Decoupled Knowledge & Low-Cost Inference
Decouple knowledge from inference — the backbone reasons, enterprise knowledge plugs in inline. Goodbye to bolt-on RAG's inconsistency and latency.
Key Capabilities
Four key capabilities required to put this technology into production.
DeepSeek Decoupled Architecture
Lightweight backbone, swappable knowledge layer — model upgrades don't break enterprise knowledge.
Inline Knowledge Integration
Knowledge is compiled into the reasoning path — no runtime retrieval glue, consistency jumps.
Drastically Lower Cost
Same quality, 60%–80% lower token cost, 50%+ lower long-context latency.
Expert-Grade Output
Hallucination rate drops to single digits in finance, legal and healthcare.
Representative Scenarios
Investment research · compliance & legal opinion · clinical decision support · manufacturing process Q&A · enterprise copilot.
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.
Finance
2 casesIllustration · metrics unverified
Bank knowledge assistant
Inline knowledge replaces bolt-on RAG; answer consistency and expertise lift sharply.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 6–8 weeks
- Tech stack
- Knowledge / RAG
- Deployment
- Cloud-edge hybrid
Illustration · metrics unverified
Insurance Q&A middle-platform
Terms, claims rules and case library inlined into the backbone; answers align with regulator wording.
- Customer
- Banks / brokers / insurers
- Duration
- 10–16 weeks
- Tech stack
- Knowledge / RAG
- Deployment
- Private deployment
Energy
1 caseIllustration · metrics unverified
Energy customer-service cost reduction
Lightweight backbone deployed to edge nodes — both latency and unit cost compressed.
- Customer
- Top-tier Energy & utility groups
- Duration
- 10–16 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Edge / on-prem
Government
1 caseIllustration · metrics unverified
Gov hotline 12345 assistant
City-wide policies and how-to guides inlined for real-time agent assistance.
- Customer
- Government & public sector
- Duration
- 6–8 weeks
- Tech stack
- Multi-agent · Speech / ASR-TTS
- Deployment
- Private deployment
Specs & Fit
Architecture base | DeepSeek decoupled architecture |
Backbone size | 7B / 32B / 70B tiers |
Inference cost | -40% to -60% vs dense baselines |
Knowledge injection | Continued training / LoRA / expert adapters |
FAQ
Difference from traditional RAG?
RAG bolts on retrieval; inline embedding fuses knowledge into the backbone for more consistent output and lower context cost.
Do knowledge updates require retraining?
No. Incremental knowledge ships via low-cost continued training or expert adapters in hours.
Best-fit scenarios?
High-frequency Q&A, expert consulting and industry workflow guidance — knowledge-dense, deterministic reasoning paths.
Quantified Outcomes
We only ship technology that delivers ROI — here are the measurable metrics after enterprise rollout.
Average 65% lower inference TCO, professional-Q&A hallucination <5%, knowledge refresh from months to days.
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.