Orchestration-first. Model-agnostic. Execution-ready.
Before we dive into how the engine works, this is what you actually do with it. Prism AI brings desktop-grade multimodal capabilities to Android for the very first time. Every mode is optimized for a specific intent, powered by the local orchestrator.
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Prism AI functions as a kernel for agentic workflows—parsing intent, scheduling tasks, mounting tools, and executing in isolated environments.
Natural Language Decoding & Route Mapping
When a user prompt enters Prism AI, the Orchestrator analyzes intent, context depth, required modality, and safety boundaries in real-time.
Models are engines, but they need a chassis. The harness observes outputs, plans next steps, evaluates results against intent, retries on failure, and formats final delivery. It protects the user from raw model hallucination.
LLMs generate code. Prism AI executes it securely. We isolate every agent interaction within hardware-backed local microVMs or the user's own GitHub Codespaces allocation.
Local agents run code via Microdroid. A protected KVM enforces hardware-backed isolation between the host OS and the disposable guest VM.
Heavy build tasks spawn inside the user's own GitHub Codespaces allocation via native port-forwarding — no Prism-hosted container fleet. Compute and billing stay on the user's own GitHub account.
Guest environments boot from dm-verity verified, read-only root filesystems preventing persistent tampering.
Communication across the host-guest boundary occurs exclusively through constrained vsock protocols, safely piping AST diffs and stdout logs.
Prism AI routes tasks through an abstraction layer. The orchestration layer remains stable regardless of the model you plug in.
Prism AI operates under a Bring Your Own Credentials (BYOC) model and does not provide proprietary hosted AI models. The platform is designed primarily as an orchestration and execution environment where users utilize their own API credentials, providers, local runtimes, or self-hosted AI infrastructure.
Watch how PRISM dynamically loads specialized skills and connects to live MCP servers on demand to complete real-world tasks.
A skill is a folder of instructions the agent loads only when it's relevant — so context stays clean and behaviour stays predictable.
MCP connects the agent to live systems — real reads and real writes, not just text. Spawned on demand, disconnected when done.
Conversations aren't just strings of text. They are vectorized, embedded, and stored for deep contextual recall across entirely separate sessions.
High-performance volatile storage for active session states, fast-moving UI locks, and immediate recent-token access. Assures sub-millisecond data retrieval during fluid conversations.
Persistent semantic embeddings of your entire workflow history. Conversations are dynamically chunked, vectorized, and injected into context explicitly when relevant topics are breached.
Prism AI does not persist, compile, or relay credentials. Experience how our sandboxed containment matrix secures your orchestration layer below.
Tokens are encoded inside browser-isolated storage and only bind temporarily into high-memory execution stacks. Change state to test the local vault.
Test the secure API handshake to confirm direct peer-to-peer pipelines work without routing queries through our backend.
Before a request leaves the device for the model provider, on-device heuristic filters scrub Personally Identifiable Information (PII) or plaintext credential structures — scrubbing happens client-side, before the direct provider call, never via a Prism-hosted gateway. Check the sandbox below.
We are currently in Phase 2. Core application interface, UI components, fundamental tools tracking, and basic workflows are actively under development.
Designing system boundaries, setting up environments, and architecture blueprints.
Core application interface, fundamental tools tracking, and basic workflows.
Integrating Android Virtualization Framework, GitHub Codespaces execution tier, backend APIs, and core feature modules.
Gathering early user feedback, refining system performance, and fixing edge cases.
Full public release, open sourcing codebase, and enabling global availability.