Multimodal Data Fusion & Vector Data Lake
Truly unify structured and unstructured enterprise data — unified governance, vectorization and permissions provide trustworthy, traceable fuel for AI.
Key Capabilities
Four key capabilities required to put this technology into production.
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.
Representative Scenarios
Enterprise knowledge platform · customer 360 · multimodal ChatBI · compliance audit & forensics.
Download Solution Playbook
Generate a printable white paper for this module
Opens a playbook page with key capabilities, scenarios, outcomes, specs and case digest — use the browser's Save as PDF to distribute.
Case Studies
Filter anonymous scenario illustrations by industry, scenario and scale. Metrics and inferred customer, duration, stack and deployment metadata are unverified, not established customer outcomes.
General
3 casesIllustration · metrics unverified
Group customer 360
Structured CRM, call recordings and video visits vectorized together for real-time retrieval.
- Customer
- Top-tier Cross-industry enterprises
- Duration
- 10–16 weeks
- Tech stack
- Multimodal backbone · Speech / ASR-TTS · Vector search
- Deployment
- Hybrid cloud
Illustration · metrics unverified
Auto-OEM R&D data lake
Road-test video, sensor streams and test reports vectorized together.
- Customer
- Top-tier Cross-industry enterprises
- Duration
- 6–8 weeks
- Tech stack
- Multimodal backbone · Vector search
- Deployment
- Hybrid cloud
Illustration · metrics unverified
Media-group rights asset lake
Massive program archives searchable by content semantics; rights clearance automated.
- Customer
- Top-tier Cross-industry enterprises
- Duration
- 10–16 weeks
- Tech stack
- Multimodal backbone · Vector search
- Deployment
- Hybrid cloud
Healthcare
1 caseIllustration · metrics unverified
Medical imaging + EHR fusion
DICOM imaging, EHR and lab reports governed in one lake.
- Customer
- Top-tier Tier-3 hospitals & healthcare groups
- Duration
- 6–8 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Private deployment
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 |
FAQ
How is it different from a data warehouse?
Warehouses excel at structured analytics; vector lakes natively handle unstructured & multimodal fused retrieval.
How is multi-tenancy isolated?
Namespace + row-level ACL + vector-space partitioning — triple-layer isolation.
How does hot/cold tiering work?
Auto-migrates by access frequency — hot data in-memory, cold data tiered to object storage.
Quantified Outcomes
We only ship technology that delivers ROI — here are the measurable metrics after enterprise rollout.
Data onboarding from quarters to weeks, vector recall P95 <80ms, 40%+ storage cost reduction.
Defined scope. Accountable delivery.
Staged pricing & acceptance
Discovery, pilot and rollout have separate scope and fees. Contracts define baselines, evaluation samples, acceptance metrics and responsibility. Results are not unconditionally guaranteed.
Customer business data stays off-chain
Business records and personal data are not written to a blockchain. Confirm deployment region, model providers, access, retention and deletion in writing before work begins.
Clear contracting & delivery responsibility
Before signing, obtain and verify the legal entity, registration, jurisdiction, delivery owner and dispute terms. DAO collaboration does not replace contractual responsibility.