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.
Really like that this focuses on hygiene, most
sales tools tell you what happened, but staying on
top of records and follow-ups after every call is
the part that actually gets skipped when reps are
busy. Curious how the AI Resolution feature decides
which signals are worth surfacing versus just noise,
is that tuned per team, or does it learn from how
each rep actually works?
Congrats on the launch, this is exactly the kind of
unglamorous problem that's worth solving well.
Katalyst
@melissa_capello Thanks! Both, really, signals key off what actually moves a deal, and each rep's agent runs on their own context, so what surfaces reflects how that rep works, not a one-size filter.
Katalyst
@melissa_capello Thanks Melissa, and you pinned the exact reason we built it, the after call hygiene is the part that quietly gets skipped, not the recap. On surfacing, it's tuned by what actually moves a deal and sharpened by each rep's own context, so the signal to noise reflects how you work rather than a fixed rule.
I really like the shift from making reps update Salesforce to having the AI proactively move deals forward. One thing I'm curious about though, how do you handle deal context that's mostly happening over WhatsApp or other informal channels instead of email and calendar? That's still a big blind spot for a lot of sales teams, especially in emerging markets.
Katalyst
@jasnoor_singh_oberoi Good callout, and honest answer: we don't capture WhatsApp yet. Today it's meetings and email. But it's exactly where we're headed, WhatsApp, field calls, phone, the informal channels where a lot of real deal context lives, especially in emerging markets. That blind spot's the frontier we're building toward.
Katalyst
@jasnoor_singh_oberoi To add, this is a blind spot we take seriously, so much real deal context lives in informal channels, and pretending otherwise would just recreate the stale CRM problem. Meetings and email are where we're sharpest today, but closing that gap is exactly the direction we're building. Appreciate you naming it.
Katalyst
@realanupreet This comes up with enterprise teams and it's exactly the kind of infra requirement we take seriously. Rather than answer it loosely here, let me get you specifics, what's your setup?
Katalyst
@realanupreet To add, routing critical data through your own AI infrastructure is a legitimate enterprise requirement, not an edge case, so it's worth getting the specifics right rather than a quick yes or no. Happy to dig into your setup alongside Div, exactly the kind of thing we'd rather scope properly than hand wave.
How does Katalyst prevent inaccurate AI-generated insights from affecting pipeline forecasts or CRM data?
Katalyst
@robert_dimla Every update shows its reasoning and needs rep approval early on, and high-stakes fields like forecast category can stay approval-only, so a bad read never silently hits your pipeline.
Katalyst
@robert_dimla To add, this is the guardrail we cared about most, nothing writes silently. Every update carries its reasoning and passes through rep approval, so an inaccurate read gets caught before it ever touches your forecast rather than after. Accuracy you can see beats accuracy you're asked to assume.
This is a smart approach to the "AI does the boring CRM work" problem.
Since Katalyst is auto-creating records and updating fields based on
call/email reasoning, how are you handling cases where the AI
misreads a signal — like marking a deal as "slipping" when it
actually wasn't, or auto-filling a field with the wrong next step?
Curious if there's a review/undo layer before changes go live in
Salesforce.
Katalyst
@amjad_shaik Yes, changes surface for rep approval before they land, each with its reasoning, so a misread like a wrongly flagged "slipping" deal gets caught upfront, not after. And anything it writes automatically is logged in the activity history, so it's reviewable and reversible.
@divyansh_lohia That's a solid design — surfacing low-confidence writes as suggestions
rather than auto-committing them is the right call, and having
everything logged/reversible closes the loop nicely. Makes sense why
you built it that way coming from watching reps lose hours to this
exact busywork at Datadog. Following along, curious to see how this
holds up once you're past enterprise pilot and into messier mid-market
data.
Katalyst
@amjad_shaik One thing to add. The agent also knows when it isn't sure. If confidence is low, it never auto-writes anything, it just parks it as a suggestion for the rep to look at. And if a wrong value ever does get accepted, fixing it is one inline edit in Katalyst, which syncs straight back to Salesforce. So a misread costs the rep a few seconds, not a data cleanup project.
What's your retrieval architecture? Are you using vector search, structured CRM data, or both?
Katalyst
@shreyans_jain20 Both. Structured retrieval on the CRM objects, vector search over calls, emails, and calendar. The part we obsess over is grounding: every write traces back to the exact source it came from, so a field update can be audited to the specific call or email. Retrieval that can't cite itself is a non-starter when you're writing to someone's CRM
Katalyst
@shreyans_jain20 Short answer, both. The Salesforce data we treat as structured truth and query directly, no embedding, because you want exact field values. Calls, emails, and calendar run through vector search so we pull the right context per deal. One gives you the state, the other gives you the story
The "Do This Now" section is probably my favorite part. How does Katalyst decide what deserves my attention first?
Katalyst
@saksham_diwan Glad that's the one that stuck. It ranks by where your time actually moves the needle: a mix of [deal value], [risk of slipping], and [fresh signals from calls and emails]. Less "here's everything," more "do this next
Katalyst
@saksham_diwan Glad that's the one that stuck. It ranks by where your time actually moves the needle: a mix of [deal value], [risk of slipping], and [fresh signals from calls and emails]. Less "here's everything," more "do this next