A precise product definition for technical buyers. Bounded state routing, not ordinary filters, RAG, summarization, or pruning.
TeleMemetry is a Bounded State Engine for real-time telemetry systems.
Engine: TeleMemetry is a core state-routing system, not a prompt pattern or ordinary retrieval wrapper.
Bounded: It enforces a fixed mathematical boundary between growing archive state and model context. At 1M records or 100M records, the evidence packet remains bounded.
State: TeleMemetry stores, indexes, and routes operational state. It does not reason over records, synthesize them, or make decisions from them. That is the model's job.
Real-time telemetry: The system is designed for high-frequency, continuous operational data streams · sensor readings, vehicle state, event logs, instrument readings · not document stores or conversation history.
What TeleMemetry is
What TeleMemetry is not
TeleMemetry sits between the telemetry source and the model. The telemetry stream ingests into TeleMemetry's archive. When the model receives a query requiring operational history, TeleMemetry routes the relevant state through a fixed boundary and passes the bounded evidence packet to the model as context. The model reasons over the packet and returns an answer. TeleMemetry does not touch the model's reasoning step.
Each ingested record is stored with a deterministic identity derived from its content. Retrieval returns the exact stored record, not a reconstruction. This means the source of any answer can be traced back to the specific ingested record that produced it.
This is not a security product feature. It is a property of the storage and retrieval architecture. It happens to produce properties that are useful in audited environments: the evidence chain is intact by construction, not by policy enforcement.
TeleMemetry exposes a retrieval interface: given a query and a context token budget, return the bounded evidence packet. The calling system passes the packet to any LLM. There is no proprietary model, no vendor lock-in on inference, and no requirement to change the existing model pipeline beyond inserting the retrieval call.
For integration inquiries and access to the engine image, contact AIFSOfficial@proton.me.