An AI Database That Works Identically On-Device

The Era of On-Device Computing and SQLite

When mobile and embedded applications began spreading in earnest, there weren't many options for reliably storing and managing data in on-device environments. At that time, SQLite was a highly practical solution. Its ability to be embedded as a library within applications without requiring a separate server process, combined with single-file-based data management, made it well-suited for device environments. Thanks to these characteristics, SQLite became widely adopted across mobile and embedded applications.

Changing Requirements as AI Moves On-Device

The changes AI is experiencing today are fundamentally similar. AI no longer operates solely on servers—it's increasingly moving to on-device environments including mobile devices, desktop applications, industrial equipment, and medical/bio equipment. However, today's on-device AI demands far more complex requirements than simply storing data locally. It must perform inference locally while maintaining persistent context and making meaningful decisions based on the diverse data that exists within the device.

On-Device AI Presumes State

The core of on-device AI is that it has state. Per-user usage history, documents and logs stored within the device, previous conversation context, and vector data embedding all of these directly impact AI quality. Such data cannot be easily managed with simple files or memory caches. As semantic search, conditional filtering, sorting, and updates repeat, local data also requires database-level management. At this point, on-device AI naturally becomes a system that presumes an on-device database.

Limitations of Existing Database Stacks

The problem is that existing database stacks were not designed with these requirements in mind. Until now, AI services have used architectures that combine OLTP databases, OLAP systems for analytics, caches, full-text search engines, and vector databases based on server environments. This structure works on servers, but it's impractical to transfer directly to on-device environments. As a result, many end up storing only metadata in SQLite, managing vectors through separate libraries or files, and operating devices and servers on different architectures with search and transactions separated. This approach works initially, but quickly reaches its limits in terms of data consistency and system complexity as AI capabilities expand.

The Core Problem Is DB Architecture, Not AI

The difficulty of on-device AI isn't just about model performance or inference speed. The fundamental problem lies in database architecture. AI simultaneously requires transaction processing, local analytics, text and structured search, vector-based semantic search, and fast cache access. The moment these workloads are split across different storage systems, on-device environments become structurally unstable. Just as SQLite was naturally chosen for on-device computing in the past, on-device AI also requires a database designed as a unified structure from the start.

Cognica's Approach

Cognica approaches this problem not by adding features for on-device use, but by addressing it as a database architecture problem itself. Cognica provides transaction processing, analytics, full-text search, vector search, and caching on a single data model and a single execution engine. The important point is that this structure isn't just possible on servers—it works identically on-device as well. The data model doesn't change, the query interface doesn't change, and the semantics of search don't vary based on the execution environment.

The Difference Between Supporting On-Device and Working Identically On-Device

Many databases claim to support on-device operation. However, in reality, development environments often diverge due to feature limitations, lack of index support, or different behavior from servers. This ultimately forces AI logic and data logic to be split by environment, compromising service quality consistency. What Cognica aims for is a structure where the data architecture doesn't change even when the deployment location changes. Whether running on-device, at the edge, on servers, or in the cloud, the data structure that AI sees should be identical.

On-Device AI and Server AI Are One Flow

Real-world AI services are evolving into hybrid structures where inference and immediate responses are performed on devices, personalization context is maintained locally, and aggregation and training are processed on servers. Search and RAG are used simultaneously across both domains. When databases are separated at this point, logic diverges, search results differ, and AI quality becomes inconsistent. Cognica aims to eliminate these boundaries at the database level.

Conclusion

The reason SQLite became widely used in on-device computing environments is that it was a database that naturally fit those environments. What on-device AI requires today is fundamentally no different. To properly implement on-device AI, state management and semantic search must be possible even in local environments, which requires a database that fully supports on-device AI. Cognica is less a database newly created for AI, and more one that emerged because AI rendered the previous fragmented database combinations no longer viable. And this database works identically, whether on-device or on servers.