Enterprise Native Multimodal Model Platform
An enterprise-grade native multimodal model platform — private / on-prem deployment with full-modal I/O. A hybrid architecture (LLM + classical algorithms + rule engine) plus RAG + knowledge graphs suppresses hallucinations, bringing world-model-grade capability to finance, manufacturing, energy and retail under full control.
Key Capabilities
Four key capabilities required to put this product into production.
Private / On-Prem Deployment
Pure intranet, hybrid cloud or信创-stack — data stays in domain.
Full-Modal I/O
Text, code, audio, image, video unified I/O — one platform, every scenario.
Triple Hallucination Suppression
LLM + classical algorithms + rule engine, with end-to-end RAG + KG validation.
Domestic-Chip Ready
Ascend / Cambricon / Hygon full-stack — FP8 train/infer out of the box.
Representative Scenarios
Financial research & compliance Q&A · manufacturing process & QC · energy pipeline inspection · retail product & marketing content production.
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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.
Finance
2 casesIllustration · metrics unverified
Top-broker private deployment
Single platform powers research, compliance and service with unified data and models.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 10–16 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Private deployment
Illustration · metrics unverified
Insurance group multi-line platform
Life, P&C and health share one backbone with line-specific tuning.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 10–16 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Hybrid cloud
Manufacturing
1 caseIllustration · metrics unverified
Manufacturing multi-plant rollout
Unified platform from HQ to 12 plants with per-plant tuning.
- Customer
- Top-tier Process / discrete manufacturers
- Duration
- 10–16 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Hybrid cloud
Telecom
1 caseIllustration · metrics unverified
Telco bulk-procurement LLM appliance
Unified appliance delivery to 31 provinces, local training and inference.
- Customer
- Top-tier Telcos & comms operators
- Duration
- 6–8 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Hybrid cloud
Specs & Fit
Deployment | Private / hybrid cloud / on-premise |
I/O | Text / code / audio / image / video |
Min cluster | 4×A800 or NPU equivalent |
Hallucination control | RAG + knowledge-graph cross-check |
SLA | 99.9% availability, 24/7 support |
FAQ
Is hallucination actually suppressed?
LLM + classical algorithms + rule engine; critical outputs go through RAG + knowledge-graph double-check.
What does private deployment need?
Starts from 4×A800; supports Ascend domestic clusters; typical 6–8 week delivery.
Can it integrate with existing systems?
Open REST / gRPC / Webhook; built-in adapters for common ERP / CRM / OA.
Quantified Outcomes
We only ship products that deliver ROI — here are the measurable metrics after enterprise rollout.
Hallucination rate <5%, ≥90% expert-Q&A adoption, private-deployment delivery from months to under two weeks.
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.