dataplor
Global location intelligence platform providing accurate, reliable, and dynamic Points-of-Interest (POI) and mobility datasets.
Website: https://www.dataplor.com/
PUBLIC
| Company | dataplor |
| Tagline | Global location intelligence platform providing accurate, reliable, and dynamic Points-of-Interest (POI) and mobility datasets. |
| Headquarters | Boca Raton, United States |
| Founded | 2021 |
| Stage | Series B |
| Business Model | API / Developer Platform |
| Industry | Other (Location Intelligence) |
| Technology | AI / Machine Learning |
| Geography | Global / Remote-First |
| Growth Profile | Venture Scale |
| Founding Team | Solo Founder |
| Funding Label | Series B (total disclosed ~$31,100,000) |
Links
PUBLIC
- Website: https://www.dataplor.com/
- LinkedIn: https://www.linkedin.com/company/dataplor
- AWS Marketplace: https://aws.amazon.com/marketplace/seller-profile?id=seller-qewssgvnt2e2c
Executive Summary
PUBLIC
dataplor operates as a global location intelligence platform, providing accurate, dynamic Points-of-Interest (POI) and mobility datasets that serve as a critical input for enterprise decision-making across international markets [dataplor]. The company merits investor attention for its focus on building a defensible data asset outside the U.S., a region where established competitors have historically concentrated their efforts, positioning itself as a primary source for global coverage [F-Prime Capital].
Founded in 2021 by Geoffrey Michener, a serial entrepreneur with prior experience in local data, the company was built to address gaps in international POI coverage [F-Prime Capital]. Its core product is a database of hundreds of millions of locations, sold via API and marketplace listings, which it differentiates through a multi-layered verification process combining AI-driven analysis with human review across languages and cultures [dataplor].
The founding team brings relevant domain expertise, with Michener having previously founded a local data platform called Datafiniti, providing a foundation in the challenges of sourcing and structuring location-based information [F-Prime Capital]. The business is venture-backed, having raised a disclosed Series A and a recent $20.5 million Series B round led by F-Prime Capital, indicating institutional validation of its model and growth trajectory [Silicon Valley Journals, 2025].
Over the next 12-18 months, the key watchpoints will be the commercial traction of its newer mobility data product and the conversion of its stated global coverage into named enterprise customer logos, which remain absent from public disclosures. The company's ability to monetize its international data advantage against well-funded incumbents will define its next phase.
Data Accuracy: YELLOW -- Key funding details and coverage metrics are reported by multiple sources, but specific customer traction and detailed team background are not fully corroborated.
Taxonomy Snapshot
| Axis | Classification |
|---|---|
| Stage | Series B |
| Business Model | API / Developer Platform |
| Industry / Vertical | Other |
| Technology Type | AI / Machine Learning |
| Geography | Global / Remote-First |
| Growth Profile | Venture Scale |
| Founding Team | Solo Founder |
| Funding | Series B (total disclosed ~$31,100,000) |
Company Overview
PUBLIC
Geoffrey Michener, a serial entrepreneur with a background in local data and digital advertising, founded dataplor to address what he identified as a significant gap in global location intelligence [F-Prime Capital]. The company, officially incorporated as dataplor, Inc., is headquartered in Boca Raton, Florida, operating with a remote-first model that supports its global data collection mandate [LinkedIn]. Its founding premise centered on building a more accurate and comprehensive database of Points-of-Interest (POI) outside the United States, leveraging a combination of automated systems and human validation from the outset [dataplor].
The company's first major institutional milestone was a $10.6 million Series A financing round led by Spark Capital, which closed in 2020 [dataplor]. This capital was directed toward expanding its international coverage and refining its data verification processes. A subsequent $20.5 million Series B round, led by F-Prime Capital and announced in June 2025, signaled continued investor confidence in its global data quality wedge and provided fuel for product expansion, including the launch of a mobility data offering [Silicon Valley Journals, 2025] [dataplor].
Data Accuracy: YELLOW -- Core founding and funding facts are confirmed by company and investor sources, but some corporate history and co-founder details lack primary-source corroboration.
Product and Technology
MIXED
The core proposition is a global location intelligence platform that sells Points-of-Interest and mobility data through bulk datasets and APIs. Its primary wedge is coverage outside the United States, with the company claiming a database of "every public and commercial location in the world" [dataplor]. The most consistent public figure for this coverage is over 370 million locations across more than 250 countries and territories, as cited by an Esri partner listing [Esri Partner Solution]. The product suite is distributed via its own platform and third-party marketplaces like AWS, where it is listed as a seller of POI and mobility data products [AWS Marketplace].
Data quality is the stated technical differentiator. The company describes a "multifaceted approach to AI" that combines machine learning, deep image processing of satellite and street-level imagery, and AI call bots to gather and verify information [dataplor]. This automated pipeline is then supplemented by what the company calls "specially trained human validators" across cultures and languages to review data, aiming for accuracy in local context [dataplor]. The mobility product, launched more recently, provides foot traffic insights by combining this POI foundation with footfall counts [dataplor].
- Core Datasets. The platform provides structured POI attributes including business names, categories, addresses, contact information, and operating hours.
- Delivery Methods. Data is accessible via bulk CSV downloads, a REST API for integration into customer workflows, and listings on data marketplaces.
- Update Cadence. The company emphasizes dynamic, near-real-time updates to its datasets, though a specific refresh rate (e.g., daily, weekly) is not publicly quantified.
The technology stack is not detailed in public materials. Inferences from job postings and team backgrounds suggest a reliance on cloud infrastructure (AWS), Python for data processing, and machine learning frameworks for the AI/ML components (inferred from job postings).
Data Accuracy: YELLOW -- Core product claims are consistent across the company's site and partner listings, but specific technical performance metrics (e.g., accuracy rates, update latency) are not publicly verified.
Market Research
PUBLIC The market for location intelligence, once a niche of mapping and navigation, has become a critical layer of infrastructure for global enterprise planning, with demand now driven by the need to understand physical consumer behavior beyond the well-mapped United States.
Third-party sizing for the specific global POI data market is not publicly available in cited sources. However, analogous reports on the broader location intelligence and geospatial analytics market provide a useful frame. One industry analysis from 2023 placed the global geospatial analytics market at $78.2 billion, projecting growth to $186.3 billion by 2030 [Grand View Research, 2023]. This growth is anchored in enterprise digital transformation, where physical and digital data streams converge for site selection, supply chain logistics, and competitive analysis.
Demand is propelled by several identifiable tailwinds. The expansion of global consumer brands into emerging markets requires reliable, ground-truth data on commercial landscapes, a gap dataplor explicitly targets [Dealroom.co]. Concurrently, the rise of AI and machine learning models has increased the appetite for large, clean, structured datasets for training and inference, turning POI data from a reference tool into a core model input [F-Prime Capital]. The regulatory shift away from less privacy-conscious mobile device tracking has also created a vacuum, increasing the value proposition for aggregated, anonymized mobility insights derived from POI-centric models.
Key adjacent and substitute markets influence the landscape. The most direct substitute is in-house data collection by large corporations, though the cost and complexity of maintaining a global, updated database is prohibitive for all but the largest players. Adjacent markets include broader business intelligence platforms, which may integrate location data as a feature, and specialized vertical SaaS for retail or real estate, which often build or license their own POI layers. The competitive threat is less about a single monolithic alternative and more about the decision to build, buy a broad dataset, or purchase a vertically integrated solution.
Regulatory forces present a dual-edged dynamic. Stricter data privacy laws like GDPR and CCPA increase compliance overhead for data aggregators but also raise barriers to entry, favoring established players with compliant processes. Conversely, regulations concerning data sovereignty and cross-border data flows add complexity to operating a truly global platform, potentially requiring localized infrastructure or partnerships.
| Market Segment | Cited Size (2023) | Projected Size (2030) | Source |
|---|---|---|---|
| Geospatial Analytics (Analogous Market) | $78.2B | $186.3B | [Grand View Research, 2023] |
The projection for the broader analog market suggests a high-growth environment where specialized data providers can capture significant value. For dataplor, the key question is not total addressable market size but its ability to capture the specific segment of enterprises paying for premium, non-U.S. location data, a segment that is likely expanding but remains unquantified in public reports.
Data Accuracy: YELLOW -- Market sizing is drawn from an analogous, broader industry report. Tailwinds and regulatory analysis are inferred from cited industry commentary and lack direct quantification.
Competitive Landscape
MIXED
Dataplor operates in a crowded location intelligence market, positioning itself as the specialist for high-quality, global coverage outside the United States.
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| dataplor | Global POI & mobility data with emphasis on non-U.S. accuracy and human+AI validation. | Series B (~$31.1M total disclosed) | Multifaceted AI and human validation for global coverage; sold via API, bulk, and AWS Marketplace. | [dataplor] |
| SafeGraph | U.S.-focused POI and foot traffic data provider. | Acquired by Near in 2022. | Historically strong U.S. POI dataset with a large developer community; now part of a larger location data stack. | [Crunchbase] |
| Foursquare | Consumer app and enterprise location platform with Places API and analytics. | Venture-backed; last round $150M Series E in 2020. | Combines first-party user data from its apps with a curated POI database; strong brand recognition. | [Crunchbase] |
| GroundTruth | Location-based advertising and audience targeting platform. | Venture-backed; last round $40.5M in 2021. | Focus on mobile advertising and attribution, leveraging SDK-derived location data for marketing use cases. | [Crunchbase] |
| Cuebiq | Mobility and foot traffic analytics for retail and real estate. | Venture-backed; acquired by Advan in 2021. | Privacy-first mobility insights derived from opted-in mobile app data. | [Crunchbase] |
The competitive map segments into three primary groups. The first comprises incumbent data aggregators like Precisely (formerly Pitney Bowes), which offer broad geospatial datasets often bundled with address verification and demographics, targeting large, conservative enterprise IT departments. The second, and dataplor's most direct competitive set, includes specialist POI and mobility providers such as SafeGraph, Foursquare, and Cuebiq. These firms typically built their moats in North America or Western Europe, creating a clear opening for a player emphasizing emerging markets. The third group consists of adjacent substitutes, including mapping platforms like Google Maps Platform and HERE Technologies, which offer foundational geocoding and mapping services but whose POI data is often a feature rather than a standalone, deeply attributed product sold for analytical use cases.
Dataplor's current edge rests on two pillars: its claimed superior data quality for international markets and its hybrid validation methodology. While competitors rely heavily on algorithmic scraping or mobile SDK data, dataplor emphasizes a "multifaceted approach to AI combining deep image processing, AI call bots, machine learning, satellite imagery analysis, and human validators" [dataplor]. This labor-intensive process is designed to overcome the patchy and unreliable source data common outside the U.S. and Europe. The durability of this edge is tied to execution scalability. The process is capital-intensive and operationally complex; maintaining quality while expanding coverage to over 370 million locations [Esri Partner Solution] requires continuous investment in both technology and a distributed validation workforce. Its second advantage is a focused distribution strategy through the AWS Marketplace, which lowers friction for technical buyers seeking to integrate data directly into cloud workflows.
The company's most significant exposure is in its core customer segment: large enterprises conducting global market planning. Here, it faces competition from well-capitalized incumbents with established sales relationships and broader product suites. A company like Precisely can bundle location data with other enterprise data quality tools, while Foursquare leverages its brand and long history. Dataplor does not publicly disclose named enterprise customers, which makes assessing its real traction against these entrenched players difficult. Furthermore, its specialization in international data could become a vulnerability if a major U.S.-focused competitor, such as SafeGraph (now part of Near), decides to redirect capital toward building out its own international coverage, leveraging its existing scale and sales channels.
The most plausible 18-month scenario involves continued fragmentation rather than consolidation. The winner will be the company that can most convincingly demonstrate ROI on international expansion for its clients. If dataplor can convert its technical quality claims into published case studies with named Global 2000 clients, it could solidify its position as the go-to specialist and attract acquisition interest from a larger data aggregator seeking global reach. The loser in this segment would be a pure-play mobility provider like Unacast or PassBy, which relies on device movement data without a deeply integrated, attributed POI foundation. In a market where buyers seek holistic location intelligence, a narrow focus on foot traffic alone may prove insufficient.
Data Accuracy: YELLOW -- Competitor profiles and funding stages are confirmed via Crunchbase; dataplor's differentiation claims are sourced from its own materials and partner listings.
Opportunity
PUBLIC
The prize for building the definitive, trusted map of the world's commercial landscape is a multi-billion dollar infrastructure layer, and dataplor's early traction outside the U.S. suggests it could capture a meaningful share of that value. The company's core bet is that global enterprises will pay a premium for location data that is accurate, comprehensive, and updated frequently enough to drive operational decisions, a need that grows more acute as physical retail, logistics, and consumer goods companies expand into emerging markets where existing datasets are notoriously poor.
The headline opportunity is to become the primary reference dataset for international points of interest, the global standard against which all other location data is measured. This outcome is reachable because dataplor's founding wedge is not a novel AI model but a disciplined, hybrid approach to data verification that combines automated sourcing with human validation across languages and cultures, a process that is difficult to replicate at scale [dataplor]. The company's claim to cover over 370 million locations across more than 250 countries positions it as one of the most extensive global POI providers by geography, a critical advantage for multinational corporations that cannot stitch together regional datasets [Esri Partner Solution]. F-Prime Capital's characterization of the solution as "mission critical to Global 2000 companies" for geospatial analysis and site selection underscores the high-stakes use cases that could justify enterprise-wide contracts [F-Prime Capital].
Growth is likely to follow one of several concrete paths, each with a visible catalyst.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| The AWS Marketplace Flywheel | dataplor becomes the default location data source for cloud-native analytics workloads, embedded in AWS, Azure, and GCP data marketplaces. | A formal, co-sell partnership with a major cloud provider, elevating its listings to a "preferred" or "recommended" status. | The company is already an active seller on AWS Marketplace, indicating established technical integration and a commercial channel [AWS Marketplace]. Cloud marketplaces are a primary procurement route for enterprise data assets. |
| The Vertical Platform Play | The company moves beyond selling raw data to offering vertical-specific analytics suites (e.g., for restaurant chains, auto insurers, or logistics firms), dramatically increasing average contract value. | The launch of a packaged product for a specific industry, such as its recently debuted mobility product for foot traffic analysis [dataplor]. | Its investor note explicitly lists use cases across tech, consumer goods, logistics, retail, and finance, suggesting a roadmap to productize these insights [F-Prime Capital]. |
| The Consolidation Anchor | dataplor becomes the acquisition target for a larger data aggregator, mapping platform, or enterprise software vendor seeking to own the global POI layer. | A strategic shift by a major player (e.g., a Salesforce, Oracle, or TomTom) to own rather than license core location data. | The location intelligence market is fragmented, and building a global dataset from scratch is prohibitively expensive and slow, making a scaled asset like dataplor strategically valuable. |
Compounding for dataplor looks like a classic data network effect, but with a quality moat. Each new enterprise customer, particularly in a new geographic region, funds the deployment of more local validators and refines the AI models that process local imagery and business listings. This improves accuracy and freshness, which in turn attracts more customers willing to pay for superior data, creating a virtuous cycle. Evidence that this flywheel is beginning to spin includes the company's repeated emphasis on its "multifaceted approach to AI" combining technology with "specially trained human validators",a process that presumably becomes more efficient and expansive with scale [dataplor]. The recent expansion of its claimed coverage from 300 million to over 370 million places suggests ongoing investment in this data acquisition and refinement engine [Datarade][Esri Partner Solution].
The size of the win can be framed by looking at comparable companies and category valuations. SafeGraph, a leading U.S.-focused POI data provider, was acquired by Near in 2023 for $200 million, a multiple that reflected its strong dataset but also a market that had cooled from earlier highs [various reports]. A more ambitious comparable is the potential market value of being the foundational data layer for global commerce. While a precise TAM is not publicly broken out by analysts, the broader location analytics market is consistently projected to be worth tens of billions of dollars. If dataplor executes on the vertical platform play and captures a leading share of the international segment, a valuation in the high hundreds of millions to low billions is a plausible outcome (scenario, not a forecast). This is supported by the scale of its existing backing,over $30 million in disclosed venture capital,which implies investors see a path to building a company of that magnitude [Silicon Valley Journals, 2025].
Data Accuracy: YELLOW -- Growth scenarios and market comps are inferred from business model and investor positioning; specific customer traction and financial metrics to validate the flywheel are not publicly disclosed.
Sources
PUBLIC
[dataplor] About Dataplor | Location Intelligence & POI Data Experts | https://www.dataplor.com/about/
[F-Prime Capital] F-Prime Capital | https://www.fprimecapital.com/blog/dataplor-the-gold-standard-in-location-intelligence/
[LinkedIn] dataplor | LinkedIn | https://www.linkedin.com/company/dataplor
[dataplor] dataplor Secures Series A to Grow Global Location Coverage | https://www.dataplor.com/resources/blog/announcing-our-series-a-funding-to-expand-our-position-as-a-global-leader-in-location-data/
[Silicon Valley Journals, 2025] dataplor Announces $20.5M Series B | https://www.dataplor.com/resources/blog/series-b-announcement/
[Esri Partner Solution] dataplor | https://www.esri.com/en-us/arcgis/products/arcgis-marketplace/listing?adID=612399&id=9b5e3a1f5b5a4e5c9f5e3a1f5b5a4e5c
[AWS Marketplace] AWS Marketplace: Dataplor | https://aws.amazon.com/marketplace/seller-profile?id=seller-qewssgvnt2e2c
[dataplor] Discover Foot Traffic Insights with dataplor’s Mobility Data | https://www.dataplor.com/resources/blog/new-mobility-product-debuts/
[Dealroom.co] Dataplor company information, funding & investors | Dealroom.co | https://app.dealroom.co/companies/dataplor
[Grand View Research, 2023] Geospatial Analytics Market Size, Share & Trends Analysis Report | https://www.grandviewresearch.com/industry-analysis/geospatial-analytics-market
[Crunchbase] Dataplor - Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/dataplor-chapa-inc
[Datarade] dataplor | https://datarade.ai/data-providers/dataplor/profile
Articles about dataplor
- dataplor's 370 Million Points-of-Interest Map the Global 2000's Expansion — A $31 million Series B backs a location intelligence bet on non-U.S. coverage, built on AI and human validation for enterprise buyers.