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Katalyst is the AI sales agent for teams on Salesforce. Hang up a call and it's already done: notes summarized, records created, fields updated, follow-up drafted, next step set. It runs 24/7, reading every call, email, and calendar, surfacing the right signals, prepping you for each meeting, and prompting you to act: follow up here, this one’s slipping. What's new: AI Resolution on every account, meeting recorder, hygiene scores, deal patterns. Built for enterprise sales teams.
Does the AI explain why it's recommending a next step?
Katalyst
@tanishq_arya Yes, recommendations come with the reasoning behind them, not just the suggestion. Katalyst grounds each next step in the actual signals and activity it's seeing, the call, the email, the account change, so you can see why it's surfacing something before you accept it. That's the whole point of keeping a human in the loop: you get context, not a black box.
Katalyst
@tanishq_arya The reasoning isn't just reassurance, seeing why it surfaced a step sharpens the rep's own instincts over time. Less a black box giving orders, more a second set of eyes that shows its work.
Is my company's data being used to train your AI models? That's a dealbreaker for a lot of enterprise companies.
Katalyst
@aliyazhanabayy Fair, and it's the right question to ask. This matters a lot to enterprise teams and we treat it that way, happy to walk you through exactly how your data is handled and isolated. What's the best way to get you those specifics?
Katalyst
@aliyazhanabayy To add, this is exactly the kind of thing that deserves a precise answer rather than a reassuring one liner, so worth walking through directly. It's a dealbreaker question for good reason, and we'd rather show you exactly how your data's handled than ask you to take it on faith.
I already use Fireflies for transcription. What's the advantage of switching to Katalyst's recorder?
Katalyst
@ayush_bansal17 Fireflies records and summarizes, then you act on it. Katalyst's recorder does the acting, updates Salesforce, sets next steps, drafts follow-ups straight off the call. The difference is a notetaker vs. the work actually getting done.
Katalyst
@ayush_bansal17 To add, it's less about switching transcription and more about what happens after, Fireflies gives you the notes, Katalyst turns them into updated records, next steps, and drafted follow ups automatically. Worth noting we also ingest Fireflies as a source, so you're not forced to rip it out to get the acting-on-the-call part.
Portal
Does this get better over time as it learns more about how my calls are going/flow or does it stay static?
Katalyst
@zach_gold It improves. Each rep's agent runs on their own calls and emails, so it sharpens to how your deals actually flow rather than staying a fixed model. The more it sees, the better it fits you.
Portal
@divyansh_lohia Makes a ton of sense!
Katalyst
@zach_gold To add, it's the opposite of static, each rep's agent builds on your own calls and emails, so it keeps adapting to how your deals actually flow. The context compounds over time, so the fit gets tighter the more it sees rather than plateauing on a generic model.
Portal
@avneet_singh_sidhu Can definitely see why this would be better than having to manage lots of MD docs to feed into my agent.
Katalyst
@zach_gold Exactly, that's the difference, no folder of MD docs to maintain or feed in. It builds that context automatically from the calls and emails already happening, so the agent stays current without you curating anything.
Interesting product. Is it able to work differently for SMB versus enterprise deals?
Katalyst
@rashmi_sinha8 It adapts to the deal rather than running one playbook. A fast SMB cycle and a long enterprise deal have different signals and rhythms, and since it learns from your actual pipeline, it shapes to whichever you're running.
Katalyst
@rashmi_sinha8 To add, this falls out naturally from it learning on your own pipeline rather than a fixed template. A quick SMB cycle and a multi month enterprise deal carry different signals and pace, so it shapes to whichever you're running instead of forcing both through one playbook.
Do you need to be technical to use this? Like does someone need to configure a bunch of stuff?
Katalyst
@aadya_saha No, that was a priority. You connect Salesforce and it reads your existing setup as-is, no scripting or heavy config. Larger orgs sometimes loop in an admin for permissions, but a rep can get going without technical setup.
Katalyst
@aadya_saha To add, this was a real design goal, not an afterthought. It reads your existing Salesforce setup as is, so there's nothing to script or rebuild, you connect and start. The whole point is removing admin work, so making reps do admin work just to set it up would defeat it.
What has been the hardest engineering challenge you've solved while building Katalyst?
Katalyst
@sanjana_dugar_cfa_msf_ Getting extraction accurate enough to trust with auto-writes. Summarizing a call is easy. Changing a close date in someone's CRM off that call is not. Every update had to be grounded in a specific source, with confidence thresholds / human review so a wrong inference never silently lands. The bar for "write to a production CRM" is a lot higher than "generate a draft."
Katalyst
@sanjana_dugar_cfa_msf_ adding my pick: the matching problem. Before the AI can update anything, it has to answer a deceptively hard question, which deal does this email or call actually belong to? Conversations arrive from everywhere, Gmail, Outlook, meeting notetakers, Notion, with no deal ID attached. Threads change subject lines, new people join mid-deal, one account can have three open opportunities. Get that link wrong and every downstream step is confidently wrong about the wrong deal. We ended up building a whole resolution layer for it, and when it isn't sure, it holds back instead of guessing. Unglamorous compared to the AI parts, but it's what everything else stands on.