Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
DJ & Lindsey Real Estate · Shilo
Vendor-reportedConstruction & real estateUnited StatesIn productionMid-marketNLPLive 2024
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 shilo.ai (vendor self-report). Checked against the source page on 2026-09-05. 7 of 7 figures below appear on that page word for word.
The problem
Managers couldn’t listen to every call, leading to inconsistent sales techniques, coaching that couldn’t keep up, and no clear picture of what was converting.
What was deployed
- Vendor
- Shilo
- Products
- Shilo
- Technique
- NLP
- Build or buy
- Bought off the shelf
- Deployment
- SaaS
- Data used
- Every agent call, inbound and outbound
- Scale
- 79 agents
- Timeline
- 4 months
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.
1,112outbound call volumefrom 5084 months
“119% increase in outbound call volume”
Quoted word for word from the source
1,467total call volumefrom 768January to April
“+91% Call Volume Total calls grew from 768 in January to 1,467 in April.”
Quoted word for word from the source
agent conversation quality
“22% increase in agent conversation quality”
Quoted word for word from the source
120000additional revenue
“$120K additional revenue from better calls”
Quoted word for word from the source
119deals closed4 months
“119 Deals Closed”
Quoted word for word from the source
3.94%conversion rate4-month period
“119 have now closed at a 3.94% conversion rate.”
Quoted word for word from the source
call quality score (individual agent)4 months
“One agent improved their call quality score by 45% in 4 months.”
Quoted word for word from the source · round percentage, no baseline given
ROI as stated: 650-1,338% ROI Even at the most conservative attribution model (50%), the investment returned $120,000 in incremental revenue against a $16,000 cost.
Difficulties and limits
- No visibility into call quality. Managers couldn't listen to every call.
- Inconsistent sales techniques. Agents had varying skill levels with no standardized feedback loop.
- Coaching that couldn't keep up. With the team growing, personalized coaching was falling behind.
- No clear picture of what was converting. Difficult to identify which behaviors were actually producing closings.
Sources
- shilo.ai — vendor self-report, 2026-02-06
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