Cognitive Database
GPU-Native Cognitive Database
A New‑generation Data Foundation for AI Agents
Transwarp Cognitive Database is a high‑performance cognitive data foundation for AI Agents. With a GPU‑native architecture, it unifies data analytics, knowledge retrieval, long‑term memory, and trusted context provisioning. It improves performance and cost‑efficiency, reduces system fragmentation, lowers the adoption barrier, and has been validated in complex scenarios to help enterprises build truly deployable, continuously evolving intelligent applications faster.
Core Values
Performance Leadership
supports real‑time analytics and intelligent decision‑making on massive data.Compared to ClickHouse: TPC‑DS SF1000 (1TB) – 456× faster; TPC‑H SF1000 (1TB) – 148× faster.TPC‑DS and TPC‑H world‑record holders.
Better Cost Efficiency
Stronger performance for the same workload, lower TCO.On TPC‑DS, compared to Snowflake: performance and cost performance ratio >20× better. Compared to Databricks: >58× better.Order‑of‑magnitude performance gains significantly lower per‑unit compute cost, reducing TCO.
Integrated Architecture
No need to stitch multiple systems together.Unifies relational, vector, graph, full‑text, document, and other models in one platform – covering data analytics, knowledge retrieval, context provisioning, and memory management – reducing system fragmentation, data movement, and operational complexity.
Memory Leadership
Makes AI Agents more business‑savvy over time.With hybrid retrieval, long‑term memory, and trusted context management, it improves continuity in multi‑turn interactions, complex reasoning, and task execution. Memory accuracy exceeds 90%.BEAM 500K and LongMemEval‑S benchmarks: dual champion.
Ease of Use
Natural‑language‑driven analytics and knowledge access.Supports natural language, SQL, Python, and API interactions. Business users can directly initiate data analysis, knowledge retrieval, and interactive queries, lowering the data usage barrier and accelerating the path from business questions to data insights.
Validated Across Scenarios
proven in high‑value business use cases.Already validated in complex scenarios such as intelligent risk control, smart investment research, credit analysis, and risk attribution – translating underlying performance advantages into business value: faster analytics, earlier risk detection, and more efficient decision support.
Why Rebuild the Database with GPUs?
Why Rebuild Databases for AI Agents?
High‑frequency Calls Demand Higher Performance
Support hundreds of Agents with high concurrency and millions of calls per single task.
Multi‑source Understanding Requires Multi‑model Retrieval
Unified management of relational, vector, graph, full‑text, document, and other data with hybrid retrieval.
Task Continuity Needs Long‑term Memory
Accumulate context, session state, and historical tasks to sustain continuous intelligence.
GPU‑Native Database for AI Agents
Redefining Data Infrastructure for the Intelligent Era
GPU‑Native Acceleration
PERFORMANCE
Built on a GPU‑native architecture, it accelerates all types of analytics, vector search, and graph analytics, delivering high‑throughput, low‑latency retrieval, blazing‑fast SQL analytics, semantic search, and machine learning – all on a single compute foundation with order‑of‑magnitude speedups.
Multi‑Model Knowledge Retrieval
MULTI-MODEL
Supports multimodal knowledge recognition, parsing, semantic extraction, and unified storage. Relational, vector, graph, time‑series, and other data models are uniformly stored, managed, and retrieved together. One foundation manages everything, replacing multiple stitched‑together systems.
Scalable Contextual Memory
MEMORY
Built on a self‑developed distributed file system with built‑in long‑term memory, session memory, and context management, it enables continuous accumulation of diverse memory types. GPU‑accelerated low‑latency retrieval and precise recall allow Agents to maintain understanding across sessions and become smarter over time.
Natural Language Data Analytics
NL2Data Analysis
Supports natural language interaction, as well as SQL, Python, and RESTful APIs. It automatically recognizes user intent and enables joint query and analysis across multi‑model data, lowering the barrier to data analysis and application development.
Four Typical Scenarios
AI Inference Acceleration
Rapid Agent responses rely on coordinated acceleration of model inference and data pipelines. By improving efficiency in data analytics, knowledge recall, and context building, Agents complete understanding, judgment, and execution faster.
Latency-Critical Decision Making
In businesses where latency directly impacts revenue and risk, real‑time data processing determines decision efficiency. Low‑latency analytics and real‑time computation help customers seize opportunities, control risks, and make critical business decisions faster.
Discovery-Driven Exploration
In scenarios where analysis cycles determine business agility, data analytics efficiency affects exploration speed and innovation pace. Accelerated multi‑source data analysis, semantic retrieval, and knowledge correlation help validate hypotheses, uncover insights, and drive innovation faster.
High-Complexity, High-Volume Scaling
In scenarios where data volume and query complexity grow continuously, system scalability defines business capacity. High‑performance computing and unified data management help customers support massive data, complex queries, and large‑scale intelligent applications with stable operation.
The Acceleration Value of GPU‑Native Is Visible
Accelerates XGBoost model training, machine learning analysis, and model validation. Supports risk identification, model iteration, and strategy optimization – shortening modeling cycles and improving risk strategy response efficiency.
● 10–20× XGBoost performance improvement
GPU-Native: Faster Response, Lower Cost for AI Agents
Performance gains from tens to hundreds of times
Compared to ClickHouse: TPC‑DS SF1000 (1TB) – 456× faster; TPC‑H SF1000 (1TB) – 148× faster.Compared to DuckDB: TPC‑DS SF1000 (1TB) – 106× faster; TPC‑H SF1000 (1TB) – 28.1× faster.
Stronger performance, better price
On TPC‑DS, compared to Snowflake: >20× better performance and price performance ratio. Compared to Databricks: >58× better.
Vector retrieval acceleration >200×
GPU‑native acceleration for vector index building and similarity search improves knowledge ingestion, semantic recall, and context building – supporting high‑concurrency, low‑latency RAG Q&A and AI Agent inference.
Unified Data & Knowledge for Trusted Business Understanding
Unified management of structured, unstructured, and multimodal data – combining relational, vector, graph, full‑text, document, and other models. Single retrieval, multi‑model joint search – efficiently building a complete and trustworthy business context for AI Agents, eliminating the need for multiple databases, vector stores, and retrieval systems.
Long-Term Memory: Smarter Agents, Deeper Business Insight
Enable AI Agents to deliver more continuous, personalized, and efficient intelligent services.
Transwarp, Shaping the Future Data World
