AI Runtime
Inference, AI data, and real-time voice — purpose-built from the ground up. No wrappers. No third-party frameworks. No external dependencies. The hardest part of AI infrastructure, built and operated as one system.
Inference Fabric
Purpose-built disaggregated inference architecture. Model execution, attention, scheduling, and sampling run directly on GPU hardware — no wrapped open-source engines, no Python in the hot path, no external libs. More throughput from the same hardware. Fewer things that break.
NVIDIA and AMD
Same codebase, same performance. Not locked to one vendor.
LoRA adapters
Hot-swap adapters per request from one base model.
Multi-model
Foundation models, fine-tuned models, and BYOM. Different agents in the same system can use different models.
5-tier KV cache
VRAM, host RAM, SSD, RDMA, and distributed across GPUs. More agents, longer conversations, graceful spill.
AI Datastore — one system, not six databases.
Object storage, knowledge graph, vector search, full-text search, agent sessions, and persistent memory — in one system, not six databases bolted together. S3-compatible. MCP-native. Agents access enterprise knowledge, build context, and remember across sessions — all from a single retrieval path.
Knowledge graph
Automatic entity extraction, relationship mapping, and traversal across documents, metadata, and structured records.
S3-compatible object storage
Drop-in replacement. Store, retrieve, and manage unstructured data at any scale.
Native MCP endpoint
Agents connect directly — no adapter layer, no middleware.
Vector and full-text search
Hybrid retrieval — semantic similarity and keyword search from the same system.
Agent sessions and persistent memory
Stateful context across multi-turn runs. Agents remember across conversations, weeks, months.
Erasure coding
Data durability without the replication overhead.
Speech Engine
STT and TTS running natively on the same GPU infrastructure as inference. Multiple model families for speech-to-text and text-to-speech. No third-party speech APIs. Audio stays on Origon infrastructure from capture to synthesis.
Connected to the voice engine over Origon's lightweight binary RPC protocol. Audio in to audio out stays on Origon infrastructure.
Real-Time Voice — Shortest path from audio in to audio out. Speech, inference, and transport on one stack. No third-party APIs.
Full-duplex audio
Simultaneous send and receive with no clipping or channel switching.
Natural interruption handling
Two-tier detection — subtle backchannel signals and full barge-in. The agent reacts naturally, not robotically.
Speculative transcription
Processing begins before the speaker finishes for faster response.
QUIC-native audio transport
MoQT delivers audio frames independently — no packet queuing.
End-to-end encryption
MLS protocol secures every audio stream from origin to destination.
Multi-server trunking
Voice sessions distributed across servers for horizontal scale.
Transport
Custom protocols purpose-built for the runtime. The entire stack communicates over a single protocol. Fewer processes, fewer failure modes, simpler operations.
- Single protocol across the stack
- Zero external coordination services
- Binary, not text-based
- Built on QUIC
Origon's binary RPC over QUIC. Purpose-built for inference and speech workloads. One stream per call. Lower overhead than HTTP or gRPC because there's no text parsing, no serialization framework, and no connection pooling.
IETF Media over QUIC Transport. Real-time audio over QUIC datagrams with no head-of-line blocking.
Kernel-bypass packet processing. Wire-speed at the application layer.