These aren't edge cases. They're the everyday reality for users of traditional enrichment tools.
Traditional enrichment tools charge you credits whether the data is accurate or not. Wrong phone number? Still charged. Bounced email? Still charged. Person left the company years ago? You guessed it—still charged.
Complex credit systems designed to be confusing. Different actions cost different credits. AI features burn through credits faster. No warnings until you're in 'credit debt.' And good luck getting a refund for bad data.
These tools require API keys, waterfall configurations, custom scripts, and weeks of training. If you're not a GTM engineer, you're not the target customer—even though you're the one who needs the data.
Annual contracts starting at $15,000+. Automatic renewals with 60-day cancellation windows hidden in fine print. Price increases baked in. Designed to trap you, not serve you.
These quotes come from G2, Capterra, Trustpilot, Reddit, and industry publications. This is what people actually experience.
"Clay generates a ton of hype but has often unreliable data"
"The data was inaccurate and incomplete, with numerous missing emails and incorrect domain names"
"Apollo verified contacts once and caches the verification status to save money. Result: Users download a 'Verified' lead that bounces"
"Enrichment fails 3/10 times at the Enterprise Level and 6/10 times at the SMB level"
"I called the number and was told the person left the company 15 years ago"
"Killed my credits in 10 minutes on the trial just trying to see what it could actually do"
"How do data enrichment companies get away with charging credits for wrong numbers? Wrong number should equal no credit charge"
"Account consumed 21,369 credits against a 15,545 monthly limit with no usage warnings"
"Credits are charged even when contact information bounces or is incorrect, with no option for refunds"
"Clay isn't designed for non-technical sellers, but for GTM engineers. Sellers don't want to mess around with complex tables, API keys, and enrichment waterfalls"
"Expect a few weeks at least to navigate all of Clay's features"
"Most RevOps teams lack engineering resources comfortable with custom workflows, API integrations, and data scraping logic"
"You have to pay $800/month to integrate with CRM, which is pretty key for all the other possibilities"
"Clay looks useful, but is way too expensive to be practical for most businesses. At their price points, it might be worth paying a dev to connect APIs instead"
"Automatic renewal process is predatory. Customers must cancel 60 days before expiration, and the policy is hidden in contract documentation"
"Pricing starts around $14,995 with high additional hidden fees for add-ons"
"The HubSpot integration can be very buggy and break often"
"Teams spend weeks—or months—building custom scripts to sync data with CRM. These one-off integrations introduce points of failure"
"Integration setup can be finicky, especially around field mapping and duplicate management"
"Credits just straight up disappear and support is useless. Nothing worked without constant fixing"
"Terrible help desk service with multiple failed attempts to get help"
"Tried to ask questions relating to CSV enrichment and got completely ignored as a paying customer"
These tools are built on a fundamentally flawed model.
Contact data decays at 2-3% per month. A database that was 90% accurate in January is 70% accurate by December. These tools verify once and cache the result to save costs—passing the burden of bad data to you.
"Waterfall" enrichment sounds smart—try multiple data sources until one works. But when sources disagree, you get conflicting information. Outdated records from one source override fresh records from another. The complexity creates inconsistency.
Their business model rewards credit consumption, not data accuracy. Why invest in quality when you get paid whether the data works or not? Why make the tool easy to use when complexity burns more credits?
You can't see where the data comes from. You can't verify it yourself. You can't know which records are likely wrong. It's a black box that expects you to trust it blindly—and pay for the privilege.
We built Helioscope to solve the problems that traditional enrichment tools ignore.
Every piece of data comes with citations. See exactly where information came from so you can verify it yourself. No more black boxes.
Know which results are reliable and which need review. Our confidence scoring tells you when the AI is certain and when it's uncertain—so you can focus your time where it matters.
Don't settle for pre-built enrichment fields. Define exactly what information you need and get it—without learning complex workflows or burning credits on trial-and-error.
| Feature | Traditional Enrichment | Helioscope |
|---|---|---|
| Pay for bad data | Yes - credits charged even for wrong/bounced data | No - you control what you pay for |
| Learning curve | Weeks to months to master | Minutes to get started |
| Data transparency | Black box - no visibility into sources | Full transparency with citations |
| Confidence scoring | None or unreliable | Built-in, calibrated confidence |
| Customization | Limited to pre-built enrichments | Define exactly what you need |
| Upfront costs | $800-$15,000+ contracts | $0 - pay as you go |
| Technical expertise required | High - need GTM engineers | Low - designed for analysts |
| Iteration speed | Slow - locked into workflows | Fast - real-time refinement |
Helioscope excels at research tasks that traditional enrichment tools struggle with.
"Does this company have a sustainability report?" "What technologies are mentioned on their careers page?" Questions that don't fit pre-built fields.
Analyze competitor positioning, pricing signals, product features, and market movements at scale with full source attribution.
Go beyond firmographics. Understand if a company actually needs your product based on their public communications and market context.
Research potential investments, acquisitions, or partnerships with verifiable, cited information you can trust.
Map out market landscapes, identify trends, and discover patterns across hundreds or thousands of companies.
Verify compliance-related information with full audit trails and source documentation.
Lost annually by businesses due to bad data (Harvard Business Review)
Of organizations don't trust their data for decision-making (2024 survey)
Monthly decay rate for contact data in static databases
