Evaluating okkigo for AI Prospecting: A 6-Step Checklist from Someone Who Owns the Decision

In January 2026, our VP of Revenue asked me to evaluate AI SDR platforms. My role isn't glamorous—I manage the revenue tech stack, vendor contracts, and the operational side of tool adoption for a roughly 340-person B2B company. When he said "find something that works this time," I knew exactly what he meant. We'd already been through one failed prospecting rollout in 2024, and I was not eager to repeat that experience.

Full disclosure: I didn't evaluate only okkigo. I looked at six platforms, ran three pilots, and ended up recommending okkigo after a somewhat messy but thorough process. Along the way, I built a checklist I think any RevOps team should steal before committing to an AI prospecting tool—especially if you're the person who has to own the decision when something goes wrong.

Step 1: Map your prospecting workflow before the demo

Most evaluation cycles start with the wrong question. Teams ask "which tool has the best features?" when they should be asking "which tool fits the workflow we actually run?" If you can't describe your process from lead sourcing to verified contact to sequenced outreach, every platform looks the same in a demo.

Here's what we mapped first: our outbound team has 18 SDR seats. Salesforce is our system of record. We source from LinkedIn Sales Navigator, event lists, and a small amount of inbound intent data. Before any outreach, we verify emails, enrich records where possible, and route by territory. That sounds obvious, but writing it down surfaced something important—we didn't just need an email finder. We needed a tool that could handle verification, enrichment, and CRM hygiene as part of one continuous flow.

If you skip this step, you'll end up comparing AI agents on demo charisma instead of fit. Do the boring work first.

Step 2: Pressure-test the okkigo human review workflow

The thing that initially made okkigo stand out was also the thing I almost dismissed: the human review workflow. In their first demo, the AI agent researched prospects and drafted email sequences, but nothing went out until a human approved it. My first reaction was, "That's just extra work for our SDRs."

I was wrong, and our own history proved it. The tool we used in 2024 was fully automated. It generated and sent emails without meaningful human oversight. We thought we were scaling outreach; instead, we spent weeks un-enrolling prospects and cleaning up replies from people who were annoyed by emails that clearly weren't written by a human. That mistake cost us credibility with a few accounts we actually cared about.

So when we piloted okkigo, I evaluated the human review workflow with specific questions:

• Which messages require approval—all of them, or only ones flagged as risky?
• Can an SDR edit the AI's draft without breaking the sequence logic?
• Does the human review step create a useful audit trail for compliance?
• Can reviewers leave feedback that the agent actually learns from?

The honest answer is that the first two weeks of the pilot were clunky. Roughly 60% of the agent's generated messages needed edits from our SDRs. But that was the point. The edits made the outreach better, and the workflow gave our team control instead of leaving them to clean up after an autonomous agent. I'd rather pay for that certainty than gamble on a tool that promises zero human involvement.

Step 3: Open the hood on okkigo API integration

The okkigo marketing page says it integrates with Salesforce and HubSpot. That sentence is technically true and almost entirely unhelpful.

What matters is not whether the integration exists; it's what the integration does. I sat down with our Salesforce admin and whiteboarded the okkigo API integration before we signed anything. Here's what we tested:

• Sync direction: does enrichment data flow both ways, or just one way into the CRM?
• Field mapping: can you map okkigo's data to custom fields, or are you stuck with standard fields that don't match your sales process?
• De-duplication: what happens when the tool enriches an existing contact? Does it update the record or create a duplicate?
• Removal handling: if a prospect unsubscribes in the CRM, does okkigo respect that across all active campaigns?

Why was I paranoid about this? In 2024, we connected a prospecting tool to Salesforce and discovered too late that it created over a thousand duplicate contacts in a single quarter. Our SDRs lost trust in the data, and our ops team spent weekends cleaning it up. That kind of mess is expensive in ways that don't show up on the vendor's invoice.

I'm not going to claim okkigo was flawless here, but they handled the questions well. They showed us their API documentation, introduced us to an actual solutions engineer, and helped us set up a sandbox test before the production rollout. In my experience, that willingness to go deep is a better signal than any feature list.

Step 4: Validate CRM enrichment on your own records

CRM enrichment is one of those features that sounds simple until you test it on real data. The demo always looks great because the vendor uses clean sample records. Your CRM is not clean. It's full of outdated job titles, missing phone numbers, and email addresses from three companies ago.

We built a test set of 50 accounts and 100 contacts. In 18 of those cases, we already knew the correct work email because the contact had recently emailed our sales team. In the rest, we had partial data and wanted to see what okkigo could fill in.

The results were solid but not magic. The tool matched about 88% of the records to an enriched profile, and in the 18 cases where we knew the ground truth, it got 16 right. One wrong email was an address from a person who had changed jobs but was still listed under their old company in a third-party database. That kind of error is unavoidable in this industry—no enrichment provider has perfect data. What matters is whether the tool flags uncertainty instead of presenting everything with equal confidence.

One thing I liked: okkigo uses a waterfall enrichment approach, meaning it pulls from multiple sources rather than relying on a single database. In practice, that gave us better coverage on smaller accounts, where big providers often have thin data. If you're evaluating a platform, don't just ask about its data sources. Ask what happens when the primary source has no match. The answer tells you whether you're getting real enrichment or just guesswork.

Step 5: Give the LinkedIn email finder a real test

Every AI SDR tool claims to have a LinkedIn email finder these days. The differences only show up when you test them on your actual target accounts.

Our pilot included a list of 60 LinkedIn profile URLs for decision-makers at mid-market companies we hadn't contacted before. These were real prospects from our SDRs' existing lists—not a cleaned-up sample provided by the vendor. We asked okkigo to find verified email addresses and enrich the associated company data.

The tool found emails for 47 of the 60 profiles. Of those, 45 passed verification, which was better than the other platforms we tested. But I'm not going to quote that as a benchmark or a guarantee—it was one sample on one day with a specific list of accounts. Your results will depend on your ICP, your data hygiene, and the industries you target.

The more important lesson was about how we measured success. The metric that matters isn't "emails found." It's "emails that actually reach a real inbox and get a response." Email finding is only the first mile of the journey. If the rest of the platform doesn't handle verification, enrichment, and campaign management well, a LinkedIn email finder is just a standalone tool wearing a trench coat.

Step 6: What Revenue Operations Teams Should Evaluate in Visitor Tracking

This step is the one most teams get wrong, which is why I wanted to include it in this checklist. Visitor tracking is not the same as intent data, and it's easy to be impressed by dashboards that look like they're working while delivering very little.

Here's the question I recommend every RevOps team ask: Are we evaluating website visits, or are we evaluating buying signals? A raw visitor tracking dashboard will show you a high number of companies visiting your site. But if you dig into the data and find that 80% of those companies don't fit your ICP and most visits are from generic IP addresses, all you have is noise.

When we evaluated okkigo's visitor tracking, we looked at four specific things:

• Identification rate: what percentage of traffic gets resolved to an actual company? If it's under 40%, the data probably won't drive meaningful outreach.
• Account accuracy: can the platform distinguish between a decision-maker at a target account and a random person on a shared IP? This matters more now that remote work makes IP-based identification less reliable.
• Integration with outreach: does a visit trigger a meaningful action in the platform, like updating the lead's priority or adding them to a relevant sequence?
• Privacy considerations: how does the tool handle data collection, and does it respect consent requirements? Visitor tracking that creates compliance risk isn't a feature—it's a liability.

I also want to mention a trap we fell into with a previous vendor. They reported that 900 companies visited our site in one month. It looked impressive. When we actually filtered for accounts that matched our ICP and had engaged with specific content pages, the number dropped to 12. Twelve accounts with genuine buying signals is far more useful than 900 random visitors, but it doesn't make for an impressive slide deck.

The right question for visitor tracking isn't "how many companies?" It's "can this tool identify, prioritize, and feed accounts into our outbound workflow in a way that actually increases our hit rate?"

Warnings and Common Mistakes We Avoided

If you're going through this evaluation process, here are a few pitfalls I'd flag based on our experience.

First, don't let a discount override your judgment. One platform we tested offered a substantial annual discount if we signed within seven days of the pilot. That pressure is a red flag, especially when you're evaluating something that will write emails to your prospects under your company's name. A tool with poor integration or weak data ethics is not worth 30% off.

Second, don't make decisions based on the quality of the vendor's sample accounts. They'll always show you ideal use cases. Bring your own data, run your own test, and judge the results against your own standards. This takes more time, but it's the only way to learn what the tool will actually do in your environment.

Finally, I'd encourage you to think about what certainty is worth. When I compared the total cost of the wrong tool—wasted SDR hours, corrupted CRM data, damaged sender reputation, and the political cost of delivering bad news to leadership—the premium we paid for a platform with a real human review workflow and strong integration practices felt small. I've learned this the hard way across multiple vendor decisions: an inexpensive tool that creates problems is a luxury you can't afford.