Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
Zembl · Relevance AI
Vendor-reportedProfessional servicesAustraliaIn productionSmall businessAI agentsLive 2025
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 relevanceai.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.
The problem
Zembl faced increasing inbound lead volumes, time-consuming and inconsistent manual lead research, and missed after-hours inquiries, costing them deals.
What was deployed
- Vendor
- Relevance AI
- Products
- Relevance AI
- Technique
- AI agents
- Build or buy
- Bought and customised
- Deployment
- SaaS
- Data used
- business research (Google, LinkedIn), contact details, energy bills, Salesforce data, email bounce data, contract renewal details
- Scale
- around 10–11 active agents
- Timeline
- 3 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.
30% increasecustomer conversion
“we’ve seen a 30% increase in overall customer conversion.”
Quoted word for word from the source · round percentage, no baseline given
8-9 minutesaverage call timefrom 22 minutes
“I think it’s from 22 minutes down to like nine now, eight minutes or something like the time saved.”
Not found on the source page in these words
almost doubledrate of customers uploading an energy bill before their first sales callfirst six weeks
“The rate of customers uploading an energy bill before their first sales call almost doubled in the first six weeks of Toni going live.”
Quoted word for word from the source
“I’d never go back to manually researching a business, finding out details of the customer’s address, phone numbers, who else is responsible for the energy contracts. The thought of doing that manually now, it’s just not a process we want to go back to.”
Difficulties and limits
- Reps had to manually research each business on Google and LinkedIn, validate contact details, and track down an energy bill before every call, wasting time on low-value tasks.
- Data quality was inconsistent across reps: "You would often have inconsistencies in the way that data was either gathered or managed and then stored."
- After-hours and weekend inquiries went unanswered until the next business day ("you wouldn't speak to one of the sales team until maybe midday on a Monday"), and leads had often already gone elsewhere by then.
- The first agent (Toni) became overwhelmed by the volume and scope of tasks, requiring additional specialized agents (Salesforce Salli, SMS Sammi, Comparison Cami) to be added to handle the load.
Sources
- relevanceai.com — vendor self-report
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