Multi-Tier Semantic Cache Architecture
Semantic-Level Intelligent Matching
Instead of simple text string matching, it leverages LLM semantic understanding to identify duplicate requests. Identical business intents hit the cache directly without repeated compute calls. Three tiers — in-memory high-speed, disk-persistent, and distributed cluster cache — balance response speed and capacity. Dramatically cuts redundant compute consumption and directly lowers overall usage costs.
- —Semantic-level intelligent matching: identifies duplicate requests via LLM semantic understanding, so identical business intents hit the cache directly
- —Multi-tier cache design: in-memory high-speed, disk-persistent, and distributed cluster cache in three layers
- —Dramatically cuts redundant compute and directly lowers overall cost — a core enabler of the 20–40% savings
Application scenarioRAG retrieval and high-frequency support bots. An e-commerce bot achieved 42% hit rate, saving ¥12K/day in GPU costs.
Dynamic Adaptive Batch Scheduling Engine
Maximize Compute Utilization
Monitors real-time frontend concurrency, model load, and compute-node idle capacity. Splits into small batches during low-traffic periods to reduce latency, and auto-aggregates similar requests into large batches during peaks to maximize GPU utilization. Works with the Scheduler for intelligent distributed compute dispatch and cross-node load balancing. The core underlying technology that reduces overall compute cost by 20–40% versus industry baseline.
- —Traffic-aware intelligence: monitors real-time frontend concurrency, model load, and node idle capacity
- —Adaptive batch grouping: small batches in low-traffic for lower latency, large-batch aggregation in peaks
- —Integrated Scheduler: intelligent distributed compute dispatch with cross-node load balancing
Application scenarioHigh-concurrency APIs and bulk text generation. A content platform scaled peak QPS from 800 to 3,200 with a 52% per-token cost reduction.
OpenAI Full-Compatible Zero-Migration Adapter
Zero Migration Cost
Fully compatible with the OpenAI standard API protocol, request/response data formats, and parameter system. The transparent translation middleware lets upper-layer business systems switch seamlessly to the TokensChain compute cluster without changing a single line of code. Compatible with mainstream LLM clients, development frameworks, and quantitative platforms. Zero adaptation cost and zero business-interruption risk when switching providers.
- —Fully compatible with OpenAI standard API protocol, request/response formats, and parameter system
- —Transparent translation middleware: switch seamlessly to TokensChain without a single line of code changed
- —Compatible with mainstream LLM clients, dev frameworks, and quantitative platforms — seamless migration for existing systems
Application scenarioExisting OpenAI / Azure / Claude app migration. An AI writing tool integrated in 30 minutes with zero prompt-engineering changes and 100% business continuity.
Built-In Full-Chain Compliance Engine
Dual Review + Audit + Filing Integration
Bidirectional request/response review: input prompt security audit plus model-output risk interception at two layers. Full-chain operational audit logs immutably record every call time, caller, request content, compute consumption, and return result. The auto-compliance filing module satisfies data-retention regulatory requirements for cross-border AI compute, enterprise government affairs, and overseas sovereign projects. Dedicated compliance routing isolates sensitive business traffic into independent compliance compute channels, adaptable to multi-national data regulations.
- —Bidirectional request/response review: input prompt security audit plus model-output risk interception at two layers
- —Full-chain operational audit logs: immutably record every call to satisfy data-retention regulatory requirements
- —Auto-compliance filing + dedicated compliance routing: satisfies cross-border, government, and sovereign compute project data regulations
Application scenarioHeavily regulated finance, healthcare and government. A bank raised moderation pass rate from 94% to 99.5% and cut compliance-audit prep from 2 weeks to 2 hours.
Custom Compute Routing & Enterprise SLA
Custom SLA + Dedicated Routing
Multi-line dedicated compute routing allocates isolated compute nodes, cross-border low-latency dedicated lines, and local属地 compute clusters according to customer needs. Customizable SLA scheduling lets enterprises set their own response-latency thresholds, compute priority, automatic failover rules, and peak compute guarantee quotas. Distributed compute isolation technology achieves physical/logical isolation of compute pools between different enterprise clients, eliminating data cross-leakage and adapting to government-enterprise and sovereign compute project requirements.
- —Multi-line dedicated compute routing: isolated nodes, cross-border low-latency lines, and local属地 compute clusters
- —Customizable SLA scheduling: self-defined latency thresholds, compute priority, failover rules, and peak guarantee quotas
- —Distributed compute isolation: physical/logical separation eliminates data cross-leakage for government and sovereign compute projects
Application scenarioFinancial-grade DR and global rollout. A brokerage achieved 99.99% availability with <50ms cross-cloud switchover and zero downtime for the year.
