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Data Enlighten

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Data Enlighten is a Transportation & Space company

Dongcheng District, ChinaFounded 2015Transportation
Valuation $50–100M Last recorded · 2018
Headcount <100 Current team size
Total funding $10–50M Disclosed equity funding
Unlock exact values This page shows ranges. Dealroom has the exact numbers for Data Enlighten and the history behind them.

About Data Enlighten

Based in Chaoyang, China, Data Enlighten has carved a niche in the automotive aftermarket by providing AI-driven data solutions since its inception in 2015. The company was established by Zhou Kai, who also serves as the CEO. Data Enlighten has secured significant financial backing, raising a total of $29.4 million over two funding rounds. A notable Series A round in September 2018 brought in $21.8 million from investors including Yunfeng Capital, Didi, and Ant Group.

The core of Data Enlighten's business is the processing of complex automotive aftermarket data to provide actionable insights. Its client base spans several sectors within the automotive industry, including insurance companies, car leasing and rental agencies, used car businesses, and logistics providers. For the insurance sector, the company offers solutions for smart underwriting and automation of the First Notice of Loss (FNOL) process. In the Chinese market, Data Enlighten serves over 50 insurance companies and processes more than 70% of the nation's car insurance claims daily. For repair businesses, it provides user-friendly software to quickly find vehicle and part information, such as OE numbers and prices. The company also assists parts suppliers by helping manage product data and vehicle compatibility, which supports sales and R&D decisions.

Data Enlighten's primary offering is a Platform as a Service (PaaS) solution that utilizes computer vision and AI for vehicle inspections. This technology allows users to perform an AI-powered vehicle inspection by taking a few photos, receiving results within a minute. The platform's capabilities include detecting 12 types of damage across various materials, identifying parts with high accuracy, and analyzing image quality for factors like blur and exposure. Specific products include 'DE Underwriter' for policy purchase automation, 'DE Inspector' for accident reporting, and 'DE Assessor' for damage assessment and estimation. This approach aims to enhance efficiency for clients, enabling precise claim payments and improving customer satisfaction.

Source: Dealroom company data

At a glance

Status
Operational
Founded
2015
Headquarters
Dongcheng District, China
Employees
32
Open positions
None listed
Dealroom signal
21.3/100
Industry
Transportation
Sub-industry
Navigation & Mapping, Vehicle Production
Sector
Transportation, Space Travel
Links
Website

Funding history

1 recorded event

Company financing events and reported valuations.

Deal typeDateAmountValuationInvestorsSource
SERIES A✓Equity / VC Oct 2018 $14M $58M Undisclosed —
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Growth signals

Data Enlighten website traffic

Latest recorded traffic for Data Enlighten is 12K monthly website visits, down 29.4% over the last three months. Data Enlighten's team grew 0% over the past year.

Latest monthly visits12K 3-month web change-29.4% 1-year team growth0%
Summary metrics are public; monthly history is available with a demoCompare with peers →

Investments and acquisitions

Capital deployed

Recent portfolio relationships for companies that also act as investors or acquirers.

Employees 32 total headcount
Founders Hidden — book a demo/100 quality score
Key operators Hidden — book a demo quality score
Potential founders Hidden — book a demo likely future founders
Startup mafia Hidden — book a demo alumni-founded startups
Talent movements Hidden — book a demo in · Hidden — book a demo out past 12 months

Source: Dealroom Talent Intelligence.

Global footprint

Workforce and web traffic by country
Workforce by country
🇨🇳 China · Dongcheng District HQ
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Market sentiment

Posts on X, past 7 days

An AI-synthesised read of the highest-engagement posts about Data Enlighten on X, ranked by likes and reposts, corporate channels excluded.

Reading the room on X — pulling top posts and synthesizing themes…

Source: X recent search ranked by engagement (likes + retweets) · Synthesis by Claude · Cached for 1 hour

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