Okki Go workflow for founders: a budget-first lead generation checklist

I am the person who reads outbound stack invoices line by line. For the past four years I have tracked every dollar our team spent on list building, enrichment, email validation and sending, mostly because I bought the wrong tools early and built a cost model to keep myself honest.

When founders ask me to review an Okki Go setup, I skip the demo. I want to see what happens in the boring parts: where leads come from, which emails pass validation, what the hard bounce report actually means, and where a human reviews the final batch. This checklist is for founders and small RevOps teams who want the benefits of an AI SDR without paying for it twice.

Here are five checks. They are not clever. They are the difference between a lead generation workflow and a spending habit.

Step 1: Lock your boundaries before the agent touches anything

Most lead generation problems are not lead generation problems. They are targeting problems. If you turn an agent loose with a vague instruction like find SaaS companies, it will find you a lot of companies. Very few of them will be worth a conversation.

Write your target down as data filters before you configure anything. Headcount range. Revenue band. Geography. Industry. Maybe one or two technologies you actually see in the accounts that close. Then add the only signal that matters enough to trigger outreach: raised a round, hired a VP Sales, posted a problem on LinkedIn, or similar.

This is where the small-team part matters. A five-person company does not need the same list volume as a fifty-person sales org. Agent-native prospecting only helps if it is pointed at a narrow enough segment to produce a list you could personally review in an afternoon. When I was smaller, this felt like a disadvantage. It is not. Narrow lists fail cheaper.

Checkpoint: if you lost access to every tool today, could you still describe the exact person you want to reach? If yes, proceed.

Step 2: Map the Okki Go agent workflow as a chain of handoffs

The Okki Go agent workflow is not one big autonomous process. It is a sequence of small jobs handing off to each other. I recommend mapping it before paying for extra credits.

Prospecting agent builds a candidate pool from the filters you defined in Step 1. Then the enrichment waterfall runs: it tries one data source, fills gaps from the next, and eventually finds the closest match for each record. Intent signals get scored after enrichment, not before. Email validation happens at the end, right before a human reviews the final batch and approves the send.

That order is a cost decision, not just a technical one. If you validate too early, you pay for verification on records that enrichment might replace. If you enrich after validation, you can end up sending to an email address that was never checked. Keep validation as close to the send as possible. It is the last thing standing between your copy and a bounce report.

I learned this one the hard way. In an older workflow, we ran verification immediately after list import because it felt tidy. By the time the campaign went out, many records had been updated by enrichment, and a fresh batch of obvious invalid addresses appeared anyway. We paid twice for the same mistake.

Step 3: Make email validation a gate, not a one-time task

Email validation is only valuable when it blocks something. If it just produces a report that nobody reads, it is decoration.

A founder once told me their email validation was done. What they meant was that the team had validated the list during import, weeks before Okki Go was ready to send. The list was not dirty at import time. It was dirty by send time. People change jobs. Domains expire. Companies get acquired. A verification performed at import is a snapshot, not a guarantee.

So I treat validation as a gate. Records pass through it right before they enter the outreach stage. Hard invalid addresses get dropped. Role addresses like sales@ or info@ get dropped or sent to a separate flow, because technically they might deliver but they are unlikely to start a conversation. Records marked risky or unknown need a decision. Do not leave that decision to the default setting.

If you are not sure how your provider treats catch-all domains, ask. Catch-all domains accept email for any address, which makes them look valid to many verification methods even when the specific person does not exist. That is not a reason to avoid the tool. It is a reason to know what the tool can and cannot see.

What should revenue operations teams evaluate in hard bounce rate?

I get asked this a lot, and honestly I do not start with a target percentage. I start with what is inside the number.

First, evaluate the definition. A hard bounce is a permanent delivery failure: the domain does not exist, or the mailbox does not exist. Some platforms mix in soft bounces like mailbox full or a temporary server timeout. Soft bounces are annoying, but they are not the same problem. If the rate mixes both, it does not tell you anything useful about list quality.

Second, evaluate the verification method. A syntax check only catches typos. A domain check can confirm the domain accepts mail but not whether the specific mailbox exists. A mailbox-level check is the one most likely to catch an invalid address. The tricky case is catch-all. Ask how many catch-all domains are in your list and what the vendor does with them. If catch-all records are counted as valid, your hard bounce rate will look worse later. If they are flagged as risky, your list will look dirtier now. Either approach is fine. Not knowing which one you have is the real risk.

Third, compare the rate before sending with the rate after sending. The number an email validation tool gives you is an estimate. The number from your sending platform is what actually happened. I compare both weekly. If validation said near zero and the campaign shows a 3% hard bounce rate, the gap tells you something: the list is old, the verification method is weak, or the sending setup is hurting you. RevOps teams should evaluate the gap, not just the percentage.

Fourth, look at when bounces arrive. Some bounces are instant. Others trickle in over 48 hours because the receiving server delayed its response. If you only measure hard bounce rate in the first hour, you will miss a meaningful portion of the damage. Watch the trend over the first few days after each send.

One rough band from my own tracking: most batches that pass a real mailbox-level check land under 1-2% hard bounce in actual sends. If a vendor claims a flat zero, ask what they are not counting. If a batch jumps well above that, ignore the overall score and find the segment causing it.

Step 4: Keep a human in the loop and track total cost

Okki Go describes itself as human-in-the-loop outreach, and that is not just a safety feature. It is a cost control feature. A human review step catches the mistake before it spends your sending budget on the wrong people.

Your review does not need to be heavy. For a small team, proof the first 20 records in each batch. Check whether the companies still match your ICP, whether the contact is still in the role, and whether the enrichment data looks plausible. Once the pattern looks stable for a few batches, you can widen the approval to a sample. You are not looking for perfection. You are looking for the moment when the agent starts drifting into broad targeting because the original filters were not specific enough.

Then measure the thing that actually matters: total cost per real conversation. The monthly subscription is only one line. Add enrichment credits, validation fees, sending infrastructure, the domain setup, and the hours you personally spend reviewing batches. Put that all in one spreadsheet and divide it by the number of replies that led to a real conversation.

That number will be ugly at the start. That is normal. What matters is direction. If total cost per conversation goes down over three months, the workflow is learning. If it stays flat while volume goes up, you are just spending more to get the same number of conversations.

And do not scale until the number works. An AI agent scales mistakes just as efficiently as it scales wins. If the unit economics are not there with 50 prospects a week, they will not magically appear at 500 prospects a week.

What I would avoid in the first 30 days

Do not buy a large annual plan before you have run three weeks of actual campaigns. The discount feels rational until you realize the workflow needs changes and you bought credits you cannot use.

Do not let risky records flow to the send queue by default. Decide what risky means to you. If you are not sure, send a small sample first and compare the result with what the validation tool predicted.

Do not mistake activity for progress. Sending more is not the same as learning more. A small list that gets reviewed, sent, measured and adjusted will teach you more than a giant list that burns through your budget in a weekend.

The Okki Go agent workflow for founders is not the part that looks impressive in a demo. It is the list boundary, the verification gate, the hard bounce review and the total cost spreadsheet. Set those up first. The AI part does the rest. That is it.