Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Artificial intelligence, professionally covered
Process automation
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 sellifyai.com (vendor self-report). Checked against the source page on 2026-09-05. 2 of 3 figures below appear on that page word for word; the rest are marked.
Inability to conduct win-back campaigns at scale due to the high number of calls and headcount required, leading to lost revenue from lapsed customers. Staffing shortages impacting revenue programs.
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.
“generated $220,000 in annual revenue”
Quoted word for word from the source
“The target was around 300 win-back sales. Without AI, that campaign wasn’t going to run at all. With Sellify, it landed close to the target”
Not found on the source page in these words
“zero new headcount.”
Quoted word for word from the source
“I always look at it through the lens of hiring a team of part-time college kids to telemarket. But the dial count to reach enough people to make any meaningful impact - it's just too many bodies and too many phone calls. That was a huge win. A no-brainer.”
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Independently reportedManufacturing · Process automation · Classical ML · Scaled
under 10 minutes — Time for root cause analysis
Independently reportedManufacturing · Process automation · AI agents · In production
88% — autonomous IT resolution rate
Independently reportedTechnology & software · Process automation · NLP · Scaled
increased by 15% — accuracy
Independently reportedInsurance · Process automation · Classical ML · Scaled · 2019
10% more — shopper spend
Independently reportedRetail & e-commerce · Process automation · Computer vision · Scaled · 2024