Thousands of machines generating sensor data continuously. Storing all of it is expensive. The IP inside it is at risk.
RNDA encodes sensor streams and discards the raw telemetry. Predictive maintenance intelligence stays. Storage costs and IP exposure go.
Request an Industrial POC →The Problem
Manufacturing facilities generate continuous sensor streams from thousands of machines. Storing raw telemetry is expensive and creates IP exposure. Yet pattern detection for predictive maintenance requires historical comparison.
How RNDA Solves It
Perfect equipment discrimination — 1.00 gap
Proven on real CNC milling, water pump, and semiconductor manufacturing sensor data. Equipment type discrimination is perfect. Each machine has a unique behavioral signature.
Supply chain anomaly detection at 991x compression
Real industrial IoT sensor streams — temperature, pressure, flow rate — encoded at 991x compression. Discrimination gap 1.027. Anomalies surface via similarity search against normal operating signatures.
Predictive maintenance without raw data stores
Failure precursors are behavioral patterns. RNDA encodes the pattern and discards the raw sensor stream. Query against historical failure signatures to predict upcoming failures.
IP protection for proprietary processes
Manufacturing process signatures carry operational intelligence without carrying the raw process data. Proprietary process parameters never exist in recoverable form.
How RNDA Applies
Storage Elimination
Sensor telemetry, vibration logs, and environmental readings compressed 991x — cutting multi-petabyte IoT data lakes down to manageable size. A 1,000 TB manufacturer saves ~$275K/year, making indefinite retention of full sensor history economically viable for the first time.
Privacy Protection
Proprietary manufacturing process parameters, supplier contracts, and logistics routes are encoded into binary representations that cannot be reverse-engineered. Manufacturing process signatures carry operational intelligence without carrying the raw process data.
Compliance Management
ISO, OSHA, and ESG compliance records stored compressed yet audit-ready, with no data loss across the retention window. Regulatory audit requests answered from compressed archives without staging raw sensor volumes — compliance without infrastructure overhead.
Intelligent Retrieval
Maintenance anomalies and quality defect patterns retrieved semantically across years of compressed sensor history in milliseconds. Proven on real CNC milling, water pump, and semiconductor manufacturing sensors — perfect equipment discrimination, 1.00 gap. Failure precursors surface before failure occurs.
Collaborative Intelligence
Compressed supply chain datasets shared with logistics partners and Tier 1 suppliers without exposing raw operational data. Manufacturing intelligence crosses organizational boundaries; proprietary process parameters do not.
Storage Impact
Industry stat: Global IoT data projected at nearly 80 zettabytes by 2025; large manufacturers generate 500–1,500 TB/year of raw sensor telemetry from factory floor operations (ARO)
1,000 TB × 20% × $276/TB ÷ 991x compression (IoT sensors)
1,000 TB manufacturer saves ~$275K/year — 991x compression on real industrial IoT sensor streams, 1.03 discrimination gap
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: Real CNC/pump/semiconductor sensors + NAB IoT data
What Becomes Possible
"A CNC machine streams sensor data continuously. Each reading is encoded and the raw stream discarded. When a signature starts clustering near historical failure signatures, maintenance is triggered — before the failure occurs. No raw sensor archive exists."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
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