Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
Evolve · Barq Digital AI
Vendor-reportedProfessional servicesUnited StatesIn productionLLM
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 barqdigitalai.com (vendor self-report). Checked against the source page on 2026-09-06. 5 of 5 figures below appear on that page word for word.
The problem
Slow AI conversation latency and fragmented candidate engagement data, leading to frustrated candidates, missed opportunities, and manual, error-prone workflows.
What was deployed
- Vendor
- Barq Digital AI
- Products
- Lovable, Supabase
- Technique
- LLM
- Build or buy
- Built in-house or to order
- Deployment
- Custom build
- Data used
- Candidate info, hiring manager feedback, and client pipeline updates (previously scattered across spreadsheets and email threads).
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 secondAI latencyfrom 3-5 seconds
“| AI conversation latency | 3-5 seconds | < 1 second | 60% reduction |”
Quoted word for word from the source
1 centralised systemData consolidationfrom 5+ tools
“| Data fragmentation | 5+ tools | 1 centralised system | 80% consolidation |”
Quoted word for word from the source
AutomatedCandidate outreach timefrom Hours per batch
“| Candidate outreach time | Hours per batch | Automated | 90% time saved |”
Quoted word for word from the source
< 24 hoursHiring manager feedback timefrom 2-3 days
“| Hiring manager feedback time | 2-3 days | < 24 hours | 2x faster |”
Quoted word for word from the source
100%Client reportingfrom Manual monthly
“| Client reporting | Manual monthly | Automated real-time | 100% automated |”
Quoted word for word from the source
“We were spending more time managing data than actually recruiting. The AI was supposed to save us time, but the latency made it feel like a bottleneck instead of an accelerator.”
Difficulties and limits
- Slow AI conversation latency
- Fragmented Data Management
- Frustrated candidates who expected instant replies
- Missed opportunities to engage high-value leads
- Reduced trust in the AI system
- Spreadsheets for lead tracking
- Manual email threads for hiring manager coordination
- No centralised view of candidate engagement status
Sources
- barqdigitalai.com — vendor self-report
Similar deployments
GE Aerospace
Independently reportedManufacturing · Process automation · Classical ML · Scaled
Apollo Tyres
under 10 minutes — Time for root cause analysis
Independently reportedManufacturing · Process automation · AI agents · In production
Broadcom · Moveworks
88% — autonomous IT resolution rate
Independently reportedTechnology & software · Process automation · NLP · Scaled
Amazon · Amazon
10% more — shopper spend
Independently reportedRetail & e-commerce · Process automation · Computer vision · Scaled · 2024
icare · SAS
increased by 15% — accuracy
Independently reportedInsurance · Process automation · Classical ML · Scaled · 2019