Customer support · Production

Lime achieves 77% reduction in time to first response with Forethought AI triage and self-service

The problem

Lime's support operations were entirely manual: every agent handled every ticket type with no prioritization or routing, agents toggled between Google Translate and their cases for multilingual tickets, compliance-critical tickets such as accidents and city-official complaints were handled in order received rather than by urgency, and there were no self-service channels. Exponential business growth made these gaps unsustainable.

Workflow diagram · grounded in source

Support ticket received

trigger

“Support inquiries were received through email, phone, the Lime app, and a web form to be followed up on by support agents”

Language and category classification

ai_action

“Triage layers the language and category classifications, and works internally to determine different service levels based on severity”

Priority-based queue routing

routing

“Lime has custom tags and triggers set up so that when Forethought predicts a specific language and category, the case is routed to a specific queue based on those tags. Now, support cases are tagged so the most critical inquiries are han…”

AI self-service resolution

ai_action

“With Forethought Solve, they're able to interpret the intent of a customer's request, through their chat widget or email, and instantly search their entire database of knowledge articles and previously resolved tickets to provide the mos…”

RPA-guided automated workflows

integration

“These RPA-guided workflows are automated from beginning to end through pre-set rules. These workflows integrate with internal tools to complete automation tasks around damaged vehicles, charge and payment issues, and receipt questions”

Complex case escalation

routing

“If the customers' question is complex, they're automatically routed to the help center web form, which is then passed on to a support agent”

Reported outcome

With Forethought, Lime automated 27% of email and web channel cases, predicted over 2.5 million language and category tags with 98% of tickets tagged automatically, and achieved a 77% reduction in time to first response, delivering significant cost savings and improved customer satisfaction.

Reported metrics

Cases automated via email and web channels27%

Language and category tags predictedover 2.5 million

Support tickets tagged automatically98%

Time to first response77%

Show all 6 reported metrics

cases automated via email and web channels27%

language and category tags predictedover 2.5 million

support tickets tagged automatically98%

time to first response77%

cost savingssignificant cost savings

customer satisfactionimproving customer satisfaction

Reported stack

Forethought TriageForethought SolveWorkflow BuilderRobotic Process Automation (RPA)

Source

https://forethought.ai/case-studies/lime-case-study/

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

With Forethought, Lime automated 27% of email and web channel cases, predicted over 2.5 million language and category tags with 98% of tickets tagged automatically, and achieved a 77% reduction in time to first respon…

What tools did this team use?

Forethought Triage, Forethought Solve, Workflow Builder, Robotic Process Automation (RPA).

What results were reported?

Cases automated via email and web channels: 27%; Language and category tags predicted: over 2.5 million; Support tickets tagged automatically: 98%; Time to first response: 77% (source-reported, not independently verified).

How is this customer support AI workflow structured?

Support ticket received → Language and category classification → Priority-based queue routing → AI self-service resolution → RPA-guided automated workflows → Complex case escalation.