Customers · Case studies

Real outcomes, on real books.

Every case study on this page carries a named client, a live production system and an attributable outcome number. No mockups, no synthetic pilots — just what the platform moved, how it was measured, and the engineering that carried it.

Customers · Case studies

Real outcomes, on real books.

Every case study on this page carries a named client, a live production system and an attributable outcome number. No mockups, no synthetic pilots — just what the platform moved, how it was measured, and the engineering that carried it.

ORI × AirtelTelecom
31.9%Save rate · engaged customers · MNP window
Retention · Hinglish + Gujarati · Bihar circle

Reversing port-outs at scale with a Hinglish AI retention voicebot

539 saves in the June MNP window, Bihar circle. Counted only on confirmed CANCEL-to-1900.

Read the case study →
ORI × Tata AIGInsurance
59.78%Auto-renewal rate · 8,662 customers
Renewals · Bilingual · Live IPDS APIs

Scaling insurance renewals with Asmita, a bilingual GenAI voicebot

250,000+ automated attempts; IDV changes handled on the call itself, 24/7 in the T-45 window.

Read the case study →
ORI × Muthoot FincorpNBFC
~1 in 3Best-bucket recovery · Post-Due Bandhan Plus
Collections · 5 languages · In-call payments

AI voicebot collections program drives measurable recoveries

5,171 of 21,796 accounts recovered in month two; payment completes inside the call.

Read the case study →
ORI × CredgenicsBFSI
5–8LCalls/day · 16+ lending brands
Collections · Multi-brand · 6 languages

Powering one partner's multi-brand collections voice channel at scale

70+ bot builds across 16+ lending brands, 300 concurrent calls; monthly volume up ~17× since ramp-up.

Read the case study →
ORI × ASUS IndiaConsumer
27×Monthly leads · dealer engagement to 43×
Multi-audience · Web + WhatsApp · Onebot

One platform, three journeys: powering ASUS India's consumer, commercial and dealer conversations

Self-serve support +135%; 96–97% intent recognition held through the spike.

Read the case study →
The measurement discipline

Every number here is tied to an event we can point at.

Retention is counted only on the CANCEL-to-1900 SMS being confirmed on the call, not a verbal "yes". So the number understates the true business impact, deliberately.

Renewals are counted only when the payment settles, not when the customer accepts on the call. So the number tracks money in, not intent.

Collections are counted only against paid/unpaid dispositions per bucket per language, with the paid figure fed by the payment gateway in the call itself.

Multi-audience outcomes are attributed by bot, channel and month, so category-level gaps surface rather than blend into one flattering headline.

Your book next

Bring one portfolio. We'll bring the baseline.

The measurement discipline

Every number here is tied to an event we can point at.

Retention is counted only on the CANCEL-to-1900 SMS being confirmed on the call, not a verbal "yes". So the number understates the true business impact, deliberately.

Renewals are counted only when the payment settles, not when the customer accepts on the call. So the number tracks money in, not intent.

Collections are counted only against paid/unpaid dispositions per bucket per language, with the paid figure fed by the payment gateway in the call itself.

Multi-audience outcomes are attributed by bot, channel and month, so category-level gaps surface rather than blend into one flattering headline.

Your book next

Bring one portfolio. We'll bring the baseline.