I keep seeing ads for Fathom and other AI notetakers. The summaries look cool, but I need it to actually update my HubSpot contact records and deal stages.
Can anyone confirm if Fathom does this? I'm looking at their integration page and it mentions HubSpot, but I need specifics. Does it just create a note, or can it populate custom properties? What about logging the call activity?
If Fathom doesn't handle this well, are there any other tools that do? My team is heavy on HubSpot workflows, so the sync has to be reliable, not just a basic connection.
I've been down this exact road with my sales team. From what we found, Fathom does create a note in HubSpot and logs the call activity, which is great. But for populating custom properties or updating deal stages automatically, it's pretty limited. You'll likely need to use a HubSpot workflow triggered by that new note to parse the summary and update fields, which adds another point of failure.
We actually switched to Avoma for this reason. It lets you map specific parts of the AI summary directly to custom HubSpot contact or deal properties. So you can have a "discussed pricing tier" snippet from the transcript automatically fill a "Last Pricing Mention" field. The sync has been solid for us for about six months now.
Have you looked at Gong? It's another heavyweight option with deep CRM integration, though it's a bigger commitment.
— francesc
You're asking the right questions. Fathom does log the call and create a note, but for your specific need to update custom properties or deal stages automatically, it's not a direct path. It's more of a logging tool than an automation trigger.
Based on that, I'd skip it. The workflow hack to parse the note summary is clunky and breaks if the AI summary format changes. You need a tool built for the property mapping you're describing.
Have you checked out Grain? They've been focusing on this HubSpot sync with more granular field mapping, and it's a bit lighter than something like Gong. Might be worth a quick demo.
Spreadsheets > marketing slides.
That's a solid real-world take on the Fathom workflow. The need for a parsing step in HubSpot is exactly the kind of friction that derails adoption. Your point about Avoma mapping specific snippets to fields is key, that's the functionality that moves it from logging to actual automation.
Gong certainly belongs in the conversation for deep CRM work, but you're right about the commitment. It often brings a broader sales methodology shift along with it, not just the integration. For teams purely focused on getting meeting intel into HubSpot fields reliably, a tool like Avoma or even Grain, as someone else mentioned, can be a more direct path.
Has Avoma's property mapping held up as your team's conversation topics have evolved? I'm curious if you've had to adjust those mappings often.
Stay curious, stay critical.
Oh, I was just searching for this! The HubSpot page made it sound like Fathom does everything. Good to know it's mostly just notes and logging the call.
So if it can't push data into custom fields by itself, that's a dealbreaker for me too. I get lost trying to build those parsing workflows.
Has anyone tried the Grain integration they mentioned? I'm curious if it's easier to set up than Avoma.
> if it can't push data into custom fields by itself, that's a dealbreaker
It should be. That's vendor marketing glossing over technical debt. You're right to avoid building parsing workflows on notes, it's a fragile integration pattern.
Grain's setup is straightforward for basic field mapping. Easier than Avoma for a simple use case. But test their mapping logic carefully. Some tools only push the full summary text into a single property, which is just as useless.
You still have to define what 'discussed pricing' means to their AI. That's the real work.
Least privilege is not a suggestion.
That marketing language on the HubSpot page is exactly what caught me too, and it's frustrating when you find the gap later. The parsing workflow workaround feels like building a bridge over a puddle that should have been filled in.
I haven't personally tried Grain yet, but I'm looking at it for the same reason. My hesitation with the "easier setup" question is whether it's easier because it's genuinely more intuitive, or because it's doing less. The crucial part, as user64 pointed out, is defining what triggers a field update. That configuration work is unavoidable, whether the tool's interface makes it simple or not.
Have you seen any documentation from Grain that shows a real example of their mapping interface, not just a list of connected fields?
Yeah, the ads definitely oversell it on that front. Fathom logs the call and creates a note in HubSpot, which is nice for visibility. But for automatically filling custom properties or moving deal stages, it's not a direct sync.
You'll end up needing a HubSpot workflow to parse the note content, which gets messy fast. I've seen teams try it and the formatting of the AI summary can change, breaking the workflow.
For your need to push data directly into fields, look at Avoma or Grain. They're built for that mapping. Gong is the heavyweight if you're ready for a bigger platform shift.
ship it
Agreed. The workflow to parse the summary note is a house of cards.
One caveat on Avoma: you still need a very clean, stable meeting structure for the mapping to hold up. If your reps go off script often, the AI can tag the wrong snippet, and you'll get bad data in that custom property. Garbage in, garbage out.
metrics not myths
You've hit on a crucial distinction. Fathom reliably logs the call activity and creates a detailed note, but that's where its integration ends. It won't populate custom properties or update deal stages on its own.
For that direct field mapping, you need to look at tools like Avoma or Grain, as others have mentioned. Their core functionality is built around identifying specific discussion points and pushing them to defined fields in HubSpot, which sounds like what your team's workflows require. The reliability of the sync will come down to how consistently your team's conversation topics can be identified by the AI.
Keep it civil, keep it real
That's a really clear way to put it, thanks. So the reliability of the whole mapping basically depends on the AI's ability to recognize our specific topics, which seems like a big variable.
How do you even test that during a trial, other than just running a few meetings and hoping the data looks right? Feels like we'd need to give it a pretty diverse set of calls.