ANZ Bank · NVIDIA
0.82 — Gini coefficient for assessing risk
Independently reportedBanking & finance · Other · Classical ML · Pilot
Other
Cuebo.ai · Prodinit
Vendor-reportedTechnology & softwareIn 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 prodinit.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
Sales simulation platform's Django monolith made it impossible to scale voice AI independently, created query bottlenecks, and imposed hard concurrency ceilings from LiveKit Cloud's hosted agent tiers.
What was deployed
- Vendor
- Prodinit
- Products
- LiveKit, Django, AWS Amplify, Sentry, PostHog, django-silk, Flower, Deepgram STT, Azure OpenAI, ElevenLabs TTS, Azure Speech, Sarvam, Google Gemini Live, Google Chirp
- Models
- GPT-4o Realtime, Claude Sonnet, Google Gemini Live, Google Chirp, Gemini Chat
- Technique
- LLM
- Build or buy
- Bought and customised
- Deployment
- Hybrid
- Scale
- 10x
- 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.
10xpeak load capacity
“10x peak load capacity”
Quoted word for word from the source
0concurrent session limits
“0 concurrent session limits”
Quoted word for word from the source
eliminateddatabase queries over 500ms
“100% of database queries over 500ms eliminated”
Quoted word for word from the source · round percentage, no baseline given
“Prodinit team brought a rare combination of deep technical expertise and execution speed that allowed us to ship faster, and scale our application's infra under pressure. One of the most trusted and valued members of our journey.”
Difficulties and limits
- scaling voice AI independently from the API
- query bottlenecks under load
- hard concurrency ceilings from LiveKit Cloud's hosted agent tiers
- AI processing latency coupled to general API availability
- slow ORM queries
- task types competing on shared worker pools with no task isolation
- ECS tasks scaled on fixed rules with no connection-aware or queue-depth-aware triggers
Sources
- prodinit.com — vendor self-report
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