections via its Wappalyzer integration) in queryable form. Working with these requires some data tooling, but they are authoritative, free, and not rate-limited the way a search engine is — the serious option when you want a comprehensive, reproducible dataset rather than a quick list.

Method 4: Scan your own seed list

Sometimes you already have the domains — exported from your CRM, scraped from a directory or association, or pulled from a marketplace — and you simply want to know which of them use a given technology. In that case, run detection across the seed list and filter. This is the most precise method because you control the universe: you are not discovering new companies so much as qualifying ones you already care about. It pairs naturally with an account-based approach, where the target accounts are known and the question is which of them fit your technographic profile.

Combine signals for a sharper list

The real power of technology lists is in combinations. Any single technology is a broad filter; two or three together describe a precise situation you can speak to. "Uses platform X" is a vast universe; "uses platform X and tool Y but not competitor Z, in region R" is a campaign. This is sometimes called co-occurrence analysis — studying which technologies tend to appear together — and it is how you turn a generic list into one where the message almost writes itself. For example, a store on a particular e-commerce platform that already runs a reviews app but has no loyalty tool tells you exactly what to pitch and why. Most technology indexes let you stack filters like this, and even with search footprints you can intersect two result sets by hand. The discipline is to start from the situation you sell into, then express that situation as a combination of present and absent technologies — and to remember that the absence of a tool is often a stronger buying signal than its presence.

Turning a list into outreach that converts

A list is only useful if the outreach is relevant, and the technology signal is your relevance. A few principles:

• Lead with the shared context. "I noticed you run [platform] with [app]" earns more attention than a generic opener — because it is specific and true.
• Solve a problem the stack implies. If their technology choice creates a known limitation or opportunity, that is your angle.
• Segment by the stack, not just the firmographics. Two companies of the same size on different platforms often need different messages.
• Personalise at the segment level rather than faking one-to-one personalisation across thousands of contacts.

A worked example

Say you sell a subscription-billing app for Shopify. Your technology signal is precise: Shopify stores that sell consumable products but do not already run a subscription app. You start with a technology index, filter to Shopify stores, then add a second filter that excludes the known subscription tools — leaving stores that look like a fit but have not adopted the category yet. You export a few hundred domains, enrich them with product category and region, and verify a sample by hand to confirm the index is current. For outreach, your opener writes itself: you noticed they sell consumables on Shopify without a subscription option, and here is what predictable recurring revenue could look like for a store like theirs. Every step flows from the technology signal — the platform qualifies the prospect, and the absence of a competing tool defines the opportunity. That is the difference between a raw list and a targeted campaign, and it is achievable in an afternoon rather than a quarter of manual research.

How often should you refresh the list?

Technology lists decay, because sites change their stack — they migrate platforms, add and drop tools, and rebrand. How fast a list decays depends on the technology: core platforms change rarely, while marketing and analytics tools churn often. As a rule of thumb, treat a list as fresh for about a quarter, re-verify priority accounts before each outreach cycle, and re-pull the full list a few times a year. Building refresh into your process keeps reps from referencing a tool a prospect dropped six months ago — a small slip that quietly destroys credibility on the very first line of an email. A list is a snapshot, not a permanent record, so plan to take new snapshots.

Accuracy, limits and responsible use

Be honest about the limits. Detection sees front-end signals only, so it can miss purely server-side software, and any index reflects when each site was last scanned, so lists drift as companies change their stack. Verify a prospect's current setup before you reference it. Just as importantly, how you contact people is regulated: follow GDPR (EU/UK), CAN-SPAM (US) and the equivalent laws for your audience — that means honest identification, a genuine opt-out, and never misusing personal data. A technology-qualified list is a powerful starting point, not a licence to spam.

The workflow

1. Define the technology signal that qualifies a prospect for you.
2. Choose your method — an index for scale, footprints for a quick handful, public datasets for research, or a scan of your own seed list.
3. Export and de-duplicate the resulting domains.
4. Verify the current stack on a sample before committing.
5. Segment and personalise outreach around the shared technology context.
6. Comply with the privacy and anti-spam rules for your audience.

Go deeper

• The detection fundamentals: how to find out what a website is built with.
• Build the whole pipeline: how to build a B2B lead list from a website's tech stack.
• The concept behind it: technographics explained.

Want to skip the assembly? StackOptic lets you filter analysed sites by technology and audit scores and export them as a qualified lead list — start free.