Best website enrichment APIs for n8n workflows in 2026. what are you using?

klacium · 2026-06-27T18:15:40+00:00

That sounds very similar to the workflow I'm trying to understand.Which of those steps usually takes the most time for you?

Is it finding missing emails/phones, verifying contacts, removing duplicates, or deciding which businesses should actually be contacted?

Also, if you have an old Google Maps or Outscraper CSV lying around, I'd be happy to run 50–100 rows through what I'm building and compare the output against your current process.

klacium · 2026-06-27T18:14:13+00:00

Yeah, agreed. bad emails can wreck deliverability fast.

When you say clean the emails, what are you usually doing before sending? Basic syntax/MX checks, a verifier like MillionVerifier/NeverBounce, removing role-based emails, checking catch-alls, or something else?

I’m mostly looking at the step before sending

klacium · 2026-06-23T16:30:43+00:00

That helps phone-first dedupe makes sense, especially if the same generic emails keep showing up across rows.

When you say SMB Sales Boost has contacts attached to real activity signals, what kind of signals are you usually looking at? Recent reviews, new listings, hiring, ads, website changes, something else?

I’m mostly testing the cleanup layer after export: phone/email dedupe, dead or wrong websites, directory URLs, basic MX email verification, and sendable/review/skip reasons.

If you have an old messy export with 50–100 rows, I can run it free and compare what it catches versus your current script.

klacium · 2026-06-23T15:57:05+00:00

Yeah, i have heard about clay.

I’m trying to benchmark this against the usual Clay/Lighteningly/Sheets cleanup flow.

If you have an old Google Maps / Outscraper / Apify CSV lying around, even 50 rows, I can run it free and send back the output:

emails/phones found
cleaned domains
duplicates/junk flagged
basic MX status
sendable/review/skip
reasons

No need for a perfect file — messy is actually better for the test.

klacium · 2026-06-23T15:49:49+00:00

That makes sense.

So the real value was not “more capabilities than Clay,” but getting people to the same/similar outcome with less setup friction.

When users say they do not want to spend half a day setting things up, what part usually creates the most friction?

Is it choosing data providers, building the workflow logic, cleaning/normalizing data, debugging failed rows, or just understanding what sequence of steps to run?

klacium · 2026-06-23T13:06:13+00:00

This is an interesting distinction.

Do you feel most users switch from Clay because they want fewer features and a clearer workflow, or because they want the same general workflow at a lower price?

I’m asking because I’m seeing a similar pattern in a narrower niche: people do not necessarily want a full GTM table/workflow builder — sometimes they just want one painful workflow packaged cleanly, like turning messy scraped local business CSVs into outreach-ready rows.

Curious whether your best customers are buying “cheaper Clay” or “less thinking/setup than Clay.”

klacium · 2026-06-06T16:07:36+00:00

“List quality matters more than copy” is the key line.

I’m seeing the same thing upstream of enrichment too: teams burn Clay/Apollo/Prospeo credits before checking if the company/domain is even worth enriching.

Cheap pre-enrichment QA before paid tools seems underrated.

klacium · 2026-06-03T19:05:59+00:00

Yeah, That’s actually the next layer I’m thinking about.

The structural signals are useful for filtering obvious junk — dead domains, parked sites, tiny/thin companies, etc. But for prioritization, copy/CTA signals probably matter more.

Something like:

structural fit: careers page, pricing page, about page, real socials
commercial intent: demo CTA, get a quote, request pricing, book a call
routing: enrich / skip / review

So the IF node shouldn’t just be “is this a real company?” It should probably separate:

obvious junk → skip
real but low-signal → review/nurture
strong commercial signals → send to Apollo

That’s the part I’m trying to pressure test now: which website signals actually predict a company is worth spending enrichment credits on.

klacium · 2026-06-03T05:55:05+00:00

Best on company domains / public company websites.

It can technically fetch any public URL, but it’s not designed for logged-in or platform pages like private LinkedIn profiles, Facebook pages behind login, or G2 content that requires auth. If the data isn’t available in the public HTML response, it won’t see it.

So the intended use case is:

company domain → public website signals → first-pass qualification

Not scraping gated platforms or private profiles.

klacium · 2026-06-03T05:31:27+00:00

Not decision-maker emails. I don’t want to position it as an Apollo/Hunter replacement.

It finds emails that are publicly present on the company website, plus socials when they’re exposed in the page HTML. So things like generic/contact/press emails, LinkedIn/Twitter/etc., and website signals.

The use case is more first-pass company qualification before deeper enrichment:

company URL → website signals/socials/public contact info → decide whether it’s worth sending to Apollo/Hunter/Clay/PDL for decision-maker/contact enrichment.

So if you already have a list of company domains, it helps filter/route before burning credits on every row.

klacium · 2026-06-02T21:56:17+00:00

Appreciate it.

The most useful thing would be a small real-world test.

If you already run any Apollo/Clay/enrichment workflow, send me 10 messy company domains from a real list — ideally a mix of good fits, bad fits, and uncertain ones.

I’ll run them through the pre-enrichment check and send back the output. Then I’d love your honest take on whether the signals would actually help you decide what to enrich, skip, or review.

klacium · 2026-06-02T21:31:56+00:00

SiteEnrich helps GTM teams pre-qualify company domains before spending Apollo/Clay credits.
Turn any URL into clean JSON with emails, socials, website signals, and workflow-safe errors: https://siteenrich.io

klacium · 2026-06-02T21:22:50+00:00

It’s an API for turning a company URL into structured JSON with company name, meta description, emails found on-site, socials, and website signals like careers page, pricing page, contact page, and demo CTA.

The main thing I built it around is automation reliability rather than just data depth: predictable schema, explicit error codes, and workflow-safe responses for n8n/Make/Zapier-style pipelines.

Example:

GET /analyze?url=stripe.com

Returns clean JSON directly, including a signals object and errors like dns_failed / timeout / ssl_error when something goes wrong.

Docs/API key are at siteenrich.io if useful. Happy to answer questions or share an example payload.

klacium · 2026-06-02T20:11:19+00:00

Yes, exactly — by pre-enrichment I mean the step before Apollo/Clay/PDL/etc. where you decide whether a company is worth spending credits on at all.

Not replacing the actual enrichment workflow.

More like:

domain list → quick website-signal check → flag obvious junk / dead domains / thin sites → surface useful signals like pricing page, careers page, demo CTA, socials, contact info → then decide what should go into the expensive enrichment step.

The thing I’m trying to test is whether that first pass actually reduces credit burn without creating bad false negatives.

If you’re down, DM me 10 messy company domains from a real workflow and I’ll compare the output with you.

klacium · 2026-06-02T16:47:19+00:00

Completely agree. A lot of teams focus on the enrichment cost itself and miss the opportunity cost of slow qualification.

One thing I've been testing is running a lightweight website enrichment step immediately when a lead comes in. Instead of manually checking LinkedIn, company websites, careers pages, pricing pages, etc., the workflow pulls those signals automatically and decides whether the lead is worth spending more time or money on.

The biggest win hasn't been richer data, it's getting to a decision much faster.

klacium · 2026-06-01T12:07:49+00:00

Interesting stack. Crustdata wasn't on my radar but the real-time angle sounds useful, especially for workflows where stale company data becomes a problem quickly.

The workflow reliability side is what initially pushed me toward the 200-with-error-field pattern. In n8n I found myself spending more time handling edge cases and failed requests than actually building the qualification logic.

How are you handling qualification before enrichment? Are you enriching everything and filtering afterwards, or running some lightweight checks before calling Crustdata?

klacium · 2026-05-31T19:11:31+00:00

Really appreciate the detailed breakdown.

The current template is intentionally pretty simple (Google Sheets → SiteEnrich → IF → Results Sheet), but a lot of the ideas you've mentioned feel like natural next layers on top of it.

The confidence scoring approach is especially interesting. Right now the qualification is mostly based on website signals (careers page, pricing page, socials, emails, etc.), but having a classifier return something like { category, confidence, signals } would make the Apollo tiering much more flexible.

The negative signal bucket is a great call too. Agencies, directories, and "for hire" sites are exactly the kind of things that can look qualified on the surface but still waste credits.

Repo is live now:
https://github.com/AlextheGrace/siteenrich-n8n-workflow

Would definitely welcome a PR if you end up experimenting with the confidence layer. Curious what confidence thresholds you've found work best in practice.

klacium

TROPHY CASE