What Should Revenue Operations Teams Evaluate in Lead Generation? A 7-Checkpoint Quality Checklist

I'm the quality and brand compliance manager at okkigo, which you'll also see written as okki-go depending on which help doc you land on. My job is reviewing outreach campaign packages before they reach customers: contact lists, enrichment records, sequence copy, sending setup. Roughly 180 per quarter, maybe 200 when things get busy. I'd have to check the tracker to give you a precise number.

The question I hear most from RevOps teams is: "Is okki go an AI SDR?" It's a reasonable question, and I'll get to it. But the better question underneath it is broader: what should revenue operations teams evaluate in lead generation tools before they buy — or renew?

I used to think the biggest database automatically made the best prospecting tool. Then I started reviewing different platforms' outputs side by side. A bloated database full of stale records can look impressive in a demo and quietly fail your outbound motion. So here's the checklist I'd want any RevOps team to run, whether they end up using okkigo or not.

1. Write the spec before the demo

A sales demo without a written spec is theater. I reject about 13% of first-pass deliverables in 2026, and almost every rejection comes back to the same root cause: the output didn't match an agreed spec.

Before you talk to a vendor, write down what a good prospect actually looks like for your business:

• Which industries, company sizes, and geographies are in your ICP?
• Which contact roles do you need to reach?
• Which triggers or intent signals make an account worth pursuing now?
• What fields must be present on every record?

An informed buyer isn't harder to sell to. An informed buyer is easier to build for, because the requirements are on the table. Quality starts with a clear requirement — not with the demo.

2. Trace data provenance to the source

Every lead generation vendor aggregates data from somewhere. The question that matters is whether that "somewhere" is a reputable original source or a third reseller of an already-old file.

Ask these directly:

• Where does firmographic data come from?
• Where do contact emails come from, and how fresh is the source?
• How is enrichment handled when one supplier lacks a match?
• Do they use a waterfall approach with multiple suppliers, or do you inherit one vendor's gaps?

The way I see it, a tool that runs a waterfall across multiple enrichment sources is more likely to cover your TAM than a tool that depends on one dataset. But don't take the word "verified" at face value. Ask what verification means — and when it last ran on the specific records you would receive.

3. Inspect the email verification method

Here's something vendors won't always tell you: email verification is not a binary. It's a spectrum.

Some tools only check syntax. Some check whether the domain has an MX record. Some do an SMTP handshake to see if the mailbox accepts mail. Others go further and check role-based addresses and known spam traps. These methods produce very different quality levels.

• What verification method is used at the point of export?
• How are catch-all domains handled?
• What percentage of records are marked as risky versus verified?
• Can you audit a sample of your own target accounts?

If a vendor claims 100% accurate email verification, run the other way. Verification catches a lot, but it doesn't catch everything. The honest answer includes a confidence tier.

4. Look for real SPF, DKIM, and DMARC guidance

There's a reason people search for "okki go SPF DKIM DMARC guidance" specifically. Deliverability is where a lot of lead generation projects go to die.

Since February 2024, Google and Yahoo require bulk senders to authenticate email with SPF, DKIM, and DMARC. If your outreach platform sends to Gmail or Yahoo addresses, and you haven't set these up properly, you're not running a lead generation problem. You're running a spam-folder problem.

A quality platform should give you more than a generic help article. As part of onboarding, it should provide:

• A step-by-step DNS setup flow for SPF, DKIM, and DMARC records
• Exact DKIM selector values for your sending domain
• DMARC record text, including a reporting address
• Pre-send validation that checks whether your records are live before the first campaign goes out

In my opinion, this is the most undervalued checkpoint on this list. It's not glamorous, but skipped SPF/DKIM/DMARC guidance is exactly the kind of quality gap that delays a launch by weeks.

5. Pin down what "AI SDR" actually means

Back to the question: is okki go an AI SDR? Yes, in the sense that okkigo uses AI agents to research accounts, build lists, enrich contacts, and draft sequences. No, in the sense that it's not a fully autonomous bot that sends without a human reviewing the output. Human-in-the-loop still matters.

Here's what you should evaluate when a tool calls itself an AI SDR:

• Where does automation stop and human review begin?
• Can you lock the AI to your ICP and account list, or does it chase volume?
• What guardrails prevent AI from producing phantom facts, fake intent signals, or off-brand messaging?
• Who reviews the AI-generated messages before they go to prospects?

Look, I'm not saying AI SDR tools don't replace repetitive work. They absolutely do. But a tool that replaces your judgment along with the repetition is not a tool. It's a liability.

6. Fit-check the tool for account-based marketing, including LinkedIn connection data

If your GTM motion includes account-based marketing, you need more than random lists and spray-and-pray volume. Test the tool against your actual ABM workflow:

• Can you upload a list of 500 target accounts and resolve contacts only within those accounts?
• Does the tool layer intent data onto the accounts you care about?
• Can you exclude accounts outside your ICP?
• Does it integrate with Salesforce or HubSpot without data loss?

One specific area people overlook is LinkedIn connection data. Some tools scrape LinkedIn aggressively and claim to offer automated connection requests. That's a red flag. If a platform risks your team's LinkedIn accounts through terms-of-service violations, the cost isn't worth it. What you want is clean, compliant use of LinkedIn connection information as enrichment — not a tool that automates your reps' networking for them.

7. Build the quality loop before launch

Quality isn't a one-time audit. It's a loop. When you evaluate a lead generation tool, you should also be designing how you'll measure it after launch.

Start with a small batch. Maybe 200 to 500 contacts across a representative set of accounts. Then review:

• Bounce rates and bounce reasons — not just the overall number
• Replies by type: positive, negative, out-of-office, unsubscribe
• Coverage gaps across your priority account list
• Which AI-generated angles are working and which feel generic

If the tool automates sending, it should also alert a human when something goes off-pattern — not just silently keep going.

Common mistakes I see during evaluations

After running quality reviews on dozens of campaigns, these are the mistakes I see repeated:

• Treating SPF, DKIM, and DMARC as "IT's problem" and skipping the deliverability review entirely.
• Choosing a tool based on total database size rather than coverage within your ICP and target accounts.
• Assuming "verified email" means the address was confirmed yesterday.
• Letting AI write messages with no human review step before sending.
• Judging campaign quality after three days instead of giving the quality loop enough time to produce real signal.

Full disclosure: I work at okkigo, so I'm not a neutral observer in this conversation. But this checklist is the same one I'd walk through with any RevOps team — because an informed customer asks better questions and makes faster decisions. Write the spec. Inspect the data. Respect email authentication. And never automate past the point where you can still catch a mistake.