OKkiGO Rollout Checklist: Install Command to First AI SDR Campaign in One Day

When I first started coordinating sales-tech rollouts, I assumed the install step was where projects would go to die. It was the opposite. After 40-plus implementation projects since 2023, many of them with go-live deadlines under 72 hours, the OKkiGO install command has been the smoothest ten minutes of the process.

What actually separates a clean go-live from a painful one is everything that happens after the install: enrichment logic, sending infrastructure, and making sure a human is still in the loop. If your VP just asked whether you can have OKkiGO prospecting live by Friday, this checklist is for you. I use this sequence with RevOps teams, SDR managers, and outbound agencies. Six steps. A small team can get through them in about one working day — I want to say that’s held up across the rollouts I’ve run, though don’t quote me on the exact hours. The email warm-up period is a separate line item, and no, you can’t skip it.

Step 1: Run the OKkiGO install command from a machine that stays on

Copy the install command from your OKkiGO dashboard under Settings → Install. The generated one-liner is scoped to your workspace, and it looks something like npx @okkigo/cli install --workspace your-workspace.

Paste it into a terminal on the machine that will actually run scheduled prospecting work. That sounds obvious, but I have watched rollouts stall because the install happened on a personal laptop that went home with someone two weeks later. The whole workflow went dark with it.

Wait for the command to finish, then refresh the dashboard. If the workspace shows connected, move on. If not, the usual cause is an expired workspace token. Regenerate the one-liner and run it again. Treat that token like a password — do not paste it into screenshots or shared docs.

Step 2: Connect your CRM and sending accounts before touching leads

This is infrastructure time. Your CRM (Salesforce, HubSpot, or whatever the team runs on) gives OKkiGO the account context. Your sending mailbox is where the AI sends from. Both connections need to be right.

Two checks I never skip:

First, confirm the CRM sync pulls the fields that matter: industry, employee count, past interactions, open opportunities. Weak field mapping means weak audience segmentation later.

Second, check domain authentication (SPF, DKIM, and DMARC) for every mailbox the agent will use. A missing DNS record can silently route messages to spam, and you will not know until after you have contacted a few hundred prospects.

If your domain is new or has never run an outbound sequence, budget two weeks of low-volume warm-up first. I realize that is the least convenient advice when the deadline is Friday. It is also the advice that has saved every client who took it.

Step 3: Set up OKkiGO data enrichment as a waterfall, not a one-shot query

This step is where OKkiGO data enrichment turns from a product feature into an actual revenue operation.

Here is how a waterfall works: when the first provider does not return a field OKkiGO needs — usually an email address — the record passes to the next provider in the sequence. If that provider comes up empty too, it flows further down the chain instead of sitting in your list as a half-empty row.

Why does that matter? Every data provider has weak spots. One is strong in US tech but thin in EMEA manufacturing. Another covers EMEA well but lacks intent signals. A waterfall stops one provider’s weakness from becoming your data quality problem.

Pay attention to the order. Put the strongest provider first so obvious matches resolve quickly, then layer fallbacks underneath.

And set a rule for incomplete records: if enrichment returns a company but no valid contact, move the record to a review list rather than letting it flow into a send segment. An email address that cannot be confirmed is not a lead; it is a risk.

One honest caveat: no email verification process is ever 100 percent accurate. People change jobs, data decays, and even a perfectly verified inbox may belong to someone who left the company last month. Treat enrichment as a filter, not a promise.

Step 4: Build your first segment with OKkiGO sales prospecting features

Now the fun part. OKkiGO sales prospecting features are agent-native — the platform researches and enriches instead of storing a static list. But it still needs direction.

Build your first segment around two things: fit and signal. Fit means firmographics: industry, company size, location, technology stack. Signal means a reason the prospect should care right now. Intent data helps here. If an account matches your ICP and has recently been researching the problem your product solves, that is a stronger first target than a perfect-fit company that has not shown category interest in a year.

Keep the first segment small. 500 to 1,000 well-chosen accounts is plenty. You are testing the motion, not trying to hit an unrealistic volume number on day one.

Do not send AI-generated email to people whose company you would not pitch manually. If you would not prospect them as a human, do not prospect them as an AI.

Step 5: Configure email automation that keeps a human in the loop

OKkiGO email automation should feel a bit under-engineered in the first campaign, not over-engineered. A three-touch sequence is a solid default.

Day 0: a short message that references something specific about the prospect. Day 4: a follow-up that adds a relevant customer outcome. Day 9: a polite breakup email that asks whether you should stay in touch for later.

Automation rules matter as much as the copy: pause immediately when a prospect replies, notify the assigned rep, and let a human take over. Email automation handles the repetitive work; it does not replace the person who has to respond intelligently.

This is the human-in-the-loop part that I push hardest. AI sales assistant features should draft and research. Replying, negotiating, and deciding if a lead is real is still human work. If no one is available to review replies this week, do not launch this week. It is that simple.

Being late to a deadline is cheaper than being the team that let an unattended campaign run for three days.

Step 6: Pilot 50–100 prospects before you scale

There is one step people skip when they are in a hurry, and it has saved me more times than I can count: send the campaign to yourself and a couple of internal test inboxes before the first real prospect ever sees it.

Open those test emails. Check rendering, links, the unsubscribe footer, and the personalization tokens. Ask yourself honestly if you would reply.

Then, and only then, send the campaign to 50 to 100 real prospects. Wait 48 hours and look at the data: how many bounced, how many opened, how many replies arrived, whether any spam complaints came in. One batch of 80 emails will not produce a statistically meaningful reply rate, and I would not try to read one as a trend. It will tell you whether the infrastructure works and whether the first replies look human.

If the results look healthy, move to the next batch. If not, pause and fix. Put another way: the goal of this step is not to impress anyone with volume. It is to learn what your actual sending reputation looks like before you stake real pipeline on it.

What is an AI sales assistant, and when should a B2B sales team use one?

A fair question, because not every B2B team should run an AI SDR tomorrow. An AI sales assistant for prospecting — like OKkiGO — combines list research, enrichment, intent data, drafting, and email automation into one workflow. The value becomes obvious when your sales motion has a repeatable shape.

Use it when you have a defined ICP, a large addressable universe, and a sequence that follows the same structure every time. That is where AI drafting and research create real leverage. Instead of having your SDRs spend two hours researching and writing before they send one email, the AI does the heavy lifting and the human reviews. Your team gets more touches out the door without losing the personalization that makes replies happen.

Do not use an AI sales assistant when your entire strategy depends on ten strategic relationships and each email is a hand-crafted piece of senior-level outreach. It can still help with research, but the bottleneck there is relationship building, not volume. Also do not use it if you have no clear ICP, no clean data source, and no capacity to review replies. AI will not fix a broken prospecting strategy. It will just help you execute it faster.

Common mistakes in a rush rollout

Here are the mistakes I keep seeing when a team tries to compress this setup into a day:

Installing on a machine that will not stay operational. The command works, the dashboard says connected, and then the laptop goes offline and everything stops. Install on the machine that will run the operation, not the one that is convenient.

Trying to enrich every field before sending. Enrichment is a filter, not a one-time project. Enrich the fields that matter for your first sequence, send the pilot, and let the waterfall keep cleaning the rest in the background.

Building a 12-touch sequence for a first test. Keep it to three touches. You have not learned anything yet. You do not know which message angle works, which subject line gets replies, or what timezone your prospects actually respond in. A giant sequence just multiplies the number of things that can go wrong.

Scaling before the domain is ready. If the domain is new or unproven, all the automation in the world will not help. In March 2025, I watched a client choose between launching late and launching with an unverified domain. The launch looked better on the calendar. It cost them the following two weeks of deliverability recovery.

The pattern under every one of those mistakes is the same: urgency convinced someone that certainty was optional. In a rush rollout, certainty is the thing you are actually paying for. If the options are a path that is fast and risky and a path that is slightly slower but verifiable, choose verifiable. The install command is the easy part. Having the discipline to make the rest of the system reliable under a deadline is the actual skill.