Meet ERAIS
Bring language, vision, audio and memory together.
We’re developing ERAIS so a team can build on skills a model has already learned. We test how those parts work together, what they remember and what they cost to run.
Efficient Routed Adaptive Inference System
How it works
Each model has a defined path through the available components: what runs, what it remembers and how much processing it uses.
Train a capability inside ERAIS or import a supported component through Fracture. Define its inputs, outputs, state and tests.
Route an input through the relevant experts. Record which components execute and what work they perform.
Carry memory between interactions and adjust processing under the model’s schedule. Test how that changes quality and operating cost.
We want simpler upgrades, leaving more of a team’s budget for new capabilities.
Inside the architectureTraining and conversion
Native training
Native language experiments learn from raw bytes. Vision and audio have their own training paths and evaluations. When the service is available, you can try a trained language checkpoint in Story Lab.
Try story generationFracture
Fracture analyses a supported model’s learned components and partitions them into experts that ERAIS can run. We’ve recorded speech and game-control conversions. Every new model still needs its own quality, resource-use and deployment checks.
See how conversion worksHow the pieces fit
ERAIS is the foundation. Fracture is a path for bringing knowledge in. The experiences below let you explore different parts of that work.
Specialist capabilities · Routing · Adaptive effort
Shared-service research
Unified World, the native API selection and the Discord pilot use the shared ERAIS service.
The recorded cutover includes a public native completion and an installed Discord adapter response. Live availability, signed Discord delivery, assistant quality, sparse savings and paid-customer readiness need their own checks.
Native language
Explore a separately trained native language checkpoint through short story generation.
Embodied research
Watch the experimental player and inspect its reported model activity when a session is being shared.
Working systems
The current programme includes native language training, converted speech and game-control components, complete male and female CNS source models, and live embodied experiments.
The latest engineering review records source identities, exact-state checks and measured CPU improvements. The cyborg Doom controller now uses a complete native neuronal expert alongside its learned executive.
We’re working toward stronger task performance and dependable deployment as these capabilities are connected.
The wider ambition
Reusable capability and selective computation could put more ambitious AI work within reach of researchers, small teams and organisations working with practical hardware budgets.