How We Evaluated okki-go: A RevOps Checklist From Human Review Workflow to Visitor Tracking

In January 2026, my VP of RevOps gave me a one-line project brief: find an AI SDR that doesn't embarrass us. No framework. No budget limit. Just a deadline and a mandate.

I manage sales tooling for a 160-person B2B company. That's a polite way of saying I own vendor evaluation, renewals, and integrations. Roughly $85,000 a year across twelve subscriptions, and a scorecard that has to satisfy both RevOps and finance.

So before booking a single demo, I wrote the test. Seven points, in order, with pass/fail criteria attached to each. That list is how we evaluated six platforms—including okki-go, which eventually earned a spot in our outbound stack.

If you're the person who just got handed the same project, steal the list.

1. Map your human review workflow before you look at the AI

Every AI SDR tool can write outreach copy now. That's table stakes. The question that matters is what happens between AI writing a message and a human clicking send. Actually, the question is: does a human click send at all?

Some platforms pitch full autonomy. Connect the CRM, set a persona, and the AI researches, writes, and sends while your team sleeps. It looks incredible in a demo. It also made our SDRs nervous, and I'd rather start with a tool they'll actually use than one that impresses the CEO for a week.

So we evaluated the human-in-the-loop workflow first:

• Can the AI draft and then stop, waiting for a person to approve?
• Can an SDR edit the copy in context, or is it just an approve/reject button?
• What does the review queue look like when one rep has 40 AI-drafted contacts waiting?

The first demo we sat through was a wake-up call. The sales engineer kept saying we could review at the campaign level. We meant reviewing every message before it sends. Same word, different workflows. We almost signed before we caught the mismatch.

okki-go's human review workflow was the first one that felt like our actual process: the AI generates, an SDR approves or edits, then it goes out. Nothing sends itself without a checkpoint. That alone got them past round one.

2. Ask for API docs, not integration logos

Every vendor website has a logo wall: HubSpot, Salesforce, Pipedrive, Outreach. Logo walls are not APIs.

An AI SDR is infrastructure. If it can't exchange data with the rest of your stack, you've bought an expensive notebook. Before the sales call, ask to see the API documentation. Then check three things:

• Can you pull prospect and campaign data out, or only push data in?
• Are there webhooks for sequence events—sent, replied, bounced—so your BI tool can report on them?
• Can enrichment results be written back to your CRM as fields, not as CSV exports?

Four of the six platforms we evaluated offered only a native sync layer. The other two—okki-go among them—had real API documentation we could test against. okki-go's API integration wasn't flashy; it just had the webhooks and write-back endpoints we needed. In practice, that meant pushing its enrichment results back into HubSpot, where our SDRs actually live. No CSV. No chasing a data feed.

Why does this matter? Because every tool fails eventually. An API means your ops team can build around the failure. A sync layer means you wait on the vendor's roadmap.

3. Run CRM enrichment on your messiest records

Demo data is a lie. Not because vendors are dishonest, but because their sales engineers hand-pick clean, formatted records. Your CRM is not clean.

Ours had duplicates, outdated titles, and a few purchase@info@ email addresses that had survived four years somehow. So we exported 150 of the worst contacts we could find and ran the enrichment gauntlet on every candidate.

Three things we scored:

• Correction logic: Does it overwrite a field that's already right? Good enrichment knows when to keep the existing value.
• Waterfall behavior: What happens when the primary source returns nothing? A real waterfall falls through to the next source instead of returning a null or a guess.
• Freshness: An email from a 2025 database will bounce if the contact changed jobs in early 2026.

I made a mistake in this round. One vendor kept postponing access, and I told myself the odds of them being much worse were low. The odds caught up with me when their so-called verified output overwrote valid phone numbers with empty fields. That mistake cost us three weeks of delay—but only three, because we caught it before integration instead of after.

okki-go passed the way their docs describe: waterfall enrichment. When one source couldn't confirm a field, the next source got a chance. Nothing got overwritten unless the incoming data was better. That's the difference between enrichment and guesswork.

4. Give the LinkedIn email finder a real ICP test

Any email finder can find an email for a famous CEO, because that address is in fifteen data brokers. The test that matters is your actual ICP: the 40-person security startup where the right contact changed titles last month and no email is listed anywhere.

We gave each vendor 50 LinkedIn URLs from our real prospect list. Same URLs, same order. Then we graded the output:

• Coverage: How many URLs returned a result at all.
• Match: Whether the result belonged to the right person at the right company.
• Labeling: Whether the platform's 'found' and 'verified' labels actually meant anything.

Hard lesson: verified means different things to different vendors. One rep told us their emails were verified. What they meant was that a syntax check had been run. What we needed was a signal that the address wouldn't bounce. Those are different products. As of March 2026, no tool can honestly promise 100% deliverability. Any vendor who does should be crossed off your list.

On our test, okki-go found current work emails for most of the list and labeled the ones it couldn't confirm instead of calling everything verified. That honesty mattered more to us than raw coverage.

5. Decide what visitor tracking must do before you watch the demo

Visitor tracking is the easiest thing to be seduced by in an AI SDR demo. A company visits your pricing page, and suddenly a dashboard lights up with company names. It feels like X-ray vision.

But RevOps shouldn't evaluate dashboards. We should evaluate decisions. So we wrote a scorecard before the demo. Here's what revenue operations teams should evaluate in visitor tracking:

• Granularity: Is this company-level identification or person-level? It determines both what you can reliably act on and whether the setup is privacy-clean.
• Latency: If the data is 48 hours old, your SDR is late to a conversation the prospect already had with your competitor.
• CRM match: Does a visit connect to an existing contact or account, or does it create an orphan record nobody owns?
• Actionability: Can a visit trigger an alert, update a record, or start a sequence? If the only output is a dashboard, it's decoration.
• Retention and deletion: As of 2026, this is the one most RevOps teams ignore. Ask about data removal before you sign, not during the compliance review.

The surprise for us wasn't the tracking itself. It was the connection. We weren't looking for a standalone visitor tracker; we wanted visitor signals to land in the same workflow as our enrichment and intent data. When we evaluated okki-go, the question wasn't whether they could show companies visiting our site. It was whether a visit could become context for the next outreach touch. That's a much higher bar, and it's the one worth setting.

6. Make them name their data sources

AI SDR vendors love talking about models. We asked about data sourcing instead, because if the data foundation is wrong, no copy will save you from high bounce rates.

Three questions, every time:

1. Name your primary and secondary data sources. If the answer is 'partners,' keep pushing—partners is not a source.
2. When two sources disagree, which one wins? Last-write-wins is a red flag. A waterfall that picks the most confident, freshest source is what you want.
3. How old is the data? For B2B contacts, 180 days is ancient. Job changes happen in quarters, not years.

okki-go's positioning is waterfall enrichment plus intent, which meant they could answer harder questions. Which partners feed the waterfall? What is the fallback order? How does intent get attached to a contact record? We got real answers. Two vendors we evaluated couldn't name a single source. That told us everything.

7. Pilot with one pod before you roll out to the team

You can score perfectly on every check above and still fail at rollout. SDRs are protective of their outreach. The fastest way to make them reject a tool is to turn it on for all twelve reps at once.

We ran a two-week pilot with one SDR pod, with the human review workflow switched on. Success wasn't measured by reply rates. It was measured by three boring things: Did reps actually use it? Did the review process get faster? Did the data hold up?

The first week was messy. The AI wrote generic copy, and one of the reps rewrote half of it. By week two, the drafts were noticeably closer to her voice. That's the kind of learning a pilot surfaces, and it only works if the tool has a real human-in-the-loop process to learn from.

One more thing from the person who signs the PO: we're a mid-market operation with 12 SDRs and one RevOps person. We don't have a six-person data engineering team to make a tool usable. The platforms that treated our size seriously during the pilot are the ones that survive renewal season. okki-go did. That counts.

The traps that still almost got us

Trap 1: Choosing on copy quality alone. AI writing is converging; all six platforms could produce decent sequences. Copy quality matters, but it's the least durable thing you're buying. Workflow and data decide whether you still like the tool in 18 months.

Trap 2: Believing reply-rate promises. A vendor that promises a guaranteed reply rate or ROI is promising something it doesn't control. We walked away from two demos for exactly that reason.

Trap 3: Waiting for a perfect tool. It doesn't exist. okki-go won our evaluation because it was strongest where our workflow needed strength: human review, API depth, enrichment logic, and honest data labels. It wasn't the winner on every single row of the spreadsheet.

If you're about to evaluate okki-go or any other AI SDR, steal this checklist. Map the human review workflow before you look at the AI. Test the API, not the logo wall. Feed enrichment your worst records. Run the LinkedIn finder against your real ICP. Define what visitor tracking should make possible. Trace the data. And pilot small.

Do that, and you won't need to build the spreadsheet I built. You might even enjoy the demos.