Dataset opportunity
Clevon — Mobility Telemetry Dataset Opportunity
Moderate mobility telemetry dataset held by Clevon, usable for Predictive Maintenance and Anomaly Detection.
Score
70.5
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
Data Sharing Agreement
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 for vehicles market was estimated at USD 4.66 billion in 2024, with a projected CAGR of 17.5% (2025-2034). [1]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-12
AI in warehousing: Akash Gupta’s vision for the future
therobotreport.com ↗ - 📰press2026-06-12
Gatik to bring autonomous freight to PepsiCo’s North American supply chain
therobotreport.com ↗ - 📰press2026-06-12
Canada Post to end door-to-door delivery for 620K addresses by 2027
freightwaves.com ↗ - 📰press2026-06-12
b2wise fin prêt pour bousculer le marché des APS
supplychainmagazine.fr ↗ - 📰press2026-06-12
Stef s’agrandit à Reichstett pour les glaces de Mars Wrigley
supplychainmagazine.fr ↗
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.
Profile
Dataset profile
Type
Mobility Telemetry Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — GDPR-sensitive (PII review)
Buyer persona
Industrial AI & maintenance-optimization vendors
Clevon possesses a valuable Mobility Telemetry Dataset from its CLEVON 1 autonomous delivery vehicles. This dataset primarily consists of Time Series data, including `iot_data`, `event_streams`, and `image_collection`, which captures detailed operational history from proprietary vehicle sensors. This rich, high-fidelity data is exceptionally well-suited for developing and training Predictive Maintenance AI models to forecast component failures and optimize vehicle uptime.
The data serves the global predictive maintenance for vehicles market, which was estimated at USD 4.66 billion in 2024 and is projected to grow at a 17.5% CAGR. [1] Despite access complexities—such as PII in images requiring anonymization, evolving data governance from a recent acquisition, and the data's tie-in to proprietary CLEVON 1 hardware—the dataset's rarity and direct real-world applicability make it a premium asset. For AI buyers, it offers a unique opportunity to leverage a specialized IoT data source for a significant competitive advantage in autonomous logistics. ⚠ Diligence (valuable data, access to negotiate): Data contains PII (faces, license plates) from public road cameras requiring anonymization; Recent acquisition by US-based indiGOtech may centralize data governance; Operational data is tied to proprietary hardware (CLEVON 1) sensors · corporate: subsidiary of indiGOtech.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Clevon owns a rare, proprietary dataset of longitudinal telemetry from a commercially operated fleet of autonomous electric vehicles. Generated over more than three years of real-world public road service, this data is a critical asset for industrial AI vendors building next-generation predictive maintenance solutions. In a vehicle predictive maintenance market projected to grow at over 17% annually, this unique operational data offers a powerful competitive advantage for developing and validating more accurate failure-prediction models.
See dimension details ↓- Dataset Specificity90
dominant 'iot_data', sector mobility, 3 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
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 Value84
fit for Predictive Maintenance
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand88
The global automotive predictive maintenance market, which relies heavily on mobility telemetry data, is projected to grow from USD 22 billion in 2023 to USD 100 billion by 2032, reflecting a very strong compound annual growth rate (CAGR) o
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility15
medium difficulty, subsidiary of indiGOtech
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 License62
ownership=owned, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
subsidiary of indiGOtech
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation56
2 data-appetite signals (2 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high, 5 recent external signals — 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 Audit75
⚠ review — Clevon's core business is developing and providing autonomous vehicle technology and services, which was recently acquired; it is not a holder of dormant data but a vendor of AI/robotics solutions. Issues: Company's core product is AI/robotics technology, not a byproduct of another business.; The company was acquired by a US tech firm, indiGO Technologies, and will now integrate its technology into new electric vehicles for delivery and ride-sharing,; The company's website explicitly
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
The dataset contains over three years of continuous time-series telemetry from vehicles in commercial service on public roads, providing a rich historical basis for training failure-prediction algorithms.
Image collection
This telemetry is sourced from a fleet of modern electric vehicles whose systems have been validated in real-world applications, ensuring the data's relevance for today's maintenance challenges.
Event streams
The data originates from a supervised fleet of vehicles operating concurrently, offering the scale necessary to train robust AI models that can generalize across multiple assets and operational conditions.
Coverage
Scanned sources
Deliverable
Premium dataset report
Clevon Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global predictive maintenance for vehicles market was estimated at USD 4.66 billion in 2024, with a projected CAGR of 17.5% (2025-2034). [1]. Investment score 70.5/100 (confidence 0.49). Recommended action: Data Sharing Agreement.