Playbooks / Recipe
Qualify accounts by what they run, then find the people
Detect a domain's public web technologies with one flat-rate call, drop the accounts that fail the test, and only then spend per-result search credits on finding people. A stack detection is a fit signal, not intent — we sell no intent feed. web_tech_stack bills on any 2xx, including one that finds nothing.
Pattern: domain list → web_tech_stack filter → people search → email finder → email verifier
In short: A RichAPI recipe for filtering an account list by detected web technologies before spending on contact data. POST https://api.richapi.ai/api/v1/web_tech_stack takes a URL and returns detected public web technologies at a flat credits_per_call rate; it bills on any 2xx response including one where nothing is detected. Surviving domains go to POST /people_search, which bills per result returned, then POST /email_finder and POST /email_verifier, which are waterfall endpoints where a 2xx carrying success:false returns billed:false and costs zero. Detection is limited to technologies observable on public web pages. RichAPI does not sell intent data and a stack detection is not evidence of purchase intent. Authentication uses the x-api-key header. New accounts get 100 free credits and require no credit card.
Last updated November 4, 2026
Credit math
Worked example at stated counts: 500 domains × 1 credit on web_tech_stack = 500 credits, charged on every 2xx including empty detections. 90 domains survive the filter; people_search returns 4 contacts each = 360 results at 0.1 = 36 credits. 360 email_finder calls at 5 = up to 1,800 credits less every miss, which bills zero. 1 credit per web_tech_stack call, 0.1 credits per people_search result, 5 credits per email_finder call
ROI math
The filter is the cheapest call in the chain and it runs first, so 82% of the list is eliminated before any per-result or per-contact spend. Convert credits at your tier rate on /pricing.
Owned internally by
GTM engineer, or a founder doing their own account selection
Best for
Products with a hard technical prerequisite or a named displacement target: integrations, migration tools, analytics, anything that plugs into a specific platform
- 01
Sign up at app.richapi.ai and copy your key.
100 free credits, no card.
- 02
Clean your domain list first — clean_domain strips tracking junk and resolves a messy URL to a root domain, at a fraction of a credit, before you spend a whole one detecting a stack.
- 03
Run web_tech_stack per domain and store the full detection, not just your pass/fail verdict. Next quarter's filter will be a different technology.
- 04
Filter to the accounts that meet your actual prerequisite.
Be strict — this is the step that saves money.
- 05
Run people_search on survivors for the two or three titles that own the technology, then email_finder and email_verifier.
Check what web technologies acme.com, globex.com and initech.com are running.
Tell me which of them use Shopify. For those, find two ecommerce or growth
leads per company and get their work emails.
Read the word "intent" carefully
This recipe finds companies that run a particular technology. That is a fit signal. It tells you a prerequisite is met, or that a competitor is installed, and both are good reasons to write to someone.
It is not intent. Nobody raised a hand. We do not sell an intent feed, we are not going to pretend otherwise, and a page that blurs the two is selling you a story about a <script> tag. The tag is real. The buying cycle is a guess.
What makes stack data worth paying for is that the guess is cheap to make and easy to check. One flat-rate call per domain tells you whether the account is even eligible, before you spend anything on people.
Filter first, because the filter is the cheap call
Most teams run this chain backwards. They build the contact list, then wonder which accounts were worth it. The cost lands in exactly the wrong place, because contact data is the expensive end.
web_tech_stack takes a URL and returns the technologies it can detect on that site. It is a flat per-call charge and it bills on any 2xx — including a response that detects nothing, which is a real outcome on a site built out of one JavaScript bundle. That is not a waterfall endpoints endpoint and the miss-free rule does not reach it. Budget for the full list, not the survivors.
Then, and only then:
people_searchon the domains that passed, billing per result returnedemail_finderandemail_verifieron those people, both waterfall, both zero on a miss
What tech-stack filtering costs, at stated counts
| Step | Count | Rate | Credits |
|---|---|---|---|
web_tech_stack |
500 domains, all billed | 1 / call | 500 |
people_search |
90 survivors × 4 = 360 results | 0.1 / result | 36 |
email_finder |
360 calls, 210 hits | 5 / call, misses free | 1,050 |
email_verifier |
210 calls | 2 / call | 420 |
Rates used above: 1 credit per web_tech_stack call, 0.1 credits per people_search result, 5 credits per email_finder call, 2 credits per email_verifier call.
Run the same 500 domains without the filter and the finder step alone is roughly five times larger. The filter costs 500 credits and removes 410 accounts before the expensive calls start. Convert to currency at your tier rate on the pricing page, and note that clean_domain at half a credit is worth running first on any list that came out of a CRM export full of ?utm_source= tails.
Where tech-stack filtering goes wrong
Detected is not deployed. A tag on the marketing site says the marketing team evaluated something. It does not prove the product team runs it, that the contract is current, or that anyone internally likes it. Detection reads public pages; it cannot see the admin panel.
Tag managers hide everything. A site that loads nearly every third-party script through one tag manager gives up very little at the HTML level. Treat a thin result as unknown, not as absent — writing "I noticed you don't use X" to a company that does is worse than not writing at all.
Stale tags outlive the tool. Dead scripts sit in templates for years after the subscription lapsed. If your pitch is a displacement pitch, say "it looks like you're running X" and let them correct you. That sentence survives being wrong. "You're running X" does not.
Detection depends on what the page exposes. Some stacks are simply not visible from outside. An empty result is a limit of the method, and we would rather write that down than sell you a coverage claim.
Plays related to tech-stack filtering
Filtering on what a company runs pairs naturally with filtering on what it is doing: see hiring signals to outbound for the staffing version, or the use-case index for the rest. To score the survivors rather than pass/fail them, ICP filter with AI enrich runs an LLM over the enriched record. The endpoint itself is documented at the tech stack lookup API, and people search covers the contact step.
Tech-stack filtering FAQ
Is this intent data?
No. It is technographic fit. We have no intent product, and a detected technology is not a signal that anyone is shopping.
Does a call with no detections still cost me?
Yes. web_tech_stack bills on any 2xx, empty included. Only the waterfall endpoints — email_finder, email_verifier, phone_finder — return billed: false on a miss. A non-2xx never bills, on any endpoint.
How current is the detection?
It is fetched when you call. There is no cached technographic index behind this and we are not selling you index size, so re-run the filter when it matters rather than trusting a result from six months ago.
Can I filter on the absence of a technology?
Carefully. Absence in the response means "not detected," which is not the same as "not used," for all the reasons above. Build the pitch so being wrong is survivable.
Can I call this from Claude or Cursor (MCP)?
Yes. web_tech_stack is exposed as a tool on the hosted MCP server, so you can qualify a handful of domains conversationally before you commit to scripting the batch.
Do I need a credit card?
No. 100 free credits on signup.
Try tech-stack filtering: 100 free credits, no card
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