Multimodal Data Fusion & Vector Data Lake
Truly unify structured and unstructured enterprise data — unified governance, vectorization and permissions provide trustworthy, traceable fuel for AI.
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. Key Capabilities
- Full-Modal Fusion —— Business systems + voice/video/doc unified ingest with OCR/ASR/CV pipelines.
- Vector Data Lake —— Native vector indexing + hybrid retrieval — millisecond semantic recall.
- Multi-Tenant & Hot/Cold Tiering —— Tenant isolation by department/project, cold data auto-tiered — TCO drops sharply.
- Lineage Traceability —— Every vector traces back to source file, source system and source permission.
2. Representative Scenarios
Enterprise knowledge platform · customer 360 · multimodal ChatBI · compliance audit & forensics.
3. Quantified Outcomes
Data onboarding from quarters to weeks, vector recall P95 <80ms, 40%+ storage cost reduction.
4. 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 |
5. Case Digest
Group customer 360
Structured CRM, call recordings and video visits vectorized together for real-time retrieval.
Customer-insight generation from weeks to hours.
Medical imaging + EHR fusion
DICOM imaging, EHR and lab reports governed in one lake.
-80% data-prep time for multidisciplinary review.
Auto-OEM R&D data lake
Road-test video, sensor streams and test reports vectorized together.
-75% issue-reproduction time, -40% model iteration cycle.
Media-group rights asset lake
Massive program archives searchable by content semantics; rights clearance automated.
+65% asset reuse, -70% clearance headcount.
6. FAQ
Q. How is it different from a data warehouse?
A. Warehouses excel at structured analytics; vector lakes natively handle unstructured & multimodal fused retrieval.
Q. How is multi-tenancy isolated?
A. Namespace + row-level ACL + vector-space partitioning — triple-layer isolation.
Q. How does hot/cold tiering work?
A. Auto-migrates by access frequency — hot data in-memory, cold data tiered to object storage.