Sales ops · Production

Patch empowers 70% of its team to use AI agents weekly with Dust

The problem

Patch's specialized climate domain expertise was a bottleneck across its sales process: sales reps lacked the knowledge to critically assess sustainability documents, proprietary carbon market data was inaccessible to non-technical staff due to technical barriers, and manually filtering thousands of VCM projects demanded specialized knowledge that few team members possessed.

First attempt

A previous AI tool built for engineers (drawing on GitHub, Slack, and Notion) worked well for engineering use cases but did not extend to other company functions. A separate admin page exposing proprietary carbon market data had zero adoption among sales reps due to technical barriers.

Workflow diagram · grounded in source
1
Sales call preparation need
trigger
“Sales reps, in particular, need this information to prepare for calls and develop account strategies”
2
Sustainability strategy decoded
ai_action
“Now, if you give this agent publicly available information about a company's sustainability strategy, it'll be able to prepare a rigorous analysis”
3
Proprietary database queried
ai_action
“the agent is translating the question from English, formatting the numbers in the database, doing fuzzy matching, and returning the data in an easy-to-understand way”
4
Project recommendations matched
ai_action
“This agent taps into Patch's project database and helps sales engineers filter projects based on customer needs written in plain language”
5
Human review of final recommendations
human_review
“final recommendations still always require human oversight”
6
Expert insights delivered
output
“sales teams get expert-level insights without waiting on climate specialists”
Reported outcome

Within 3 months, 70% of Patch's team became weekly active Dust users across dozens of distinct use cases.
Previously unused proprietary carbon market data went from being used in approximately 10% of sales calls to approximately 70%, and sales teams now access expert-level climate insights without waiting on specialists.

Reported metrics
Weekly active team usage70%
Time to reach 70% weekly active usage3 months
Sales calls using proprietary data (before)about 10%
Sales calls using proprietary data (after)around 70%
Show all 7 reported metrics
weekly active team usage70%
time to reach 70% weekly active usage3 months
sales calls using proprietary data (before)about 10%
sales calls using proprietary data (after)around 70%
time to recreate prior use case in Dustabout 20 minutes
task accessibility improvementstep-function improvements
hackathon Dust adoption15 people
Reported stack
Dust
Source
https://dust.tt/customers/how-patch-empowered-70-of-its-team-to-use-ai-agents-weekly
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Within 3 months, 70% of Patch's team became weekly active Dust users across dozens of distinct use cases.

What tools did this team use?

Dust.

What results were reported?

Weekly active team usage: 70%; Time to reach 70% weekly active usage: 3 months; Sales calls using proprietary data (before): about 10%; Sales calls using proprietary data (after): around 70% (source-reported, not independently verified).

What failed first in this deployment?

A previous AI tool built for engineers (drawing on GitHub, Slack, and Notion) worked well for engineering use cases but did not extend to other company functions.

How is this sales ops AI workflow structured?

Sales call preparation need → Sustainability strategy decoded → Proprietary database queried → Project recommendations matched → Human review of final recommendations → Expert insights delivered.

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