Customer support · Production

Checkr scales customer support with Ada AI agent, achieving 162% CSAT improvement and 69% auto-resolution

The problem

Checkr's traditional support model was not equipped to handle rapid growth and inquiry volume spikes during peak hiring seasons, with headcount potentially needing to double and over 20 or 30 fragmented email addresses in use, while needing to maintain compliance standards at scale.

Workflow diagram · grounded in source
1
Omnichannel inquiry intake
trigger
“With Ada's omnichannel capabilities, Checkr was able to assign the same AI agent to their various support channels”
2
AI agent auto-resolves inquiry
ai_action
“Checkr's AI agent now automatically resolves 69% of all their support inquiries, dramatically reducing response times and improving the customer experience across chat and email”
3
Salesforce customer data pull
integration
“Checkr integrated their AI agent with Salesforce so that it could pull unique data points on each customer”
4
Personalized response delivery
output
“their AI agent to greet customers by name, understand the issue context, recognize whether they are a small or large customer, and give a personalized response”
5
Compliance validation and phased rollout
validation
“Checkr launched their AI agent with a phased deployment, initially limiting its scope to non-compliance inquiries. The AI agent then underwent rigorous testing and certification, ensuring it would meet strict standards before it handled …”
6
Conversation analysis and refinement
feedback_loop
“By analyzing conversations and resolution rates, Checkr identified opportunities to refine support content and improve response accuracy”
7
Conversation designer optimization
human_review
“high-performing members of the support team transitioned into conversation designer roles, optimizing AI responses rather than handling repetitive tickets”
Reported outcome

Checkr's AI agent automatically resolves 69% of all support inquiries, achieved a 162% improvement in CSAT scores, manages the equivalent of 150 FTE workloads, and allowed the company to scale support without increasing headcount.

Reported metrics
CSAT improvement162%
CSAT improvement within 4 weeks2x
Support inquiries automatically resolved69%
AI agent CSAT score at 9 months76%
Show all 8 reported metrics
CSAT improvement162%
CSAT improvement within 4 weeks2x
support inquiries automatically resolved69%
AI agent CSAT score at 9 months76%
typical and peak support headcount40 to 50 people, could double during peak times
fragmented email addresses managed before AIover 20 or 30 email addresses
ROIincreased their ROI
cost savingscost savings
Reported stack
AdaSalesforce
Source
https://www.ada.cx/case-study/checkr
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Checkr's AI agent automatically resolves 69% of all support inquiries, achieved a 162% improvement in CSAT scores, manages the equivalent of 150 FTE workloads, and allowed the company to scale support without increasi…

What tools did this team use?

Ada, Salesforce.

What results were reported?

CSAT improvement: 162%; CSAT improvement within 4 weeks: 2x; Support inquiries automatically resolved: 69%; AI agent CSAT score at 9 months: 76% (source-reported, not independently verified).

How is this customer support AI workflow structured?

Omnichannel inquiry intake → AI agent auto-resolves inquiry → Salesforce customer data pull → Personalized response delivery → Compliance validation and phased rollout → Conversation analysis and refinement → Conversation designer optimization.

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