Auto-OEM R&D data lake
Road-test video, sensor streams and test reports vectorized together.
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
- 6–8 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 "Data & R&D". 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 |
|---|---|---|---|
| issue-reproduction time | 100% | 25% | -75% |
| model iteration cycle | 100% | 60% | -40% |
Source metric: -75% issue-reproduction time, -40% model iteration cycle.
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 |