01
TrainCustom weights, measured before they are hosted.
AI research · geometry & physics
actVite trains custom models and hosts them locally. Private embeddings, vector databases, and RAG stay on your side of the line. Language models and decision models go into electronics, and we 3D-print the instrument around them.
Apparatus 01 — move across the solid. Edge length fixed at 2.
01
TrainCustom weights, measured before they are hosted.
02
HostLocal models, private embeddings, vector DB, RAG.
03
BuildElectronics, decision models, and printed form.
Research programs
01
Train
Training runs against your data, your loss, and the things the model is not allowed to invent. You leave with weights, an evaluation record, and adapters when a full retrain is the wrong tool.
02
Host
The weights run on hardware you control. Prompts and completions stay inside that boundary. No shared tenancy, no silent offsite log of the work.
03
Index
Embedding models and the vector store, hosted privately. The index is yours. Documents, chunks, and nearest neighbors do not sit in someone else’s database.
04
Retrieve
Retrieval wired only to that private index. Answers are constrained to the corpus you own, with the passages kept available for inspection.
05
Silicon
Language models and decision models designed into the circuit. Sensors, power, latency, and the act the board is allowed to take are part of the objective.
06
Form
Housings, fixtures, and instrument bodies printed for that board and that model. The enclosure is part of the device, not a box chosen afterward.
What we measure
A model that only wins on a dataset is unfinished. The bench includes the body the weights will live in, and the boundary they are not allowed to cross.
Where the weights, embeddings, and vector store are allowed to live.
Private RAG only over the index you own, with passages you can inspect.
Language and decision models measured on the electronics that will ship.
Printed housings and fixtures that fit the board, not a generic shell.
Method
What is fixed: data, latency, power, privacy boundary, and what a wrong act costs.
Custom weights, then local hosting. Embeddings and the vector database stay private.
Private RAG for language. Decision models on the board when the output is an action.
If it needs a physical form, we print the instrument around the electronics.
Brief
Name the constraint: where the weights may live, what retrieval may see, what the board may do, and what has to be printed. Sending a brief opens your mail application. Nothing is stored on this site.