Any system where the operational archive grows without bound and full context replay is not viable. Concrete scenarios by domain.
TeleMemetry is appropriate when all three of these are true:
1. The system generates a continuous stream of operational records at high frequency.
2. The AI must answer factual questions about specific past records from that stream.
3. The archive grows large enough that passing the full history to the model context is not viable.
If your archive is small and stable, you do not need TeleMemetry. Use a context window.
Primary domains
AV systems generate high-frequency telemetry: vehicle state, sensor readings, object detections, route events. A 24-hour operational period at 10Hz produces ~864,000 records. At production fleet scale, per-vehicle archives grow continuously.
Benchmark validation: TeleMemetry's Bounded State Engine stores operational state as content-hashed evidence packets and retrieves bounded subsets for each probe without full-context replay. In the nuPlan AV benchmark at 2,000,000 telemetry records, this produced 100% verified factual retrieval recall and 0 integrity failures within the documented run scope. The dataset contains real nuPlan Mini telemetry replayed as a streaming feed.
Applicable queries: What was the vehicle's speed at timestamp T? What object was detected at position P? What was the heading at waypoint W?
Not applicable to: Real-time control decisions, path planning, sensor fusion, or production safety certification. TeleMemetry is a memory layer, not a driving component.
Industrial equipment generates continuous sensor streams: temperature, pressure, vibration, flow rate, cycle counts. A single production line running 24/7 produces millions of records per day. Maintenance AI, anomaly detection, and root cause analysis all require querying specific past states.
Applicable queries: What was the bearing temperature at time T before the fault? What was the pressure differential over the last 1,000 cycles? Which machine state preceded the anomaly?
Advantage: Archive depth is not a constraint. The same bounded query cost applies whether the archive contains 10,000 or 10,000,000 records.
Robots operating in physical environments accumulate operational history: joint positions, force readings, task completions, error events, environment observations. Long-running robots in warehouse or manufacturing settings need access to operational history for diagnostics and adaptive behavior.
Applicable queries: What was the gripper force on the last 50 pick attempts for item class X? At what point in the operational history did the first positioning error appear for station Y?
Edge AI systems operate with constrained compute and often cannot offload full context to cloud inference. Bounded retrieval is particularly valuable here: the evidence packet is small enough to run local inference on embedded hardware while maintaining access to the full operational history on local storage.
Power validation: A 1M-record run on RTX 4000 Ada averaged 12.53W GPU power (Low-Power Watch 001, 2026-07-06). This validates low-power sustained operation at benchmark scale.
AI agents operating continuously over days or weeks accumulate operational context that exceeds any practical context window. TeleMemetry provides bounded access to the agent's full operational history without requiring a growing context window or expensive summarization chains.
Applicable scenarios: Agents monitoring infrastructure, agents managing long-running workflows, agents operating physical systems over extended periods.
Distinction from conversation memory: TeleMemetry is designed for high-frequency operational records, not conversation logs. For conversational long-term memory, see the LongMemEval evaluation (in progress, not a primary TeleMemetry claim).
Flight operations, ATC, maritime, and rail systems generate continuous telemetry over long operational periods. Post-event analysis, anomaly investigation, and maintenance querying all require factual recall from specific past states in large archives.
Telemetry terminal benchmark: TeleMemetry has been demonstrated on live ADS-B aviation telemetry (OpenSky Network, 2,365 events, 239 aircraft, 2026-06-21) with 100% probe recall and 106 bounded tokens vs an estimated 49,894 full-replay tokens. This was a live demonstration run, not a deterministic benchmark; a deterministic replay version on OpenSky historical data is planned.