Launched this week
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.
How hard is this to set up? I know enterprise software can take forever to implement.
Katalyst
@deepanshu_bansal4 Fair concern, most Salesforce tooling is a months long implementation slog. Katalyst is the opposite: it's native to Salesforce with no middleware, so you connect your org, pick the fields you want synced, and you're live in minutes. The AI starts working your pipeline from day one. No rip and replace, no admin project.
Katalyst
@deepanshu_bansal4 Adding to Avneet, fast setup doesn't mean it's writing freely on day one, it starts in approve mode, suggesting immediately while you stay in control, and you widen its autonomy as trust builds. Quick to start, careful by default.
What's the "Zero Board" I saw it mentioned? Sounds interesting.
Katalyst
@mayank_singla4 The Zero Board is the pipeline hygiene dashboard. It shows every deal that's missing critical information: empty fields, stale next steps, overdue actions. The goal is to get the board to zero. The cool part is Katalyst doesn't just show you what's broken, it actively suggests fixes and the agent can handle many of them automatically. It turns pipeline hygiene from a painful weekly audit into something that practically maintains itself.
Katalyst
@mayank_singla4 managers tend to love the Zero Board most, it replaces the Monday round of chasing reps for updates with one screen, worst deals first. The weekly pipeline audit basically runs itself.
How does it interpret pipeline on day 90 vs real time?
Katalyst
@mallika_chawla2 Good question, want to make sure I answer the right thing, are you asking how it reads a long-cycle deal as it matures, or how it treats existing pipeline history vs. changes it tracks live?
Katalyst
@mallika_chawla2 both readings have good answers, so here's each in short. If you mean a deal that's 90 days into its cycle: nothing is read as a snapshot. Every call and email along the way has been folded into that deal's memory, so on day 90 the AI is interpreting the latest conversation with three months of context behind it, and it's deliberately careful with age, older facts still count but stale urgency gets dialed down rather than repeated. If you mean what happens when you first connect an existing pipeline: Katalyst syncs your Salesforce as it stands and pulls in the email history around each deal, so it doesn't start blind on deals that are already in motion, and from there it tracks everything live as it happens.
Could this work for customer success teams managing renewals, or is it strictly for sales?
Katalyst
@new_user___1882026b09014e561aa470a If the CS team manages renewals and expansion as opportunities in Salesforce, then yes, a lot of the value applies: pipeline analysis, meeting briefs, follow-up drafting, contact signals. The agent doesn't distinguish between a new deal and a renewal. That said, our core features and recommendations are tuned for sales workflows today. But it's a use case we're watching closely.
Katalyst
@new_user___1882026b09014e561aa470a Adding to Avneet, the renewal-slip detection is the piece CS folks tend to find most useful, catching one going quiet before the date arrives. Would genuinely like to hear how it works for your team if you try it.
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.