Okki-Go vs Clay Was the Wrong Question. What RevOps Should Evaluate in a B2B Contact Data Platform

On March 12, 2025, I told our VP of Sales that the campaign scheduled for March 17 wasn't going out. The sequences were written. The SDR time was booked. The prospect database had 3,468 accounts in it. And none of that mattered, because I no longer trusted the data underneath it.

That's a hard conversation to have when your quarter depends on pipeline. What made it worse was that the data didn't look bad. It had job titles. It had company sizes. It even had phone numbers. It looked like a ready-to-launch list. It just wasn't.

I've spent seven years in revenue operations, mostly at B2B SaaS companies. I've seen what good contact data looks like when it's working, and I've seen what it looks like when it's silently rotting. The difference usually doesn't show up until you're already sending.

3,468 Account Records, and None of Them Safe

In late February, our VP announced an expansion push into the mid-market. The goal was simple: 3,500 net-new accounts, with the first outbound wave starting mid-March. For our SDRs, that meant they had to generate leads almost immediately. For me, it meant the data had to be ready before the marketing team started booking time.

It wasn't.

Our existing setup used a legacy prospect database that had been refreshed in quarterly batches. It was fine for accounts we already knew. It was not fine for net-new logos in a market we hadn't touched in two years. We exported the list anyway, ran it through enrichment, and got a match rate that looked respectable. Then we checked deliverability on a small sample. That's when the numbers started falling apart.

The hard bounce rate on the first sample was 4.7%. The spam complaint rate was higher than I was comfortable with. And when we looked at the records that did pass verification, a scary percentage had no recent engagement signal or intent data attached to them. We weren't building a campaign. We were building a list of people who might exist, at companies we hoped were still buying.

I paused the launch.

Our VP asked what we needed instead. I said something like, “We need the same number of accounts, but we need to know they're real, reachable, and actually showing buying behavior.” That was the moment I stopped treating a contact database as a static asset and started treating it as a decision input.

Okki-Go vs Clay Was the Wrong Question

Once we started looking for a replacement, everyone asked the same thing: okki-go vs Clay?

We did evaluate both. I built the spreadsheet. I mapped the workflows. And I eventually realized the comparison itself was a trap. We were asking “which tool is more flexible?” when the real question was “where should prospecting intelligence live in our stack?”

Clay is genuinely impressive. If we had a dedicated data engineer on the RevOps team, and if we wanted to build custom data models for every segment, Clay would've been a strong choice. The flexibility is real.

But we're a RevOps team of two, supporting ten SDRs. We didn't want to orchestrate enrichment, verification, and intent signals ourselves. We wanted a platform that could do the orchestration for us and still leave room for human judgment before anything went out.

That's what made the Okki-Go approach different in practice. Okki Go is agent-native: you set the prospecting objective, and the agent builds the research plan, pulls signals, enriches contacts, and gives you a shortlist to review. It also uses a waterfall enrichment model, which means it doesn't trust any single data source. If one provider can't confirm an email, it tries the next one, and only stops when it runs out of options.

For us, okki go for RevOps meant fewer SDR hours wasted on dead accounts. It meant sequences that started with better raw material. And most importantly, it meant human-in-the-loop review — the agent proposes, but a person approves the final list and the messaging. It didn't replace anyone. It made the people we had more effective.

The truth is that okki-go vs Clay was the wrong question because we asked it before defining our actual requirements. Once we wrote down what we needed, the choice became much easier.

What Should Revenue Operations Teams Evaluate in B2B Contact Data Platforms?

If you're a RevOps team going through the same exercise, here's the checklist we now use. It came out of a painful launch, and it has saved us from repeating the mistake.

• Verification method comes first. Ask how the platform validates email addresses and whether it depends on one source or a waterfall of multiple sources. Single-source verification is a single point of failure. A platform that checks across multiple providers and only keeps contacts that survive the waterfall is more trustworthy — but always test it on a sample list before you commit.
• Look for intent data as a core field, not an add-on. A contact record with a valid email and the right title is table stakes. The real value is knowing whether that account is showing buying signals now. We learned this the hard way: a “clean” list that has no intent behind it is still a gamble.
• Check whether the platform can generate leads incrementally, not just in bulk. Some tools are built for one-off lists. RevOps needs continuous prospecting — new accounts, updated contacts, fresh intent signals every week. If you have to re-run the same manual export every month, the tool isn't doing the work.
• Demand human-in-the-loop controls. There's a difference between AI-generated outreach and AI-assisted outreach. We only use tools that let our SDR leads review, edit, and approve before the agent goes further. If a platform tries to automate too much, you lose the ability to catch problems before they reach a prospect's inbox.
• Review compliance posture carefully. Under the FTC's CAN-SPAM rules, unsolicited commercial email must include a valid physical postal address and a working opt-out mechanism, and opt-out requests have to be honored within 10 business days (ftc.gov). If a data provider can't tell you where a record came from or whether the contact gave consent, that's a red flag. We also found our old sequence template was missing the postal address requirement entirely — that was a small fix, but it only surfaced because we slowed down and audited everything.
• Measure what actually matters: deliverable and relevant contacts, not raw records. A platform that gives you 100,000 contacts but only 40% of them are reachable is worse than a platform that gives you 5,000 contacts with a high probability of conversation. The first number feels good in a demo. The second one generates pipeline.

A year later, I'd add one more thing: the best B2B contact data platform is the one that fits the team you have, not the team you wish you had. Our situation is two RevOps people, ten SDRs, and no tolerance for maintaining a data pipeline as a side project. Okki Go fit that. If you have a larger data engineering team and a need for highly custom modeling, Clay might be the better fit for you.

This isn't a universal verdict. It's just the framework we use now — and the reason I check a prospect database for intent and verification before I check it for size.