There is no single answer to what permissions Okki-go requires or whether LinkedIn automation scraping is a good idea. The right answer depends on whether you are in a 72-hour pipeline sprint, building a durable B2B contact database, or using intent data to prioritize accounts. I run RevOps at a B2B SaaS company. I have handled 40+ urgent prospecting sprints in six years, including same-week list builds for outbound agencies. The mistakes are usually the same: teams ask about price per contact first and completely miss permission scope, data provenance, and whether the intent signal is actionable.
So instead of a generic checklist, here are three scenarios. Find the one that matches your deadline, then apply the specific checks.
Scenario A: You need contacts in 72 hours or less
This is the emergency room. You have a webinar, a conference, or a campaign launch, and the list is not ready. In March 2024, a client called on a Tuesday morning needing 1,200 contacts for a Thursday webinar. Normal turnaround for a clean, verified list was about five business days. We had 36 hours.
The temptation is to scrape LinkedIn or connect a tool that automates profile visits and connection requests. I get why. It feels fast. But according to LinkedIn's User Agreement (linkedin.com/legal/user-agreement), scraping or automated activity is prohibited without permission. The risk is not a small fine. It is your team's accounts getting restricted right before the campaign.
For Okki-go or any agent-native prospecting tool, the permission question becomes urgent. Expect requests for LinkedIn account access, email sending permissions, CRM read or write, and possibly browser extension access. The minimum viable set should be narrower than the maximum. If a tool asks for full mailbox access when it only needs send-as, that is a red flag (not that the 'unlimited' plan was actually unlimited).
What actually worked in that March sprint: we used a licensed data provider with a clear source map, paid $400 extra for a rush verification pass, and kept a human-in-the-loop review on the first 100 records. We missed a few fringe titles. We did not miss the deadline. To be fair, cheaper tools can work for low-stakes list building. But for a webinar 36 hours out, I am not betting on probably.
Scenario B: You are building a B2B contact database for the long haul
Here the game is different. You are not buying speed. You are buying data source transparency and hygiene. The question everyone asks is how many contacts do you have. The question they should ask is where did each field come from, and when was it last verified.
Ask any vendor, including Okki-go, for a data source map. Which fields come from first-party enrichment? Which come from licensed providers? Which come from public web data? Which come from user uploads? Okki-go's stated angle is waterfall enrichment plus intent, so the right question is not do you have a database. It is which provider solved each field, in what order, and what happens when the first provider fails.
We did not have a formal permission review process. Cost us when an unauthorized enrichment tool synced about 8,000 contacts. Maybe 6,000, I would have to check. Some were duplicates. Some had stale titles. One had a personal email that should not have been in the CRM. That was before we had a formal permission review. Oh, and we now require admin consent for every enrichment tool that touches the CRM.
Per FTC guidelines (ftc.gov), if a vendor claims verified or accurate contact data, that claim needs to be truthful and substantiated. So ask for the substantiation. Under GDPR (Regulation 2016/679), B2B prospecting still needs a lawful basis, and people have rights to object, access, and erasure. CCPA and CPRA add similar rights for California residents.
For a durable database, prioritize tools that show source labels, opt-out status, retention controls, and suppression lists. Waterfall enrichment is useful because it improves coverage, but only if you can see which source won and why. Human-in-the-loop outreach is not a weakness. It is how you catch bad data before it damages your domain reputation.
Scenario C: You want intent data features and need to know when to use them
Intent data is not a magic list. It is a prioritization layer. Features usually include topic or keyword signals, surge scores, recency, source type, account match, contact match, and decay. Decay matters. A company that researched your category three months ago is not the same as one that did it yesterday.
Use intent data when you have a clear ICP, at least a few hundred target accounts, and sales capacity to act within 7 to 14 days. It works best when marketing and sales agree on what signal matters. For example, if a target account starts engaging with content about sales automation and also visits your pricing page, that is a stronger signal than either alone.
Do not use intent data as a replacement for ICP fit. If the account is a bad fit, intent just helps you annoy them faster. Also, do not buy intent data if your team cannot follow up quickly. A signal without action is just a dashboard.
I have seen teams buy intent data before they had a validated outbound message. They blamed the data when the reply rate was low. The problem was the message, not the signal. Granted, intent data requires more upfront work. But it saves time later by focusing effort on accounts that are actually in market.
How to tell which scenario you are in
Use this quick filter:
- If your deadline is under 72 hours and the list is for a specific event, you are in Scenario A. Optimize for certainty, not the lowest price. Pay for verified sources and keep a human review step.
- If your timeline is 30 to 90 days and you need repeatable pipeline, you are in Scenario B. Optimize for data source transparency, permission scope, and CRM hygiene.
- If you already have a database and need better prioritization, you are in Scenario C. Optimize for intent signal quality, recency, and your team's follow-up capacity.
If you are in more than one scenario, sequence them. Fix permissions and data sources first. Then add intent. Then use urgent sprints only when the pipeline math justifies the premium.
The common thread is time certainty. In an emergency, certainty is worth paying for. In a database build, certainty about data provenance is worth paying for. In intent data, certainty about timing is worth paying for. The cheapest option is rarely the one that protects your deadline, your domain, or your CRM.
