Okki-Go Permissions and Company Data APIs: What RevOps Teams Actually Need to Ask Before They Sign

Stop Evaluating Company Data APIs Like You're Buying a Database

I'll say the unpopular thing first: most RevOps teams evaluate company data APIs by comparing list size and price-per-record, and that's why so many of us end up with a tool that looks great in a demo and falls apart inside the workflow.

I learned this the expensive way. In Q1 2022, I sat through four vendor demos for enrichment and data APIs. Made a weighted scoring sheet — database size, match rate claims, cost per credit, number of integrations. Felt very analytical. Felt very mature. Eight months later, we were ripping one of them out because nobody, including me, had asked what permissions it needed during onboarding, and it ended up with write access to fields my team didn't want it touching. We spent roughly $1,400 on the contract and another three weeks of cleanup re-sequencing fields that got silently overwritten.

So when people ask me "what permissions does Okki-Go require?" or "should we go with Okki-Go vs Hunter?" — I get why they're asking. But that's usually the second question. The first one should be: what happens after the API is connected, and who's responsible when the data decides to do something you didn't plan for?

What Permissions Does Okki-Go Require — And Why That Question Should Come Earlier

Here's the thing about AI SDR platforms and prospecting APIs in general: permissions aren't just a security checklist item. They're a leading indicator of how the tool is designed to interact with your stack.

For tools like Okki-Go, the permission surface typically includes some combination of:

• CRM read/write access (HubSpot, Salesforce, Pipedrive, etc.)
• LinkedIn account authorization for outreach sequencing
• Email sending permissions (via connected mailbox or sending domain)
• Company data API access for enrichment, intent signals, or waterfall lookups

Notice what that list actually implies. If a tool asks for CRM write access, it can modify records. If it asks for LinkedIn authorization, it can send messages from your reps' accounts. If it wants email sending rights, it's operating inside deliverability infrastructure you can't fully revoke after the fact.

From the outside, it looks like permissions are an IT concern. The reality is they're the most concentrated form of risk in the entire procurement.

I once approved LinkedIn outreach tooling that had permission to act on behalf of five SDR accounts. Didn't think much about it — the SDRs had opted in, the tool had a decent reputation, what could go wrong? Three weeks later one rep's account got temporarily restricted because our sequence configuration was tripping LinkedIn's activity thresholds. Not the tool's fault, exactly (circa 2023, before most vendors had better pacing controls). But nobody on my team had actually read what the permission scope meant in practice. We just clicked 'Approve.'

The lesson wasn't "don't use LinkedIn automation." It was: if you can't explain in one sentence what each permission grants, you're not ready to sign.

Okki-Go vs Hunter: The Wrong Axis of Comparison

People ask me about Okki-Go vs Hunter a lot. And I get it — both come up in the same procurement conversations, both touch data sourcing and outreach.

But here's my honest read after going through this cycle: comparing them on "which has better data" misses the more important question of what you're actually trying to build.

Hunter is primarily a domain search and email verification tool. Solid for what it does. If you need to find emails for a specific list of domains or verify a CSV, it's a clean, focused product.

Okki-Go leans more into agent-native prospecting — the idea being that the workflow (find prospects, enrich them, sequence outreach, coordinate across channels) runs as a coherent loop rather than as five disconnected tools held together with Zapier and duct tape. Waterfall enrichment plus intent data plus human-in-the-loop outreach.

Those aren't competing feature sets. They're competing philosophies. One says "give me a precise tool, I'll build the workflow." The other says "give me the workflow, I'll manage the pieces."

To be fair, both approaches have a legitimate place. Smaller teams with strong ops discipline often do fine assembling their own stack. Teams without a dedicated ops person tend to get more value from the integrated approach, simply because there's nobody whose full-time job is keeping the pipes connected.

The trap is evaluating Okki-Go vs Hunter on "accuracy" or "coverage" when the real decision is about what shape of workflow you can actually maintain.

What RevOps Teams Should Evaluate in a Company Data API (But Usually Don't)

If I were rebuilding my 2022 evaluation checklist from scratch, these are the questions I'd lead with. Not database size. Not price per credit.

1. Permission Scope and Revocation Model

Ask each vendor: what exactly will this API be able to read and write? Can I scope it down? If we cancel, do you revoke all tokens, and how do I verify that? Get it in writing before the contract, not in a support ticket after.

I knew I should get written confirmation of data deletion terms before signing, but thought "what are the odds we actually need that clause?" Well, the odds caught up with me when we churned one tool and had to chase down whether our enrichment data was fully purged. Took six emails and a security review to get an answer.

2. Enrichment Behavior Under Failure

What happens when the API can't find a match? Does it return a null? A guess? A partial record you'll later mistake for a verified one? This matters more than match rate percentages because match rate is a marketing number and enrichment behavior under failure is an operational reality.

Most waterfall enrichment systems — including the ones built into modern AI SDR platforms — will cascade through multiple sources when the first one misses. That's a good design pattern. But you need to know what the output looks like when all sources miss. If it returns an empty but non-null field, downstream automations can break silently.

3. Intent Signal Provenance

"Intent data" is one of those phrases that means twelve different things depending on who's selling it. Some vendors pull from content syndication networks. Some from bidding behavior. Some from job postings, tech stack changes, or LinkedIn engagement signals. Ask where the signal comes from, how fresh it is, and how quickly it decays. If a vendor can't answer those three questions clearly, treat the signal as directional at best.

4. LinkedIn Outreach Compliance Posture

LinkedIn outreach integrations are where I've seen the most misunderstanding. People assume that because a tool has an official-looking integration, the outreach pattern is automatically safe. That's not how it works. The vendor provides the plumbing; your sequence design determines the risk. Ask: does the platform enforce pacing limits? Can it detect when a rep's account is approaching activity thresholds? Does it degrade gracefully when LinkedIn changes something?

We didn't have a formal review process for LinkedIn sequence cadence until after the account restriction incident. Cost us two weeks of one SDR's outreach volume. Now every sequence gets reviewed against a short checklist before it goes live.

5. Human-in-the-Loop Controls

This is where agent-native platforms differentiate themselves from pure automation. Can a human review outbound messages before they send? Can they override enrichment decisions? Can they flag a bad record without filing a support ticket? The tools that treat humans as supervisors rather than just approvers age much better inside real teams.

"But Isn't This Overthinking It?"

Someone's going to say: this is a lot of process for what's ultimately a data subscription. Just measure reply rates and cancel what doesn't work.

I get that view. In early-stage companies with two-person sales teams, a lightweight approach makes sense. You can absorb mistakes because the volume is low and the stakes are contained.

But past a certain team size, the calculus changes. When you've got 8 SDRs, three sequences running in parallel, and a CRM that three departments depend on, a silent permissions issue or a broken enrichment flow isn't a small thing. It's a multi-week cleanup project. Five minutes of verification beats five days of correction. Every time.

Granted, this requires more upfront work than a typical SaaS evaluation. It slows down procurement. And I'll admit I don't have a clean formula for how much process is right at every company size — that's a judgment call, and reasonable people will draw the line differently.

The Checklist I Actually Use Now

Here it is, roughly as our team uses it. No scoring weights, no fancy spreadsheet. Just questions that have to be answerable before we move forward.

• What permissions does this tool need, in plain language?
• What's the smallest permission scope that still makes the tool work?
• What happens when enrichment fails silently?
• Where do intent signals come from, and how fast do they decay?
• What guardrails exist for LinkedIn or email outreach cadence?
• What does the tool do when a human and the automation disagree?
• What does cancellation actually look like, including data revocation?

Seven questions. Took me three years and roughly $2,000 in avoidable rework to arrive at them. You can have them for free.

My view, plain: the company data API you pick matters less than the questions you ask before you pick it. Get the questions right first, and Okki-Go vs Hunter stops being a coin flip and starts being a decision you can defend six months later.