Wappalyzer
Sales IntelligenceGive AI agents instant technographics: look up the full technology stack behind any website with Wappalyzer, browse the technology and category taxonomy, and track remaining API credits. Useful for enriching leads and qualifying prospects by the tools they already run.
What This Integration Enables
Technographics answer the qualification question most firmographic data cannot: not how big the company is, but what it actually runs. A prospect on a legacy ecommerce platform is a different conversation than one who migrated last quarter, and Wappalyzer detects exactly that, per URL, with categories, versions, confidence, and traffic rank. FlowRunner turns those lookups from a research chore into a standing enrichment layer: agents batch Lookup Technologies across new accounts, translate detections through the technology and category taxonomy, and write the results where sales will act on them. The credit economics get the same treatment: because every lookup spends from a finite pool, agents budget batches against the live balance instead of discovering the wall mid-list. - Enrich new leads and accounts with the detected stack behind their website, up to ten URLs per lookup - Qualify and route prospects by the specific CMS, ecommerce, or analytics platform the play targets - Run competitive and market research on technology adoption across a defined set of domains - Sync the taxonomy with List Technologies and List Categories so CRM picklists match what detection can return - Treat credits as the finite budget they are, monitored by agents and spent deliberately
Without FlowRunner
With FlowRunner
Use Case Scenarios
The account list that qualifies itself overnight
Sales ops imports three hundred target accounts into [HubSpot](/integrations/hubspot). Overnight, an agent checks the balance with Get Credit Balance, then works through the list with Lookup Technologies in ten-URL batches using cached results. Each account gets its detected stack written to custom properties, and accounts matching the play's profile, a specific ecommerce platform plus no reviews widget, get tagged for the relevant sequence. By morning, reps open a list sorted by evidence, and the first call of the day goes to the account whose stack says they are ready.
The displacement play with a live-scan gate
A team runs a campaign targeting companies on a competitor's platform. Cached lookups cover most of the list, but for the twenty highest-value domains the cache is old enough to matter, a prospect who migrated off the competitor last month is a wasted sequence. The agent queues those twenty for live scans, posts the list and the credit cost to [Slack](/integrations/slack), and waits. The ops lead approves, the live scans run, and two domains turn out to have already migrated. Those two get a different, better-timed message.
Market research without the intern month
A product team wants technology adoption across five hundred companies in a segment. An agent runs cached lookups through the list over several scheduled batches, resolves detections against List Categories so results group cleanly by CMS, analytics, and ecommerce, and appends everything to [Google Sheets](/integrations/google-sheets). The output is a live dataset with per-technology counts, refreshed on schedule, instead of a one-time spreadsheet someone assembled by hand. When the segment shifts, the next refresh shows it, and the analysis stays an artifact of the data rather than of the month it was compiled.
Human-in-Loop Highlight
Lookup Technologies with Live enabled is the spend decision in this connector: a live scan forces a real-time crawl and consumes materially more credits than a cached read, and the credit pool it draws from is finite and shared by every workflow in the account. An agent that quietly flips to live scanning because cached results look thin can drain the month's budget on one oversized batch, and the cost lands on every other enrichment job that suddenly cannot run. So live is never the agent's call. The workflow reads Get Credit Balance, assembles the specific domains where cached data is too stale to act on, and presents the batch with its credit price to a person. Cached lookups flow freely; the expensive path runs only after someone with budget authority says it is worth it, and the balance check afterward confirms what it actually cost.
Agent Capabilities
4 actionsDetection
1- Lookup Technologies Detects the technology stack of up to 10 comma-separated URLs per call, returning detected technologies with categories, confidence, versions, CPE, and traffic rank. Defaults to cached results; enabling Live forces a real-time scan at higher credit cost. Additional data sets such as company, social, and contact data can be included.
Account
1- Get Credit Balance Retrieves the remaining API credit balance for the authenticated account. Every lookup consumes credits, live lookups more, so this runs before large batches and after them, as the budget check on both ends.
Taxonomy
2- List Technologies Lists the technologies in the Wappalyzer dataset with slug, name, description, categories, and website, paginated. Used to sync the catalog of detectable technologies into CRM fields and matching rules.
- List Categories Lists all technology categories with numeric ID, slug, and display name, such as CMS, Analytics, and Ecommerce. The grouping layer that turns raw detections into filterable, reportable segments.
Frequently Asked Questions
What can FlowRunner do with Wappalyzer?
FlowRunner agents can run Lookup Technologies, Get Credit Balance, and List Technologies in Wappalyzer, plus 1 more action.
Does connecting Wappalyzer to FlowRunner require OAuth?
No. Wappalyzer connects to FlowRunner with an API key, no OAuth flow required.
Can Wappalyzer trigger a FlowRunner workflow automatically?
Wappalyzer doesn't currently expose triggers in FlowRunner. It connects as an action step inside workflows started by another trigger.
Start building with Wappalyzer
$100 in credits. No card required. Connect in minutes.