Media-group rights asset lake
Massive program archives searchable by content semantics; rights clearance automated.
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
- Top-tier Cross-industry enterprises
- Project duration
- 10–16 weeks
- Tech stack
- Multimodal backbone · Vector search
- Deployment
- Hybrid cloud
2. Business Context & Pain Points
The customer is a Enterprise / Group player in General, 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 "Multimodal Data Fusion & Vector Data Lake", Global AI DAO integrated the following capability stack into a single solution:
- Multimodal backbone —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Vector search —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Deployment: Hybrid cloud, balancing data compliance, performance SLA and operability.
4. Quantified Outcomes (Before vs After)
| Metric | Before | After | Delta |
|---|---|---|---|
| asset reuse | 100% | 165% | +65% |
| clearance headcount | 100% | 30% | -70% |
Source metric: +65% asset reuse, -70% clearance headcount.
5. Specs & Fit
| Data types | Structured + unstructured + multimodal |
| Preprocessing | OCR / ASR / CV pipelines |
| Vector dim | 768 / 1024 / 1536 (configurable) |
| P95 retrieval | <80ms at billion-scale |
| Scaling | Horizontal sharding + smart tiering |