Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
Met Office · AWS
Independently reportedPublic sectorUnited KingdomPilotEnterpriseLLM
Independently reported. An independent or peer-reviewed source reports this result, alongside the vendor.
Reported by government-transformation.com (independent reporting). Checked against the source page on 2026-09-06. 2 of 2 figures below appear on that page word for word.
The problem
Generating textual forecasts from raw weather model data, a task that typically takes expert meteorologists hours to a day.
What was deployed
- Vendor
- AWS
- Products
- Amazon Nova Foundation Model
- Models
- Amazon Nova Foundation Model
- Technique
- LLM
- Build or buy
- Bought off the shelf
- Data used
- raw data output of weather models, a day’s worth of hourly forecast information from a multitude of data sources
- Timeline
- 1 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.
under five minutesTime to complete textual forecast generationfrom a few hours to a dayAfter four weeks of prototyping
“After four weeks of prototyping, the system was able to complete this task in under five minutes”
Quoted word for word from the source
between 52% and 62%Accuracy of textual forecastsAfter four weeks of prototyping
“After four weeks of prototyping, the system was able to complete this task in under five minutes with between 52% and 62% accuracy.”
Quoted word for word from the source
“[In doing this,] we are minimising time building something that already exists and maximising time building something new that usefully extends it, exploiting our particular expertise and deep weather and climate domain knowledge.”
Difficulties and limits
- Machine learning models lacking an inherent understanding of meteorology
- Ensuring predictions are coherent
- Struggling with predicting the development of extreme weather events
- Extreme weather events cannot be sufficiently represented in training datasets
- Need to ensure the reliability and trustworthiness of outputs
- Machine learning models lacking an inherent understanding of meteorology
- Ensuring predictions are coherent
- Struggling with predicting the development of extreme weather events
- Extreme weather events cannot be sufficiently represented in training datasets
- Need to ensure the reliability and trustworthiness of outputs
Sources
- government-transformation.com — independent reporting, 2026-06-10
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