customer_support · saas · workflow
Checkr scales customer support with Ada AI agent, achieving 162% CSAT improvement and 69% auto-resolution
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Omnichannel inquiry intake
Customer inquiries arrive across multiple support channels via Ada's omnichannel capabilities.
Tools used
Ada
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.
Results
Time saved2x
Volume162%
Cost replacedcost savings
Grounding & classification
Source type: vendor customer story
34 fields verified against source quotes, 2 dropped as unverifiable.
content generationconversational aipersonalizationsupport agentchat transcriptemailsupport ticketfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedsource backedtools describedvendor confirmedworkflow describedprofessional servicessoftwareautomation ratecost reductioncustomer satisfactiondeflection rateemployee productivityvendor customer storycustomer supportautonomous resolution