Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
Altruist · Cast AI
Vendor-reportedBanking & financePilotEnterprise
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 cast.ai (vendor self-report). Checked against the source page on 2026-09-06. 4 of 4 figures below appear on that page word for word.
The problem
Altruist's platform consumed significant cloud resources for daily workloads. Managing complexity while controlling cost and scalability was a challenge.
What was deployed
- Vendor
- Cast AI
- Products
- Cast AI
- Build or buy
- Bought off the shelf
- Deployment
- SaaS
- Data used
- workload requests, resource utilization
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.
45%+cloud cost savings
“45%+ cost savings”
Quoted word for word from the source · round percentage, no baseline given
85–130 hoursengineer time savedper month
“Engineers save 85–130 hours per month previously spent on manual work and incident response”
Quoted word for word from the source
13x–26x value per $1 spentROI
“Every $1 spent on Cast AI delivers $13–$26 in value”
Quoted word for word from the source
1,350% to 2,600%ROI
“Our return on investment with Cast AI has been exceptional, ranging from 1,350% to 2,600%.”
Quoted word for word from the source · round percentage, no baseline given
no downtimedowntimetwo or three years
“In the two or three years we’ve been working together, there has been no downtime, so we’re confident about our journey.”
Quoted word for word from the source
ROI as stated: Every $1 spent on Cast AI delivers $13–$26 in value; return on investment with Cast AI has been exceptional, ranging from 1,350% to 2,600%.
“Cast unlocked a lot of our time for experimentation and innovation rather than daily toil. There’s a bit of “magic,” if you want to call it that, in the way Cast works – and my team enjoys that.”
Difficulties and limits
- End-of-day processes triggered sharp, short-lived surges in demand.
- Continuous reporting and performance-monitoring environments ran 3-5x normal production traffic.
- Workloads had to be divided across working groups to stay cost-efficient, making it hard to manage complexity while controlling cost and scalability.
- At the moment, we’re only using Cast in non-production environments, where our cloud footprint is actually bigger than in production. The reason we don’t yet have it in prod comes from a combination of factors, like internal processes that we need to mature to build that confidence.
Sources
- cast.ai — vendor self-report, 2026-01-14
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