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
Customer inquiries arrive across multiple support channels via Ada's omnichannel capabilities.
▾ source quote
“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
The AI agent automatically resolves 69% of all support inquiries, dramatically reducing response times.
▾ source quote
“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
The AI agent integrates with Salesforce to pull unique data points on each customer.
▾ source quote
“Checkr integrated their AI agent with Salesforce so that it could pull unique data points on each customer”
4
Personalized response delivery
Output
The AI agent greets customers by name, understands issue context, recognizes customer size, and delivers a personalized response.
▾ source quote
“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
The AI agent underwent rigorous testing and certification to meet strict standards before handling all inquiry types.
▾ source quote
“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 other inquiries.”
6
Conversation analysis and refinement
Feedback loop
By analyzing conversations and resolution rates, Checkr identifies opportunities to refine support content and improve response accuracy.
▾ source quote
“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 support team members transitioned into conversation designer roles to optimize AI responses.
▾ source quote
“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
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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.
This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.