Multi-region SaaS cost governance
LLM calls across 8 global regions metered and capped per tenant/product.
Evidence notice: cases, figures and inferred project metadata are unverified scenario illustrations, not established client outcomes, credentials or return promises. Signed contracts define scope, fees, baselines, acceptance and data responsibilities; client business data stays off-chain.
1. Project Overview
- Customer type
- Multinational Government & public sector
- Project duration
- 12–20 weeks
- Tech stack
- Speech / ASR-TTS
- Deployment
- Private deployment
2. Business Context & Pain Points
The customer is a Global / Multinational player in Government, with the core pain concentrated in "General". Manual workflows and point tools could not scale or stay consistent, driving up response time, operating cost and compliance risk. This project delivered an end-to-end AI upgrade for that workflow.
3. Solution
Anchored on "AI Engineering & Ops Platform", Global AI DAO integrated the following capability stack into a single solution:
- Speech / ASR-TTS —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Deployment: Private deployment, balancing data compliance, performance SLA and operability.
4. Quantified Outcomes (Before vs After)
| Metric | Before | After | Delta |
|---|---|---|---|
| monthly inference bill | 100% | 59% | -41% |
Source metric: -41% monthly inference bill, 98% anomaly-call interception.
5. Specs & Fit
| Core modules | MLOps / AgentOps / Sandbox / Observability |
| Observability | Trace / token / cost / error rate |
| Release | Canary / blue-green / rollback |
| Integrations | Prometheus / Grafana / Webhook |