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.
The meeting prep feature actually saved me time this morning, I just reviewed the brief right before the call instead of digging through Salesforce myself.
Katalyst
@alya136521 that's exactly the swap we were after, a quick brief instead of digging through Salesforce right before you dial in. that pre-call scramble quietly eats time across a whole week, so glad it gave you the morning back. thanks for sharing.
Katalyst
@alya136521 From the build side, this is one of my favorites to hear. The whole point of the brief was to collapse that pre-call scramble into a quick read, so you walk in prepped without the digging. Multiply it across every call in a week and it's real time back. Thanks for sharing 🙏
That's clever. When two reps share an account, do their signals actually merge or stay separate in the pipeline view?
Katalyst
@dhiraj_patel5 They see the same ones. Signals are tied to account and opportunity ownership, so when two reps share an account or opp, the same signals show up on both their dashboards, it's one shared view, not two separate feeds. What stays personal to each rep is their agent's own context from their emails and calls, but the signals themselves are common ground.
Katalyst
@dhiraj_patel5 To confirm from the build side, signals live at the account and opportunity level, not per rep, so shared owners genuinely see one common feed rather than two copies drifting apart. What stays personal is each rep's own agent context, the email and call intelligence that only runs for the deal's owner. Shared truth on the account, private context on the rep.
Been looking at Attio and Clarify. The pitch of a clean AI-native CRM is tempting honestly. But migrating 4 years of Salesforce data and workflows feels insane. How do you think about that trade-off?
Katalyst
@arjun_bagur That's exactly why we built it this way. The clean AI-native CRMs are nice, but you nailed the catch, no one wants to migrate four years of Salesforce data and workflows. Katalyst skips that entirely: it runs directly on your existing Salesforce, reads your real schema, and adds the AI-native layer on top. You keep your system of record and get the intelligence, no rip-and-replace.
Katalyst
@arjun_bagur one more thing worth knowing on that trade-off: getting started on Katalyst is the opposite of a migration. You connect Salesforce, pick which fields Katalyst gets to work with, and it syncs from there, you're running the same day. And because everything it produces gets written into Salesforce itself, you're never creating a second system of record. Worst case you stop using Katalyst and your Salesforce is simply cleaner than it was before. The trade-off with rip-and-replace is you're betting the whole org on the new tool. This way the bet is basically zero.
Love the focus on reducing CRM busywork with AI. Does Katalyst learn from each team's sales process over time to improve its recommendations?
Katalyst
@dhatri_rai1 Yes, it learns from how each rep and team actually sells, their calls and emails, so recommendations get sharper over time instead of staying generic.
Katalyst
@dhatri_rai1 To add, that's why it improves per rep, not just per company. The context comes from your own calls and emails, so the recommendations start reflecting how you actually sell rather than a one size fits all playbook.
How accurate is the AI? I feel like every AI tool I've tried hallucinates or gets things wrong. What's different here?
Katalyst
@sai58 Fair concern, I'd be skeptical too. The difference is Katalyst isn't generating opinions, it's extracting facts from your own calls and emails and updating fields off them. Every change traces back to the exact source it came from, so it's less "trust the AI" and more "here's the line in the call this update is based on." And nothing writes blind, you can review what it changed. We didn't try to build a model that never errs, we built it so when it does, you can see and catch it
Katalyst
@sai58 To add, most tools hallucinate because you're asking them to invent, Katalyst is doing the opposite, pulling facts from your own calls and emails and tracing each update back to the exact source line. That's the real difference: you're not trusting a black box, you're checking a citation. And nothing writes blind, so a wrong read gets caught, not buried.
What kinds of signals does Katalyst track? Are these just news alerts or something more useful?
Katalyst
@gurmaandeep_singh More than news alerts. Katalyst tracks account level signals that actually move deals: hiring surges, new partnerships, contact and champion changes, plus activity signals pulled straight from your own calls, emails, and calendar. Each one is AI scored and prioritized so you're not drowning in noise, you just see what's worth acting on and why, right next to the deal it affects.
Katalyst
@gurmaandeep_singh Quick example of what that looks like live: a champion at one of your accounts switches jobs and updates LinkedIn. Katalyst flags it, your champion just left, but there's now a warm intro waiting at their new company. That's the kind of thing that dies in a feed and quietly costs you a deal.
For enterprise sales teams with complex Salesforce setups, custom objects, non-standard field names, and org-specific process rules, how much configuration work happens before Katalyst is actually writing to the right places? "Records created, fields updated" assumes the field mapping is solved, which on a heavily customized enterprise org is rarely trivial.
Katalyst
@ansari_adin Katalyst reads your live Salesforce schema directly, so custom objects and non-standard names surface as-is, no fixed template to map against. You just scope which record types and fields it can touch. Config is setting boundaries, not hand-mapping.
@divyansh_lohia Reading the live schema directly is the right approach. Curious what happens when the schema has 200 custom fields across a mature enterprise org, does Katalyst surface all of them and expect the admin to scope down, or is there some inference about which fields are actually active and relevant based on recent usage patterns?
Katalyst
@ansari_adin one detail that matters a lot on a heavily customized org: those boundaries aren't checked once at setup, they're re-checked at the moment of every single write. Each field has its own permission level, and if your admin flips a field to read-only after Katalyst has already drafted a suggestion for it, the write gets blocked at accept time. Same if Salesforce itself marks the field non-updateable. So config drift, which is constant on enterprise orgs, doesn't turn into wrong writes. The current state of your org always wins over whatever was configured earlier.