Okki Go Contact Discovery in an AI Agent: The 4-Gate Config That Holds Up

If you're setting up Okki Go contact discovery inside an AI agent, configure it as a stage in a pipeline, not a lookup button. Waterfall the email lookup across at least three sources, run verification as a hard gate before any record leaves the agent, filter on intent before job title, and keep a human on the first touch. That setup took me about 90 minutes and roughly halved the number of bad contacts reaching our sequencer.

That's the whole answer. Everything below is why it works, where it broke when I got it wrong, and the cases where you shouldn't build it this way at all.

I run outbound operations at a B2B SaaS company. I've handled 60+ pipeline emergencies in seven years, including same-week list builds for enterprise accounts where a meeting had already been promised to an executive before anyone checked whether we could actually reach the buyer.

Why I'm writing this down

In March 2024, our VP of Sales told the board we'd add 40 qualified meetings in a quarter that was already six weeks old. We had four SDRs and a CRM full of contacts that hadn't been refreshed in 18 months. We rebuilt the list through contact discovery plus waterfall enrichment, spent about $2,100 on extra data credits on top of our existing subscription, and finished at 38 of 40. Three of those 38 came from records our old list had flagged as "unreachable" — the emails were simply wrong.

I've configured this three times since: once for a two-person team, once for an agency running outbound for 11 clients, once for our own RevOps group. Not all three went the same way, and I'll flag where they diverged.

What Okki Go contact discovery actually is

It's a lookup layer. You hand it a domain, a name, a LinkedIn URL, or a company, and it returns contact records — usually email, sometimes phone, plus firmographic fields. In agent terms, that makes it a tool call, not the agent itself.

The reason to put discovery inside the agent at all — agent-native prospecting — is sequencing. The agent can take an intent signal, decide whether the account qualifies, look up the right person, verify the address, and hand a clean record to outreach without a human moving a CSV between four tabs. That's the value. Not the lookup itself.

Where teams go wrong is treating the discovery tool as the entire lead generation stack. Discovery produces a record. Lead generation is the process that turns a record into a conversation, and that process has its own required parts.

So, to answer the question directly: lead generation features are the components that move a stranger from "we know they exist" to "they replied." In practice that's six things — discovery (finding the person), enrichment (filling in the context), verification (making sure the contact path works), intent (knowing they might care right now), sequencing (the outreach itself), and CRM sync (so it isn't lost). Miss any one and the other five underperform.

When should a B2B sales team use it? When your ICP is definable in a sentence, your addressable market is big enough that you can't research accounts by hand, your average deal size justifies the cost per meeting, and inbound alone can't hit the number. When shouldn't you? I'll get to that.

How to configure Okki Go contact discovery in an AI agent

Four gates, in this order.

Gate 1: Waterfall the lookup

Don't let the agent lean on a single email lookup tool. Ask three, in order of cost, and stop at the first hit. The cheap provider runs first; the expensive one only fires when the first two miss. In our setup that cut per-contact data cost by roughly 60% versus running the premium source on everything.

One caveat: waterfall setup has diminishing returns. When we tested adding a fourth source, marginal coverage moved about 3%. That's one data point from one ICP — I don't have clean cross-industry numbers on it — so treat it as a reason to measure your own coverage, not as a benchmark.

Gate 2: Verification is a hard gate, not a score

Anything returning "unknown" or "catch-all" goes to a separate queue and does not enter the sequencer. This is the single change that most affected our deliverability.

I've watched teams export catch-all addresses straight into a campaign because the CSV looked full, then wonder why their sending domain started getting filtered. Verification isn't a nice-to-have field. It's the gate.

Gate 3: Intent before title

Why does intent come first? Because there are 400 VPs of Sales in a typical TAM and maybe a dozen of them are in market this month. Filtering on intent first means every downstream step — enrichment credits, verification calls, human review time — gets spent on the smaller, better pool.

Filter by title first and you burn data credits on 400 people to find the same 12.

Gate 4: Human-in-the-loop on the first touch

Agents write acceptable first lines and terrible first sentences. Let the agent draft, have a human approve the first 20 sends per sequence, then switch to sampling — read one in ten. We never removed the human entirely, and I don't think we should.

One more thing people forget: write the contact back to the CRM with a stable dedupe key (email hash or LinkedIn URN). Agents re-run. Without a key, you'll enrich the same person four times and pay for it four times. (Note to self: document the waterfall order inside the agent config, not in a Slack thread.)

The part that surprised me: fewer contacts, more meetings

We took a 6,000-contact list down to 2,400 and booked more meetings. The 3,600 we cut weren't neutral dead weight — they were actively harmful. Unverified addresses were dragging down inbox placement for the good ones, so the deliverable-looking list was suppressing its own best records.

The second surprise: the highest-signal field in our agent wasn't the email address. It was last_activity_date. Contacts whose company had touched our pricing page or opened a competitor comparison in the previous 14 days converted at a visibly different rate. We don't have a clean multiplier for it, but the pattern held consistently enough that we rebuilt the whole scoring model around it.

Volume doesn't fix a bad list. It just gets you filtered faster.

Sales engagement platform features worth paying for

Short list, from someone who's evaluated too many: per-inbox send caps and throttling, reply detection that actually stops the sequence, two-way CRM sync, a usable API and webhooks for the agent, cross-campaign suppression lists (mandatory if you're an agency with multiple clients), and separate sending domains per client or per brand.

What I'd deprioritize: 40-step cadences, a 200-template library, and any "AI personalization" you can't audit line by line. The features that decide whether your program survives are the unglamorous infrastructure ones.

Where this breaks

Boundaries, honestly stated.

• Small TAM. If you have fewer than a few hundred target accounts, manual research beats any agent. The setup cost doesn't amortize.
• PLG or bottom-up motions. If users adopt without talking to sales, contact discovery solves a problem you don't have.
• Regulated buying. Healthcare, government, and financial procurement often run through channels where cold email is the wrong door. Also — I'm not a lawyer, so I can't speak to your specific compliance posture. GDPR legitimate-interest assessments and CAN-SPAM's opt-out requirements both have details that depend on your jurisdiction, your data sources, and your industry. Consult counsel before you scale a cold outbound program, and verify current rules at the relevant regulator's site.
• Damaged domain reputation. If your sending domain is already burned, a better list makes it worse, not better. Fix reputation first.

And the biggest one: this doesn't replace SDRs. It removes research and list-building work so SDRs can spend time on conversations. Teams that use it as headcount replacement usually discover that the sequences get sent and nobody handles the replies.

One regret worth sharing. In 2023, on the agency setup, we pushed 400 contacts per day per client inbox because the client asked for volume and we said yes. Two of those sending domains got burned and we lost them. I still kick myself for not capping at 50 per inbox per day from day one. If we had, we'd still be running outbound from those domains today.

Even after we implemented the 50/day cap, I second-guessed it for a solid month. What if we were leaving pipeline on the table? The numbers settled it eventually, but the doubt was real, and it was annoying.

So: waterfall the lookup, gate on verification, filter on intent, keep a human on the first touch. That's the config. The rest is discipline.