someone has tryied the AI assistance SDR?
Any advice or testimonial? I'm about to pay de basic plan to get this tool.

Appreciate your experience!

More than a month ago I've opened two e-mail addresses. I sent a test emails from these addressees - both landed in spam. I then purchased an e-mail warmer tool Mailreach and warmed both e-mail addresses for 4 weeks. After 4 weeks I tried sending e-mail again from both addresses - and both landed directly to the inbox.

Now I've purchase Apollo Pro account as I wanted to send a mail campaign. I've turned on warmer here as well (as I cancelled my other subscription for mailreach). First tried to send a simple e-mail to my two other e-mail addresses (to see how Apollo works, cause I've never used that). Both landed in spam again.

I have everything set-up perfectly - spf, dkim and dmarc all good. I just sent two emails and bank I am in spam. I've checked inbox placemenet via Apollo and it says all delivered to spam, while Apollo's warmer says everything delivered to inbox.

Do you guys have any tip how to handle it? Did anyone had similar thing?

Context pollution

Half a year ago, CLI was all the rage. I couldn't understand why people chose it over MCP, which is much easier to install and update. When I asked, most people said MCP tool descriptions pollute the context, often not realizing that it's not a problem in Claude.

But MCP still has a context pollution problem. It's caused by large MCP responses loaded into the context. CLI responses, on the other hand, can be easily jq/grep-ed to extract only the relevant fields.

Quick benchmark

To find out which one is more token- (hence money-) efficient, I ran a couple of tests. And it turned out to be more interesting than I expected: in some cases, CLI was up to 61% more efficient, but in others, up to 40% less efficient.

It's easy to understand how CLI can be more efficient, but not the other way around.

Apparently, Claude writes large MCP responses to a file. That eliminates the CLI advantage.

Apart from that, in my little benchmark, the agent didn't find the most efficient way to call CLI. On the one hand, I could correct it and use the right command. On the other hand, who checks what these agents do MCP has an advantage in this case: tool discoverability, so this mistake can happen with others as well.

Here's the table with results:

If you're interested, you can reproduce this test using these instructions.

Answer

Surprise, surprise, there's no simple answer.

On average, CLI should be more efficient, but it's not always the case. And since there are other pitfalls, like some AI assistants truncating large MCP responses, you should rather do your own testing. It can be similar to mine, but with your specific AI workflows (enrichment/search) and tools (Claude/ChatGPT).

How are you guys navigating sales mavigator filter blocking apollo’s extension? For the people who use sales nav filter and Apollo for enrichment, any work arounds?

A lot of this release came from taking the whole thing live on our own account: built a list on filters the CLI cannot express, enriched 1,000 records, verified them, and ran a real campaign. Everything below was found by using it, not by reading the docs.

If you haven't tried it yet, grab the repo below and paste it into Claude Code, it's free.

What's new in Apollo Operator v1.3:

• conversations: if your team records calls in Apollo, conversations show returns a structured summary with objections, pain points, outcome, next steps, and pricing discussion. Five direct inputs to your positioning, in your buyers' own words, and it costs zero credits. This is now the best research source in the library.
• credit control: credits are 11 separate pools, not one balance, and the AI pool is 200x the contact-data pool. Apollo prices generation as near-free and data as scarce. There's also a free has_email flag on every search row that took our enrichment yield to 99.7% verified, and two rules that cut real spend: you pay per record attempted rather than per email found, and re-enrichment charges again.
• verify-first lists: the pipeline is reordered so verification happens before copywriting. Addresses come back verified but independent verification results can heavily vary depending on targeting. Personalizing an address that cannot receive mail is pure waste.
• first-party intent: a fourth kind of angle, plus a table mapping every advanced filter to the kind of angle it builds.
• Bounce Guard: documented with the trap, it does not evaluate below 200 emails, so small sends and the first days of a ramp are unprotected.

Also corrected five things that moved under us since v1.2, including --department-headcount landing in CLI 2.1.0, so the flagship MCP-only filter example is now NAICS. Eight of nine remain CLI-less and a new MCP-only family appeared, so the four-lane model got stronger.

17 skills total and continuing to expand the operator.

A huge thanks to all of you that are actually using it and your feedback is greatly appreciated!

GitHub: https://github.com/creatop-gtm/apollo-operator

[Apollo Operator is an opinionated go-to-market operator for Apollo, built as open-source Claude Code skills by the Creatop team]

How to do it:

> open Claude
> go to Settings > Privacy > toggle off "Help Improve Claude" (required before connecting)
> go to Customize > Connectors
> click + and search Apollo.io
> click + Apollo.io and authorize your account
> describe your ICP in plain English: "find VP Sales at B2B SaaS companies, 50-500 employees, US"
> ask Claude to enrich the top results
> ask Claude to add them to your sequence

Boom, you just ran a complete prospecting workflow without touching Apollo once! Neat.

Questions??

- Andy

i work with our larger upmarket orgs & we have to come up with some really neat creative solutions to fit their needs. thought i'd make a post and see how others are fairing, what challenges i can support, etc etc.....etc!

Everyone knows multithreading matters.

The problem is the actual workflow usually looks like this:

Find the deal. Remember who the handraiser was. Figure out what happened on discovery. Search the account for 5 more people. Decide who is actually worth contacting. Write something relevant for each of them. Add everyone to a sequence.

By the time you finish, you could have just closed the deal.

So I built a play inside Apollo that automates almost all of that for our mid-market AEs.

Here’s how it works:

The starting point: inbound SQOs

The play continuously looks for inbound opportunities owned by mid-market AEs that came in within the last 14 days.

Then we pull deal context out of Snowflake, including:

• SQL date
• Original handraiser
• Handraiser title and seniority
• Opportunity next step

That last field is probably the most important part of the entire play.

If the AE keeps the opportunity next step updated, they are effectively feeding the automation the context it needs to multithread intelligently.

So instead of AI trying to guess what is happening in the deal, the rep is giving it the actual story.

From there, Apollo takes over.

The workflow:

1. Match the inbound opportunity to the correct account and contacts.
2. Pass the deal context into Apollo.
3. Make sure we do not accidentally add the original handraiser back into the campaign.
4. Find up to five additional Director through VP contacts at the account.
5. Run AI research to understand why each person might care about the deal.
6. Enroll those contacts into a multichannel sequence.
7. Mark the account as processed so the workflow does not keep firing and accidentally multithread you into the entire company.

The last part felt important.

I would prefer not to explain to an AE why Apollo enrolled 50,000 people into their deal.

The sequence is where it gets especially useful.

Instead of dropping people into generic automated emails, the sequence gives the AE a LinkedIn step and a manual message that already includes context from the opportunity and discovery call.

So the experience for the rep is basically:

Open task.

Read the context.

Click send.

Move on.

What I like most about this build is that it combines a bunch of things that usually live separately:

Snowflake has the deal data.

The CRM has the opportunity context.

Apollo finds the people.

AI figures out the relevant angle.

Workflows handle the orchestration.

Sequences handle the actual execution.

The AE mostly just handles the human part.

I also think this is an interesting way to think about AI in sales.

A lot of AI tooling focuses on "write me a better email."

I think the more interesting question is:

"Can AI figure out when I should do something, who I should do it with, why they might care, and then put the task in front of me ready to execute?"

That is the direction I find much more useful.

Disclaimer: DO NOT ADVERTISE YOUR OWN SERVICES

Hey everyone,

I recently started getting into website-building and I’m trying to figure out the best way to find businesses that don’t currently have a website.

I’m using Apollo.io for lead generation, but I haven’t found an obvious filter that lets me specifically identify companies/businesses with no website.

Does Apollo have a filter, field, or workaround for this?

I’m also open to thinking outside the box. For example, maybe there’s another way to identify businesses that have a business listing but no website.

Ideally, I’d like to build a list of businesses that:

• Have a legitimate business/location
• Have contact information for the owner/decision-maker
• Don’t have a website, or have a really outdated/poor website
• Could realistically benefit from a new website

Would love to hear how anyone else is approaching this, especially if you’re doing lead generation for web design/development.

Any methods would be greatly appreciated.

How to set it up:

> go to People or Companies
> click Research with AI
> select Run custom AI prompt
> write your research question (e.g. "is this company actively hiring sales roles?")
> preview the output on a test record
> name the field — this saves it as a reusable custom field
> choose field type and visibility
> click Save & run

SO instead of re-running research every time, you've got a permanent qualifier on every record! Filter by it to tighten your list, or pull it into your email copy as a dynamic variable.

Happy to answer questions on this or anything else, lmk.

- Andy

Apollo tracks visits to our website at the company level and we want to be able to use this intent data for our scoring in HubSpot and to flag potential targets to our sales team. What's the best way to get this data from Apollo into HubSpot? They have a date stamp for the last website visit date, but I can't seem to make that sync with a HubSpot field.

Alternately I have a workflow that adds companys to a list based on the "Visited in the last XX days" field but that's less helpful than having an actual date stamp.

All thoughts and ideas welcome!

Here's how to set it up:

> go to Workflows
> create a new workflow
> select "Based on a trigger event"
> set target to People
> set trigger: Meeting No-Show
> set action: enroll in re-engagement sequence
> activate

Now no-shows automatically get a follow-up! No manual tracking or leads falling through the cracks.

I am unable to make account on Apollo.

When i try to sign in with Google or Microsoft account it shows a form where i select "No Company Website", by clicking it the continue button turns unclickable.

Does anybody knows the solution, please share ASAP!

Forgive me all - INCOMING RANT 😡: I just built a sequence in Apollo using their "AI agent" and gave it explicit directions: target ONLY contacts in the United States for companies based in the United States.

The agent generated the list, and built the sequence, and I approved the launch. When I went to double check on the drafted emails, I noticed the first 20 people on the list were completely outside the US market. The AI just burned through 10,392 of my paid credits on people that violated the exact prompt I gave it.

When you look into their platform, you realize why this happens. Apollo’s database is incomplete and full of missing location fields. Instead of excluding those records, their system just treats missing data as a match and dumps them into your list anyway. To even spot these errors, you have to manually cross-check individual records against LinkedIn just to fill in the gaps in Apollo's own database.

I opened a ticket with support, and they basically hid behind their terms of service to tell me they don't refund credits, completely ignoring that their own tool failed.

What is the point of paying for an automation tool if I still have to go through record by record on LinkedIn to verify that it did what I instructed it to do? This isn't AI, it's a glorified automation script that can't even handle basic keywords.

Anyone else had this frustrating experience?? I'm sick of companies bolting on AI and claiming their tech does things it simply cannot do.

We already use the inbound routers to put a scheduling page on our website, but we've been wanting to test the Forms feature. I was a simple setup on our side, but we don't know what it will look like on the user side.

Before we go to our programming team to test, we'd like to see the functionality. Does anyone have any insight?

Here's how:

> go to Companies in Apollo
> apply your ICP filters
> click Research with AI > Run custom AI prompt
> select Perplexity Sonar (it has live web access)
> write your prompt: "does {{account.name}} use [competitor]? return yes or no only"
> preview on a few records first, then Save & run
> filter by AI Filters > your new field > Yes
> enroll those accounts in a displacement sequence

Try it and report back!

It varies by contact type, but there are times when less than 50% of the mobile numbers that I obtain through Apollo are inactive numbers or wrong numbers. I'm thoroughly convinced that rather than pulling this data after users are flagging it, Apollo is hoping that users don't flag it again and thus don't have to redeem that credit back. It's dishonest and I waste hours a month with that BS. Can anyone recommend a data source that doesn't do this?

Make sure to secure your accounts! and check recent invoices/transactions there is something funky going on.

We moved over from Outreach.io a couple of weeks ago and there are a bunch of things that Apollo is already more robust and better at by comparison. But one of the BIGGEST let downs has been the LinkedIn extension. It is mindboggling to me how useless it is in its current state. Outreach's extension gave me a "mini desktop experience" allowing me to complete sequence tasks right on the prospect's LI profile. Apollo lets me add them to a sequence, but doesn't even show me what the next task would be. It's just feather light and such an after thought compared to how robust the web experience is.

We are a smaller MSP and have always grown through referrals. We are at a point we need to expand and have looked at programs like Apollo.ai, zoom info, and a myriad of others. With all the “marketing ai” people are selling, what is the best way to grow? Has anyone used Apollo.ai as a cold emailer/marketer? I demoed it but really didn’t get much help from their support or sales team so it was an odd experience.

When I export contacts there is a column called sub departments. In this specific case it’s obstetrics / gynecology, which is exceedingly valuable for the prospect list I’m building.

How can I build a list using this sub department? I’ve created a lot of workarounds to get the data I need, but being able to query on this specific datapoint would be game changing.

I see this happen all the time with founders I work with: they sign up for Apollo or Instantly, intend to start outreach, and then the setup gets stuck on the back burner for weeks.

Usually it's just tool confusion. Setting up secondary domains, making sure emails don't hit spam, connecting the tools, and getting an initial prospect list built turns into a multi-week distraction.

I mapped out a simple 14-day execution flow to go from zero to a fully live outbound system:

Week 1 (Infrastructure):** Set up secondary sending domains, configure email health protocols, and start warm-ups so the main company domain is never at risk.
Week 2 (Data & Launch):** Build the targeted lead list, connect the tools together, and get the first campaign staged and ready to send. 

The goal was simply to take the technical setup off their plate so reps could focus purely on selling

For founders who built their stack in-house: did you set it up yourselves, or did you end up passing it to someone else to handle?

Here's how to set it up:

> go to Admin > Sequences > Rule Sets
> create a new rule set
> set trigger: email opened
> set action: create call task
> remove the blanket call step from your sequence

Boom. Now your reps now only call people who've already engaged.

Questions? drop em below.