…How DataSapien Bridges the Gap 

While on-device LLMs are powerful, they don’t yet enable dependable, agentic, and private personal AI. 

There’s a growing wave of interest in AI agents – tools that don’t just respond, but act autonomously on your behalf. From managing health to helping you eat better or shop smarter, agentic intelligence promises a more useful, personalised, and frictionless experience. On-device language models are seen as a key part of this shift, and for good reason. 

These compact models are a breakthrough – not only for privacy, but for enabling truly personal AI. Running directly on a smartphone or edge device, they: 

• Use local compute, reducing server load, bottlenecks and running costs  

• Operate with low or no latency, ideal for time-sensitive decisions 

• Function offline, with no dependency on Wi-Fi or cellular signal 

• Keep all computation on-device, protecting data from exposure, hacks or misuse 

This makes them uniquely suited to power personal agents. But there’s a catch — today’s on-device LLMs, as impressive as they are, aren’t agentic AI. Not yet. 

Here’s why: 

• They produce freeform text, not structured outputs needed to trigger actions 

• They rely on user prompts — they can’t observe or act on their own 

• They lack memory, context awareness, and goal continuity 

• They are not multimodal — they can’t reason across images, voice, and video inputs on-device 

In other words, on-device LLMs are powerful assistants. But they’re passive — not autonomous. 

DataSapien: The Intelligence Stack That Bridges the Gap 

This is where DataSapien comes in. While the industry pushes to make on-device LLMs more interactive, contextual, and agentic, DataSapien has already built the supporting infrastructure that delivers those capabilities – today. 

At its core, the DataSapien SDK provides: 

• An engine to collect and store personal data to provide context for on-edge intelligence (including LLMs) 

• Access and action on a diverse array of personal data (e.g. health, habits, preferences) in real-time 

• A rules engine that can monitor personal data in real time 

• Classical machine learning models for pattern recognition, prediction, recommendation and segmentation 

• A no-code flow orchestration platform for building goal-driven, data-aware journeys 

• Leverage of on-device LLMs for language generation or natural interaction 

• Integration with private cloud LLMS for heavy lifting and multi-modal capabilities 

Crucially, the rules and ML models can initiate agentic AI loops. For example, a rule can detect a sudden drop in activity, or a deviation from a dietary goal, and proactively trigger a journey or suggestion. The LLM can then step in to personalise the message or assist with a response – but it’s the orchestrated intelligence stack that drives the autonomy: The brand designs and controls the experience, while the individual controls the data and AI. 

From Passive to Proactive: The Path to Private Personal AI 

With DataSapien, it’s no longer just a model running on a phone – it’s a fully orchestrated, privacy-preserving personal AI system. One that: 

• Understands context 

• Acts on your behalf 

• Learns from your data (only when you allow it) 

• Respects your boundaries 

As on-device models continue to evolve, DataSapien is already delivering the agentic capability they aim for – bridging the gap between potential and reality. The future of personal AI isn’t just local. It’s intelligent, dependable, and decisively yours.