Okki-go vs Clay for RevOps: What to Evaluate in a B2B Contact Data Platform

Ask “Okki-go vs Clay?” in a RevOps group, and you’ll get opinions. Ask “which B2B contact data platform should we choose?” and you’ll get even more. The question that matters more is narrower: who actually sends the first touch, and what happens if the data is wrong?

I do quality and brand compliance reviews for a B2B sales data company. Full disclosure: that company is okkigo, so I have a horse in this race. I also review every customer-facing claim that goes out—roughly 200+ deliverables each year—and in Q1 2026 I rejected about 7% of first drafts because a phrase like “verified email” wasn’t backed by a verification method. That habit makes me an annoying buyer.

There’s no universal “best” platform. In my notes, three scenarios keep coming up:

• Scenario 1: a person clicks send. Human-led SDR/BDR outbound.
• Scenario 2: an AI SDR/BDR sends and follows up. Agent-led outbound that needs audit trails.
• Scenario 3: your RevOps engineers build the workflow. API-first, deterministic, developer-friendly.

The evaluation criteria change with the sender. If you evaluate all three the same way, it’s easy to end up with more contacts and no more pipeline.

Scenario 1: A human SDR or BDR clicks send

This is where “generate leads” usually starts. You want a prospect database, an enrichment layer, and a clean way to find current decision-makers. A human still writes the icebreaker, picks the sequence, and uses judgment about replies.

Here, the easiest mistake is comparing row counts. It’s tempting to think a bigger prospect database means better odds. But the spec isn’t “how many contacts.” It’s “how many contacts have a clear source, age, and verification level.” A sales team can’t use 10 million rows if it can’t tell which rows are safe to send on.

Ask the vendor to show one email’s verification path. The useful answer looks like: source = company website, job change detected 10 days ago, first enrichment source had no email, waterfall source B returned a work email, verified by mailbox ping on a specific date. That level of detail is what your SDRs need. It’s not what demo slides usually show.

Also ask what “verified” does and doesn’t mean. I once assumed “verified email” meant mailbox-level. I didn’t check. We found out after a burst of bounces. Per FTC guidance (last checked at ftc.gov/business-guidance/advertising-marketing in March 2026), marketing claims need to be truthful, not misleading, and substantiated. I apply that standard to data labels too. If a vendor says their data is perfect or that every email will land, I get more skeptical, not less.

Scenario 2: The AI SDR sends and follows up

When the AI can research, write, send, and follow up, the workflow becomes agent-native. Evaluation shifts from human-friendly labels to machine-readable evidence. I care less about how polished the email sounds and more about what the AI did when contact confidence was low. A lead that gets pushed into a sequence without a good reason isn’t a lead yet. It’s a risk.

This is the counterintuitive part for many buyers: the most important feature isn’t “AI chooses from our huge prospect database.” It’s the ability to say “not enough evidence, skip and explain why.”

Ask for a decision trail:

• Which source produced the email?
• Was it verified? When?
• If no verified email existed, did the agent wait, try LinkedIn, or skip?
• If a reply said “not interested,” did that status suppress future outreach?
• If an email bounced, did the workflow pause?

I don’t think any serious RevOps team wants an AI process with zero human review, no matter how good the tool is. If a platform says “the AI just runs,” ask who approves the campaign before it goes out. There should be a stage where a human can inspect a sample of contacts and messages. In my product reviews, if I can’t see that stage in the workflow, I assume it doesn’t exist.

Scenario 3: You’re comparing Okki-go vs Clay, and the builder is RevOps

This is where Okki-go vs Clay gets interesting. Clay is strong in the composition layer. If your strategy involves multiple data sources in a spreadsheet-like interface and you want to run enrichment row by row, the workflow is probably Clay-shaped. Okkigo is closer to agent-native prospecting: B2B data, waterfall enrichment, AI SDR/BDR, and API/npm access in the same loop. The tools overlap, but they live on slightly different parts of the pipeline.

If your RevOps team owns the build, compare by workflow layer, not by vendor name. Ask whether you can define a waterfall enrichment workflow in code. If source A has no email, should source B run automatically? Should the response preserve which source produced the email? Can you pass verification status and reason codes into your CRM?

If the API returns “email” as a plain string with no source or timestamp, your downstream automation will have to guess. And guesswork is the thing quality reviews are designed to remove.

Take the sender test before the demo

Which scenario are you in? Take the sender test:

1. If the next step in your campaign belongs to a named SDR or BDR, you’re in Scenario 1.
2. If the next step belongs to an AI agent that also owns follow-up, you’re in Scenario 2.
3. If the next step is a webhook or an API call with no human queue, you’re in Scenario 3.

Many teams are a blend. That’s fine. But evaluate one scenario at a time. Usually, I want low-confidence contacts routed to humans and high-confidence contacts handled by automation. If a platform can’t express that rule, it doesn’t matter how polished the interface looks.

What should Revenue Operations teams evaluate in a B2B contact data platform?

Source, verification, fallback behavior, and actionability. The number of contacts in a prospect database is a starting point, not a quality score. “Generate leads” is easy when duplicates and unverified records are allowed. Generating pipeline is harder because a lead has to be trustworthy enough to send on.

I’d rather spend ten minutes helping a RevOps team understand those questions than deal with mismatched expectations after a contract. An informed customer asks better questions and makes faster decisions. That’s the kind of B2B data buying decision I want to see.