Dataset opportunity
Quicargo — Mobility & Geospatial Dataset Opportunity
Large mobility & geospatial dataset held by Quicargo, usable for Geo AI and Routing & Forecasting.
Score
75.4
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
79%
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 Geospatial Analytics Artificial Intelligence market = USD 47.76 billion in 2024, CAGR 25.71% (2025-2035)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-05
Criminals target freight with fake IDs, spoofed emails and stolen identities
freightwaves.com ↗ - 📰press2026-06-05
Delivery reliability trumps speed, Macy’s and Ulta execs say
supplychaindive.com ↗ - 📰press2026-06-05
Black Marker, Magnetic Signs, and Peeling Decals: Here Is What 49 CFR 390.21 Actually Requires.
freightwaves.com ↗ - 📰press2026-06-04
A Driver’s Paper Logs Said He Was in One Place. A Roadside Camera Network Said Otherwise. Welcome to the New Era of Trucking Enforcement.
freightwaves.com ↗ - 📰press2026-06-04
Trucking is driving double-digit growth for this rail freight category
freightwaves.com ↗
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 & Geospatial Dataset
Modality
Tabular
Sector
mobility
Volume
Large
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Aggregated / third-party — GDPR-sensitive (PII review)
Buyer persona
Geospatial-AI & mobility-analytics teams
Quicargo holds a Mobility & Geospatial Dataset in a Tabular modality, evidenced by API access, significant data volume, geo-data, industrial data, IoT data, a knowledge base, and transaction data. This rich combination provides granular insights into real-world movement patterns and logistics operations, making it highly valuable for Geo AI applications that require precise location-based intelligence for analysis, prediction, and optimization.
The market for such data is experiencing rapid expansion, with the Geospatial Analytics Artificial Intelligence market alone valued at USD 47.76 billion in 2024 and projected to grow at a CAGR of 25.71% from 2025 to 2035. This high growth is driven by increasing demand from sectors like transportation and logistics for real-time location intelligence and predictive analytics to optimize operations and enhance decision-making. Despite complexities such as being a subsidiary of GVT, data originating from third-party carriers, and the presence of GDPR-sensitive data, the valuable insights derived from this rare and comprehensive dataset for Geo AI use cases, particularly in route optimization and demand forecasting, justify the negotiation of access. ⚠ Diligence (valuable data, access to negotiate): Subsidiary of GVT; Data partially originates from third-party carriers; GDPR-sensitive data on users/employees · corporate: acquired of GVT.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Quicargo possesses a highly proprietary and extensive mobility and geospatial dataset, offering unparalleled insights into European road freight logistics. This rich collection of real-time tracking and historical transaction data, sourced from over 6,000 trucks and 270 carriers, directly addresses the critical needs of Geospatial-AI and mobility-analytics teams. With the global Geospatial Analytics AI market projected for substantial growth, this dataset provides a unique foundation for developing advanced solutions in logistics optimization, predictive routing, and sustainable supply chain management.
See dimension details ↓- Dataset Specificity100
dominant 'geo_data', sector mobility, 4 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity94
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume98
8 evidence hits, explicit data-volume mention
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 Value94
fit for Geo AI
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand95
The global artificial intelligence (AI) in mobility market, which leverages geospatial datasets for Geo AI, is projected to grow at a compound annual growth rate (CAGR) of 44.6% from 2026 to 2035, indicating extremely high buyer demand.
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
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 Feasibility0
medium difficulty, acquired of GVT
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength100
7 evidence types, 8 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License10
ownership=aggregated, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence45
acquired of GVT
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 Audit100
✓ good target — Quicargo is a good target as it is a contactable SME operating a digital freight forwarding business that generates valuable logistics data as a byproduct, and explicitly states it does not sell this data as its core offering.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Geospatial data
This demonstrates Quicargo's extensive operational footprint across European countries, providing a broad geographical scope for the underlying mobility data, essential for geospatial AI and regional logistics analysis.
API access
This evidence confirms Quicargo's robust API integration capabilities, indicating a structured and accessible data environment for automated freight management, highly valuable for AI systems requiring programmatic data access.
Knowledge base / docs
This refers to Quicargo's textual knowledge base, offering contextual information on logistics operations, terms, and support, which can enrich understanding or train NLP models for domain-specific insights.
IoT / sensor data
This highlights the collection of real-time tracking data on shipment location, condition, and ETA via Quicargo's Track & Trace system, providing critical time-series data for predictive analytics and dynamic logistics optimization.
Transaction data
This confirms the availability of historical transport order data, including dispatch, transit, and delivery details, crucial for performance analysis, route optimization, and identifying long-term operational patterns.
Industrial data
This outlines Quicargo's core mission to optimize road transport by filling empty trucks, implying data on operational efficiency and resource utilization, valuable for AI models focused on sustainability and capacity management.
Data-volume signal
This quantifies Quicargo's significant operational scale, connecting 3,000+ businesses to over 6,000 trucks from 270 carriers, underscoring the breadth and depth of the proprietary dataset for large-scale AI applications.
Coverage
Scanned sources
Deliverable
Premium dataset report
Quicargo Mobility & Geospatial — a Large mobility & geospatial dataset (Tabular modality) in the mobility domain. Primary AI use-case: Geo AI. Market signal: Global Geospatial Analytics Artificial Intelligence market = USD 47.76 billion in 2024, CAGR 25.71% (2025-2035). Investment score 75.4/100 (confidence 0.79). Recommended action: Data Sharing Agreement.