Perry Ellis · Yotpo
1.4% — conversion rate
Independently reportedRetail & e-commerce · Customer support · NLP · Scaled
Customer support
Arbor · Lorikeet
Vendor-reportedEnergy & utilitiesUSIn productionStartupNLP
Vendor-reported. The customer is named and the numbers are quoted from the source page, but the account comes from the vendor. No independent confirmation.
Reported by lorikeetcx.ai (vendor self-report). Checked against the source page on 2026-09-06. 2 of 2 figures below appear on that page word for word.
The problem
Arbor needed AI that could solve hard problems, not just deflect, handling complex, state-specific inquiries tied to brokerage rules, utility breakdowns, and market conditions.
What was deployed
- Vendor
- Lorikeet
- Products
- Lorikeet, Olive (Arbor's AI agent)
- Technique
- NLP
- Build or buy
- Bought off the shelf
- Deployment
- SaaS
- Data used
- state-specific, utility provider, and energy nuances from their knowledge base (Notion integration) and real-time customer data.
- Scale
- across 13 states
What changed
Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.
89%CSAT
“89% CSAT”
Quoted word for word from the source
4.7Trustpilot ratingfrom 4.6
“4.7 Trustpilot rating, up from 4.6 before Lorikeet”
Quoted word for word from the source
on parAI CSAT compared to human agents
“1:1 AI CSAT on par with human agents”
Quoted word for word from the source
“The Lorikeet team loves untangling complex models — they know exactly what issues we’re solving for.”
Difficulties and limits
- Their knowledge base wasn’t just state-specific — it spanned state, utility provider, and energy nuances.
- When confidence drops or policies require human judgment, the agent self-escalates cleanly, avoiding loops or vague replies — and routing tricky account issues to the right human at the right time.
Sources
- lorikeetcx.ai — vendor self-report
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