Sales ops · Production

Vanta's GTM team saves ~400 hours per week with Dust multi-agent platform

The problem

Vanta's GTM teams held critical knowledge in siloed functions — GRC, finance, product, and marketing — making cross-functional work, especially meeting and QBR preparation, labor-intensive and time-consuming.

First attempt

After vetting seven AI platforms, most were either too shallow for enterprise use or too technical for widespread adoption.

Workflow diagram · grounded in source
1
QBR or meeting prep triggered
trigger
“Preparing for customer meetings or quarterly business reviews often meant hours spent pulling data from dashboards, assembling charts, and pasting insights into slides”
2
Domain agents built per function
ai_action
“Each GTM function began by building Dust agents that captured its unique knowledge and workflows. For example: The GRC team created an agent for compliance frameworks. Finance built one for usage insights and revenue signals. Product and…”
3
Agents exposed as APIs
integration
“These agents were then exposed as APIs, turning functional expertise into reusable building blocks that any team can call on demand”
4
Orchestration calls domain agents
ai_action
“the automation uses Dust to call relevant agents, including: Finance for usage metrics GRC for compliance updates Voice of Customer for feedback signals”
5
Pre-built deck and summary delivered
output
“a pre-built deck, speaker notes, and context-rich summary generated in minutes”
6
Slack agent answers in-channel
integration
“The GRC SME agent now answers security and compliance questions directly in-channel”
7
Human review before response
human_review
“with a quick human review before responses go out”
8
Agent updates propagate automatically
feedback_loop
“When a team updates its agent, every connected workflow instantly improves, creating a system that evolves on its own”
Reported outcome

Automating QBR prep reclaimed around two hours per week per rep, saving around 400 hours per week across the team — thousands of collective hours a year.
Dust adoption grew beyond the GTM organization to become company-wide.

Reported metrics
hours saved per week across GTM repsaround 400 hours saved per week
Hours reclaimed annuallythousands of collective hours a year
time saved per rep per week on QBR preparound two hours per week per rep
Dust training attendanceover 180 people
Reported stack
DustSlack
Source
https://dust.tt/customers/how-vantas-gtm-team-saves-thousands-of-hours-annually-with-dust
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Automating QBR prep reclaimed around two hours per week per rep, saving around 400 hours per week across the team — thousands of collective hours a year.

What tools did this team use?

Dust, Slack.

What results were reported?

hours saved per week across GTM reps: around 400 hours saved per week; Hours reclaimed annually: thousands of collective hours a year; time saved per rep per week on QBR prep: around two hours per week per rep; Dust training attendance: over 180 people (source-reported, not independently verified).

What failed first in this deployment?

After vetting seven AI platforms, most were either too shallow for enterprise use or too technical for widespread adoption.

How is this sales ops AI workflow structured?

QBR or meeting prep triggered → Domain agents built per function → Agents exposed as APIs → Orchestration calls domain agents → Pre-built deck and summary delivered → Slack agent answers in-channel → Human review before response → Agent updates propagate automatically.

Related sales ops cases