Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
TELUS · Kong
Vendor-reportedTelecomCanadaIn productionEnterpriseLLMLive 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 konghq.com (vendor self-report). Checked against the source page on 2026-09-05. 3 of 3 figures below appear on that page word for word.
The problem
The API environment was scaling faster than the organization’s ability to govern, observe, and stress-test it, leading to fragmentation and operational impact.
What was deployed
- Vendor
- Kong
- Products
- Kong Gateway, GitHub Copilot, Aider, Cursor, Roo, Klein
- Technique
- LLM
- Build or buy
- Bought off the shelf
- Deployment
- Hybrid
- Data used
- API gateway runtime behavior data (latency patterns, failure rates, consumer–owner relationships, timeout configurations, unused APIs), real production data.
- Scale
- enterprise-wide
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.
90%platform dashboards and insights generated using AI agents
“>90% platform dashboards and insights generated using AI agents built on top of Kong”
Quoted word for word from the source
100%enterprise-wide visibility across all onboarded APIs
“100% enterprise-wide visibility across all onboarded APIs”
Quoted word for word from the source
hoursTime to build platform dashboards and reportsfrom weeks
“Roughly 90% of TELUS’s internal dashboards and platform tools are now generated or enhanced by AI agents, turning what used to take weeks into hours.”
Quoted word for word from the source
“We see what APIs have been onboarded, who's consuming them, how often they fail, how fast they're responding — and we can show you exactly where the bottlenecks are.”
Difficulties and limits
- API environment scaling faster than ability to govern, observe, and stress-test it
- fragmentation across multiple cloud providers, on-prem data centers, and engineering teams
- slow APIs went unnoticed until business impact was visible
- API owners didn’t know their consumers or how changes affected them
- performance tests didn’t reflect real user behavior
- minor latency spikes went undetected by individual teams
- legacy APIs accumulated technical debt without centralized visibility
- high-traffic events amplified issues across uncoordinated teams
- machines cannot simulate proper Black Friday load very well
Sources
- konghq.com — vendor self-report, 2025-12-18
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