Lead Generation Software Checklist: What RevOps Should Evaluate (Kaspr vs Seamless.AI)

I manage software purchasing for a 45-person B2B company. We spend roughly $120K a year on SaaS tools across about 12 vendors, and I report to both operations and finance. Sales leaders sign off on tools; I'm the one who has to make sure the contract doesn't create headaches six months later.

If you're a RevOps lead deciding on lead generation software, you're probably looking at tools like Kaspr, Seamless.AI, Lusha, Apollo, ZoomInfo... The specs all look similar on paper. The problem is that most evaluation checklists miss the things that actually cause pain after the demo.

This is not a review. It's a checklist I've built from buying—and cleaning up after—several prospecting tools. It has 8 steps. If you're comparing platforms for a new team or replacing an existing one, this will get you past the marketing page.

Step 1: Map the Actual Workflow First

Most evaluation checklists start with “database size” or “pricing.” That's backwards. Start by asking: where is your SDR actually going to use this tool?

If your team works inside LinkedIn all day, the tool's LinkedIn integration isn't a nice-to-have—it's the main interface. Kaspr, for example, is known for its Chrome extension that sits on LinkedIn profiles. You see a profile, click, and the data appears. That's a very different workflow than having your SDRs log into a web portal, build a list, and export a CSV.

We've had more than one tool get adopted because the path of least resistance is in the browser, and another expensive platform ignored because it required opening a separate app. (Should mention: we also looked at Seamless.AI, which has a desktop app and web app. It's powerful, but “powerful” means nothing if the team won't use it.)

Checkpoint: Write down where your SDRs currently spend their prospecting time. Then ask each vendor where their tool lives in that workflow.

Step 2: Audit the Lead Database, Not Just the Record Count

From the outside, lead generation software looks like a giant contact database. The reality is that the database is just raw material. What matters is what's underneath: Where did the records come from? How often are they refreshed? How many are duplicates or already dead?

A lead database with hundreds of millions of contacts sounds impressive. But if 20% of those emails bounce, your domain reputation suffers, your CRM gets junk, and your SDRs stop trusting the tool.

I'm not saying record count is irrelevant. I'm saying it's a quantity metric, and the quality metrics are harder to evaluate in a demo. Ask for a sample list that matches your ICP. Run it through your own checks. If a vendor won't give you a sample, that's an answer.

Step 3: Ask How Email Verification Actually Works

This is where the confusion starts. A lot of salespeople say “we verify emails” without being specific. I'm not an email engineer, so I'll offer my best understanding: verification typically includes a syntax check, a domain/MX record lookup, and sometimes a mailbox-level check.

The mailbox check is the one you need to ask about. Some tools only verify that the domain can receive mail—not that the specific inbox exists. Others attempt an SMTP handshake, which can detect invalid mailboxes but can also produce false negatives, and some providers won't do this at scale for privacy and abuse reasons.

So when a platform says “98% deliverability,” ask what that number is based on. In my experience, “verified” doesn't mean “guaranteed.” It means “passed the checks we ran.” That's not the same thing.

I want to say the best line I ever heard from a sales rep was: “Our verification is good enough for our room, not for a lawsuit.” He was trying to be honest. I appreciated it.

Step 4: Check Data Freshness and Intent Signals

What should RevOps teams evaluate in lead generation software? In my view, data freshness is too often ignored. A record that includes someone's work email from 2019 is not a lead. It's an archaeology exhibit.

Ask how often the dataset is refreshed and what happens when an email bounces. Does the platform automatically suppress that address? Is there a feedback loop? Or do you just get a CSV and figure it out yourself?

Intent data is another one. It's easy to bolt an “intent” label onto a database. The harder question is: what triggered the intent signal, and when was it captured? If the intent signal is three months old, treat it as stale.

Step 5: Test the LinkedIn Extension and Automation Boundaries

If you search “kaspr chrome extension linkedin,” you'll see a lot of happy SDRs. But beyond the demo, you need to test the extension under real conditions. How many profile views can you do per day before hitting a limit? How many connection requests can you automate? What does the extension do when LinkedIn changes its DOM—which it does often?

This is also where compliance matters. LinkedIn's terms of service are not a joke. If the tool gets your account flagged, the cost isn't just the subscription; it's the lost network and the time spent recovering. Ask the vendor how they handle LinkedIn's rate limits and changing rules. If they dodge the question, that's a red flag.

Step 6: Compare Apples to Apples: Kaspr vs Seamless.AI

I don't want to write a full review, because your team's needs will differ. But let's use Kaspr and Seamless.AI as a test case because they're often in the same consideration set.

I went back and forth between these two for about two weeks. On paper, Seamless.AI has a huge database, and the click-to-search desktop approach is interesting. But my gut said our SDRs would actually use Kaspr's LinkedIn extension more consistently because it's embedded in the browsing they already do. In the end, we trialed both and chose the one that fit our workflow. I don't want to say “chose” too strongly—we're still running Kaspr with a small team and keeping Seamless.AI on one seat for direct search.

If you're doing a Kaspr vs Seamless.AI comparison, evaluate:

• Workflow: Kaspr is LinkedIn-first; Seamless.AI is a desktop/web search app with a Chrome companion.
• Data source: Both pull from public sources, but check how they handle contact changes and deduplication.
• Pricing: Both vendors list pricing publicly. Based on what I checked in June 2025, Kaspr's entry-level paid plan is priced lower than Seamless.AI's. But the plans are structured differently, so compare credits, exports, and user seats instead of just the sticker price.
• Free tier: Kaspr has a free tier; Seamless.AI also has a free tier with daily credits. I might be misremembering the exact credit counts, but the point is to test the free tier before paying.

To be fair, I get why some RevOps teams prefer Seamless.AI. The database breadth is real. But the tool that wins the spreadsheet comparison isn't always the tool that wins the desk.

Step 7: Run a Real Pilot, Not Just a Demo

The demo is a performance. The pilot is a test drive on a road you actually drive.

Give the tool to 3–5 SDRs for two weeks. Ask them to use it in their actual workflow and report:

• Number of leads found vs. contacts found
• Bounce rate on sent emails
• Time from search to send
• Any times they gave up and switched back to manual search

The last one is the one most people don't ask. A tool can have perfect data but poor UX, and the SDR will still use old habits.

Step 8: Don't Miss the Procurement and Compliance Details

This is the “boring” step that can save you later. Ask for the contract's data deletion policy. When you cancel, can you export your enriched data? Or does the vendor keep what it scraped? What about GDPR/CCPA?

I've bought tools where the sales rep said “your data is yours” but the contract said something broader. Finance hated me. Actually, finance still mentions it.

A lead database isn't a one-way value proposition. It's a recurring operational risk. If the vendor's opt-out process is hard, your sending domain can suffer. Oh, and check the API rate limits before you connect it to your internal tools.

The Two Mistakes I See in Almost Every Evaluation

First, people focus on the size of the database and miss the verification workflow. That's why I put that step near the top. Second, people compare pricing lists without counting the cost of rework—bad emails, missed opportunities, and SDRs who stop using the tool.

To be fair, I've made both mistakes. In 2022, we chose a tool because it had a huge record count. After three months, the SDR team was back to using a spreadsheet. The renewal was painful to cancel.

The Bottom Line for RevOps

An informed customer asks better questions and makes faster decisions. That's the whole point of this checklist. You don't need to know every detail about every platform. You need to know the 8 questions that separate a useful tool from an expensive icon on the desktop.

And if you go into your next evaluation and ask “how does email verification actually work,” you'll already be ahead of most buyers.