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 ticvic.com (vendor self-report). Checked against the source page on 2026-09-06. 4 of 4 figures below appear on that page word for word.
Facilities of national importance depended on humans watching dials, leading to single points of failure and staffing burden.
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.
“AI-driven environmental monitoring for DRDO data centres — one proactively caught power event prevented a $1.3M loss.”
Quoted word for word from the source
“100% Power-event detection”
Quoted word for word from the source
“9.5/10 Client rating”
Quoted word for word from the source
“24/7 Autonomous monitoring”
Quoted word for word from the source
“My rating is 9.5/10! Ticvic delivered an excellent IoT-based product within the agreed timeline. Its compact, handy design makes it easy to deploy in places like server rooms or power rooms.”
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
10% more — shopper spend
Independently reportedRetail & e-commerce · Process automation · Computer vision · Scaled · 2024
increased by 15% — accuracy
Independently reportedInsurance · Process automation · Classical ML · Scaled · 2019