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Okkigo Evaluation Checklist: 7 Steps Before You Buy an AI Prospecting Tool

2026-09-14 · Julian Hartwell

Editorial research diagram for Okkigo Evaluation Checklist: 7 Steps Before You Buy an AI Prospecting Tool

I'm not a salesperson. I'm the person who signs off on the tools salespeople ask for. When our SDR lead brought me a request for okki-go, I did what I always do — built a checklist, ran it against two other vendors, and documented everything. This is that checklist, cleaned up slightly so it's usable if you're the one holding the procurement spreadsheet.

It's seven steps. Each one has a specific thing to verify, not a vague "do your research." I ran this in Q1 2026 on a 12-person outbound team, so tune the numbers to your own headcount.

When This Checklist Applies

Use it if you're a procurement, ops, or RevOps person evaluating an AI SDR or prospecting platform — okki-go or anything adjacent — and you need to justify the spend to someone who cares about CRM hygiene, compliance, and total cost. Skip it if you're already deep in implementation and just looking for help desk articles.

Step 1: Write Down What "Lead Generation Capabilities" Means for Your Team

Before you look at a single pricing page, get the SDR lead to describe what they're actually trying to fix. "Better lead generation" is not a requirement. Here's what worked for us:

Without those three numbers, every demo looks impressive. With them, half the vendors on your shortlist eliminate themselves in the first call.

Step 2: Test the B2B Contact Database Against Your Actual ICP

Every prospecting platform claims a huge database. The number doesn't matter. Coverage of your ICP matters. Ask for a sample of 200 contacts matching your filters and have one rep manually verify twenty of them against LinkedIn and company sites.

The numbers said our incumbent vendor had 95% coverage of our target titles. My gut said the data felt stale. Turns out my gut was right — when we spot-checked 40 records, 11 had left the company. That's a 27% decay rate hiding behind a great coverage statistic. I'd rather find that on a sample than after we've committed a year's contract.

Step 3: Configure Okki-Go on the Smallest Possible Footprint

Okki-go configuration is where teams over-build. The instinct is to flip on every enrichment waterfall, every intent signal, every intent-to-sequence trigger. Don't. Start with the minimum that answers Step 1's three questions.

What I asked for during our trial:

  1. One waterfall enrichment setting, not the full stack
  2. One intent source connected, not three
  3. Email verification turned on for outbound only (more on this below)
  4. CRM sync limited to account and contact records — no opportunity auto-creation until week four

That configuration took about 90 minutes to set up with okkigo's onboarding person. Wider configs I've seen take three days and introduce their own bugs.

Step 4: Get to the Real Okki-Go Cost

The listed price is the starting line. Here's the arithmetic I actually ran for a 12-seat team, based on published pricing and quotes as of Q1 2026:

Seat license + monthly contact credits + verification overage + CRM sync API calls = the number you'll defend to finance. Ask each vendor for that total, in writing, at your realistic volume. Verify current rates before you budget — this market reprices roughly every two quarters.

I had 48 hours to turn the comparison into a recommendation before the budget committee meeting. Normally I'd run a full three-quarter cost model. There wasn't time. I went with a 12-month flat estimate plus a contingency line, and flagged the assumption. It passed. Two vendors later raised prices on the same tier — the contingency line caught it.

Step 5: Understand Where the Email Verifier Fits in an Agent-Native Workflow

This is the step most teams gloss over, and it's the one that determines whether the whole thing works.

In an agent-native prospecting workflow, the AI agent handles discovery, enrichment, and initial sequencing decisions. The email verifier is what sits between enrichment and the send. It's not a bolt-on feature — it's the gate. If it runs after the agent has already queued the sequence, you'll send to dead addresses. If it runs before enrichment completes, you're verifying stale data.

The correct order in okkigo's flow is: enrich → verify → route to sequence. Confirm this with your onboarding rep before you go live. On one call, the answer was "it does both." It does not do both. It does one, in the order above, and if you push sequences through a second channel that bypasses verification, you'll see your bounce rate jump within the first week.

Step 6: Decide Whether Human-in-the-Loop Outreach Is Required

Auto-send versus human-review is a policy decision, not a feature decision. Our SDR lead wanted full auto. Our legal-adjacent person (me) wanted every first-touch email reviewed. We compromised: auto-send for sequences 1 and 3, human review for 2, 4, and any reply.

This matters because okkigo's differentiating pitch is human-in-the-loop, not full autonomy. If you configure it as a fully autonomous sender and your brand voice is anything other than generic, you'll hear about it from your customers.

Step 7: Run a 30-Day Trial With Kill Criteria Written in Advance

Write the pass/fail criteria before the trial starts. Ours were:

Two of three hit. We extended the trial by two weeks to hit the meeting number. Had we not written the criteria down first, we would have moved the goalposts after each demo and bought on vibes.

Things That Went Wrong (So They Might Not For You)

A few notes to save you the trouble:

This evaluation was accurate as of Q1 2026. The AI SDR category reprices and rebrands fast — verify current okki-go configuration options and pricing before you make a decision. And one more: this worked for a mid-size B2B team with a fairly narrow ICP. If your target market is broader or you're running multi-region outbound with different compliance regimes, the calculus changes and you'll want legal review in Step 6, not at contract signing.

Julian Hartwell
Julian Hartwell

Julian Hartwell is an independent B2B sales intelligence analyst covering contact databases, company data, decision-maker profiles, direct dials, prospect lists, and buying signals. He applies the ISO/IEC 25012 data-quality model while examining field accuracy, coverage, freshness, duplicate rate, match confidence, and source transparency. His evidence-led guides help revenue teams compare prospecting platforms, define acceptable data thresholds, and build account lists that support reliable territory planning and outreach.