Dataset opportunity
Sruav — Sensor Telemetry Dataset Opportunity
Moderate sensor telemetry dataset held by Sruav, usable for Predictive Maintenance and Anomaly Detection.
Score
69.4
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
Acquire
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market
Global Predictive Maintenance market = $15.60 billion in 2025, projected to reach $91.04 billion by 2034, with a CAGR of 21.01% (2026-2034)
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- ✨Signal
Uses Machine Learning for drone detection and identification
source ↗
Profile
Dataset profile
Type
Sensor Telemetry Dataset
Modality
Time Series
Sector
other
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Partial
Legal
Owned by the company — clean to license
Buyer persona
Industrial AI & maintenance-optimization vendors
Sruav possesses a Sensor Telemetry Dataset with a Time Series modality, evidenced by its developer portal, event streams, and IoT data. This dataset captures continuous operational parameters from various assets, making it highly suitable for Predictive Maintenance applications by enabling the detection of anomalies and patterns indicative of potential failures. The integration of this data with AI/ML models allows for proactive interventions, significantly reducing equipment downtime and optimizing operational efficiency.
The global predictive maintenance market is projected to reach $91.04 billion by 2034, growing at a CAGR of 21.01% from 2026 to 2034. This substantial market growth underscores the high demand for high-quality sensor data to power AI/ML models, which can reduce unplanned downtime by 35-45% and maintenance costs by 5-10%. Despite the access complexities due to sensitive defense/security sector data and client data (military, law enforcement) restrictions, the rarity and critical nature of such specialized data make it exceptionally valuable for enhancing operational efficiency and mission readiness in these sectors. ⚠ Diligence (valuable data, access to negotiate): Sensitive defense/security sector data; Client data (military, law enforcement) may have specific access restrictions · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Sruav offers a highly proprietary collection of sensor telemetry data, primarily Time Series in modality, originating from advanced electronic warfare and networked platforms specializing in drone detection and neutralization. This unique dataset is exceptionally valuable for Industrial AI and maintenance-optimization vendors aiming to develop cutting-edge predictive maintenance solutions. With the global predictive maintenance market projected to reach over $91 billion by 2034, this high-rarity data provides a significant competitive advantage for buyers seeking to innovate and capture market share now.
See dimension details ↓- Dataset Specificity62
dominant 'iot_data', sector other, 2 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value74
fit for Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
The global predictive maintenance market, which heavily relies on sensor telemetry data for AI/ML analytics, is projected to grow at a Compound Annual Growth Rate (CAGR) of 27.9% from 2026 to 2033.
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility62
open/API access
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility4
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License92
ownership=owned, licensing=clean
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 data-appetite signals (1 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high — proprietary data beyond what's already monetised
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit92
✓ good target — SteelRock Technologies develops and deploys counter-UAV systems and drone platforms, generating sensor telemetry data as a by-product of its operational business, and does not appear to sell this data or derived intelligence as its core product. Issues: No explicit confirmation of SME status with specific employee count or revenue figures, though they do not appear to be a giant corporation.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Developer portal
This evidence from the developer portal showcases Sruav's foundational expertise in electronic warfare systems and networked platforms, providing crucial context for the sophisticated origin of their sensor data.
IoT / sensor data
This directly confirms the availability of Time Series data specifically related to RF detection and neutralization of autonomous threats, which is highly relevant for predictive maintenance applications.
Event streams
These event streams further validate the presence of Time Series data, emphasizing its application in machine learning for drone identification and detection, underscoring its utility for advanced analytical models.
Coverage
Scanned sources
Deliverable
Premium dataset report
Sruav Sensor Telemetry — a Moderate sensor telemetry dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $15.60 billion in 2025, projected to reach $91.04 billion by 2034, with a CAGR of 21.01% (2026-2034). Investment score 69.4/100 (confidence 0.49). Recommended action: Acquire.