Phantom Threat Intelligence
Alternatives
0 PH launches analyzed!
Phantom Threat Intelligence
Safeguard your data in an everevolving cyber landscape
1
Problem
Security teams rely on manual monitoring of public/hidden sources for compromised credentials, breached data, and attack trends, leading to delayed threat detection and reactive responses.
Solution
An AI-powered threat intelligence platform that automates real-time monitoring of public/dark web sources, analyzes attack patterns, and provides actionable insights to proactively defend against cyber threats.
Customers
Cybersecurity professionals, especially security operations center (SOC) teams and threat analysts in enterprises handling sensitive data.
Unique Features
Combines AI-driven analysis of hidden/dark web data with real-time attack trend monitoring, offering predictive threat alerts and compromised credential tracking.
User Comments
Saves hours of manual threat hunting
Accurate dark web monitoring
Proactive breach alerts
Easy integration with existing tools
Critical for compliance
Traction
Launched on ProductHunt (2024)
Details on users/MRR unspecified in provided data
Positioned in fast-growing cybersecurity AI sector
Market Size
The global cybersecurity AI market is projected to reach $60.6 billion by 2028 (Grand View Research, 2023).
Data Protection- Encryption Data Control
Data Protection is Revenue Protection
6
Problem
Users are at risk of data theft, leaks, and unauthorized access with the current solution.
Drawbacks include lack of comprehensive safeguards, compromised confidentiality, and integrity of critical records.
Solution
A data protection application
Provides comprehensive safeguards against data theft, leaks, and unauthorized access.
Ensures confidentiality and integrity of critical records.
Customers
Businesses handling sensitive customer and employee data,
Companies prioritizing data security and confidentiality.
Unique Features
Robust safeguards against data theft, leaks, and unauthorized access.
Comprehensive protection for critical records.
User Comments
Great product for ensuring data security!
Easy to use and effective in safeguarding sensitive information.
Provides peace of mind knowing our data is secure.
Highly recommend for businesses prioritizing data protection.
Efficient solution for maintaining data confidentiality and integrity.
Traction
Innovative product gaining traction in the market.
Positive user feedback and growing user base.
Market Size
$70.68 billion global data protection market size expected by 2028.
Increasing demand for data security solutions driving market growth.
Cyber Risk in Retail
Cyber risk management in retail — ostrich cyber
4
Problem
Retail businesses currently rely on fragmented or manual cybersecurity measures, leading to inefficient protection against evolving cyber threats and increased risk of data breaches that damage customer trust.
Solution
A cybersecurity dashboard tool enabling retailers to automate cyber risk assessments, monitor threats in real-time, and implement tailored security protocols. Example: Scanning for vulnerabilities in payment systems.
Customers
Retail managers, IT security teams, and compliance officers in mid-to-large retail chains prioritizing data protection and regulatory compliance.
Alternatives
View all Cyber Risk in Retail alternatives →
Unique Features
Specialized focus on retail-specific threats (e.g., POS system breaches), combining compliance tracking with proactive threat detection.
User Comments
Simplifies compliance audits
Reduces response time to breaches
User-friendly interface for non-technical staff
Cost-effective compared to enterprise solutions
Lacks integration with legacy systems
Traction
Launched on ProductHunt with 180+ upvotes, details on revenue/users not publicly disclosed.
Market Size
The global cybersecurity market is projected to reach $200 billion by 2023, with retail cybersecurity a growing segment due to rising digital transformation.
Urban Data Dictionary
Your translator for corporate data speak. Duolingo for data.
11
Problem
Data professionals manually decipher corporate data jargon and unclear terms during meetings and documentation, leading to a time-consuming and error-prone process that causes miscommunication and frustration.
Solution
A web-based translation tool that translates corporate data jargon into plain language using a Duolingo-like approach, enabling users to input terms like 'synergy' and receive humorous, context-aware explanations (e.g., 'empty buzzword').
Customers
Data analysts, data scientists, and business analysts in corporate roles; managers and non-technical stakeholders collaborating with data teams.
Alternatives
View all Urban Data Dictionary alternatives →
Unique Features
Combines sarcastic humor with practical translations to make decoding jargon engaging, unlike traditional dry glossaries.
User Comments
Saves time in meetings
Makes jargon relatable through humor
Improves cross-team communication
Easy to integrate into workflows
Reduces misunderstandings
Traction
Launched on ProductHunt (exact metrics unspecified). Founder’s social media presence and engagement not publicly quantified.
Market Size
The global data analytics market is projected to reach $303.4 billion by 2030 (Grand View Research), indicating high demand for tools that streamline data-related communication.
Ostrich Cyber-Risk
Why cyber risk quantification ? — ostrich cyber
5
Problem
Users struggle to quantify financial impacts of cyber risks and rely on manual or qualitative assessments, leading to inefficient resource allocation and unclear cybersecurity ROI.
Solution
A cyber risk quantification software enabling users to measure financial impacts of cyber risks using data-driven models, e.g., prioritizing investments based on risk exposure vs. mitigation costs.
Customers
Cybersecurity professionals, risk managers, and CISOs in mid-to-large enterprises seeking to align cybersecurity strategies with financial outcomes.
Unique Features
Focuses on translating cyber risks into monetary terms, enabling cost-benefit analysis for cybersecurity investments.
User Comments
Simplifies complex risk calculations
Helps justify cybersecurity budgets
Intuitive dashboard for financial metrics
Lacks integration with some tools
Steep learning curve for non-financial teams
Traction
Launched on ProductHunt (exact metrics unavailable from provided data). Typical CRQ tools like RiskLens report $10M+ ARR and enterprise adoption.
Market Size
The global cybersecurity market is projected to reach $200 billion by 2024, with risk quantification tools gaining traction in enterprises.
Thomson Data
Data as a Service (daas)
9
Problem
Users struggle to collect, access, and leverage global datasets and ABM insights effectively for business growth.
Solution
A platform providing Data as a Service (DaaS), offering access to global datasets, ABM insights, and a comprehensive 360° view of data to facilitate business expansion.
Customers
Business owners, marketers, sales professionals, and data analysts seeking to enhance their strategies with curated global datasets and ABM insights.
Unique Features
Comprehensive ABM insights, global datasets access, and a 360° view of data distinguish this platform in offering tailored data services.
User Comments
Helpful insights for business growth
Great source for global datasets
Invaluable tool for targeting the right audience
Easy to navigate and utilize
Highly recommended for data-driven decisions
Traction
The specific traction details for Thomson Data are not available.
Market Size
No specific market size data available for Thomson Data, but the global data as a service (DaaS) market was valued at around $5.24 billion in 2020 and is projected to reach $16.61 billion by 2026.
Data Driven VC Landscape 2025
How Top VCs Use AI to Win: Insights from 235+ Leading Firms
45
Problem
Venture capitalists and investors manually research AI strategies and tools, leading to an inefficient, time-consuming process and lack of comprehensive data on competitors’ approaches.
Solution
A community platform and report that aggregates insights from 235+ VCs, offering data-driven strategies, toolkits, and AI adoption trends. Users access curated reports, frameworks, and a network of 46k+ professionals to optimize investment decisions.
Customers
Venture capitalists, investment professionals, and startup founders seeking competitive insights into AI-driven VC strategies.
Unique Features
Exclusive aggregation of proprietary data from leading VCs, 600+ tool recommendations, and actionable frameworks for building data-driven investment firms.
User Comments
Saves months of research with consolidated AI-VC insights
Practical toolkits for immediate implementation
High-value network for collaboration
Up-to-date industry benchmarks
Critical for staying competitive in AI-driven investing
Traction
46,000+ community members, report covering 235 VCs and 100 thought leaders, founder David Teten (Data Driven VC founder) has 10k+ LinkedIn followers
Market Size
The global venture capital market reached $300 billion in 2022 (Preqin data), with AI-investing tools becoming critical differentiators.
Financial Data
Stock Market and Financial Data API
6
Problem
Users currently rely on traditional methods of gathering financial data, such as manual searches on financial websites or outdated data platforms. The drawbacks are the inefficiencies and inaccuracies associated with collecting comprehensive data sets such as market data, company fundamentals, and alternative data.
Solution
Financial Data API offering a comprehensive data access solution. Users can access over 20 years of historical market data on various financial instruments like stocks, funds, and ETFs, along with alternative data, all via an API.
Customers
Finance professionals, data scientists, analysts, and developers who require extensive financial data for analysis, modeling, and investment decision-making. Typically, they are tech-savvy individuals who engage in data-driven decision-making processes.
Alternatives
View all Financial Data alternatives →
Unique Features
The solution offers access to a massive repository of financial data, including over 20 years of historical data on more than 15,000 stocks, 20,000 funds, and 2,000 ETFs. This depth and breadth of data available via API access for integration with other tools is unique.
User Comments
Users appreciate the comprehensive data coverage.
Easy integration with existing systems via API.
Some users request more real-time data updates.
Positive feedback on historical data depth.
Some concerns about the learning curve for new users.
Traction
The product has aggregated a substantial amount of historical data for thousands of financial instruments and has a user base of individuals and organizations involved in financial data analysis.
Market Size
The global financial data market was valued at approximately $30 billion in 2020, with expectations for continuous growth driven by the increasing demand for accurate and historical financial data.
Ask On Data
Open Source GenAI powered chat based Data Engineering tool
7
Problem
Users, especially data scientists and engineers, struggle with traditional data engineering tools that are not user-friendly and efficient for tasks like data migration, cleaning, and analysis.
Solution
A chat-based ETL tool powered by AI for data engineering tasks such as data migration, cleaning, and analysis, offering an open-source and accessible solution for data scientists and engineers.
Users can interact with the tool via chat to perform various data engineering tasks.
Customers
Data scientists, data engineers, and professionals in need of efficient data engineering tools for tasks like data migration, cleaning, and analysis
Unique Features
AI-powered chat-based interface for data engineering tasks, open-source nature of the tool, accessibility, and user-friendliness.
User Comments
Efficient and user-friendly tool for data engineering tasks.
Helps streamline processes and enhance productivity for data scientists and engineers.
Accessible and easy to use via chat interface.
Great alternative to traditional data engineering tools.
Traction
The product has gained traction in the data engineering community with a growing user base and positive feedback.
It has received attention for its unique approach and ease of use.
Market Size
The global data engineering tools market was valued at approximately $1.02 billion in 2021 and is expected to reach $3.31 billion by 2028.
Data Up Pro
Automate and simplify data processing .
4
Problem
Currently, users manually prepare datasets for analysis and machine learning, which can be time-consuming and prone to errors. The drawbacks of the old situation include time-consuming dataset preparation and susceptibility to errors.
Solution
The solution is a data processing tool called Data Up Pro, which automates dataset preparation for analysis and machine learning. Users can automate data preparation and utilize features such as API integration and batch processing to further streamline large-scale data cleaning.
Customers
Data scientists, machine learning engineers, and analysts working in tech companies and research institutions, who are looking to streamline their data preparation processes. They might often deal with large datasets and need accurate and efficient data processing.
Unique Features
The unique aspects of Data Up Pro include its ability to automate key data processing tasks, provide API integration for seamless workflow adaptation, and enable batch processing for handling large-scale data effectively.
User Comments
Users appreciate the time-saving technology.
The automated processes reduce errors in data cleaning.
API integration enhances their workflow.
Batch processing is beneficial for large datasets.
Efficient tool for data scientists to prep datasets.
Traction
As of the latest information on ProductHunt, specific values like the number of users, revenue, or financing were not provided. Detailed traction data may be acquired from additional market research or company updates.
Market Size
The global data preparation tools market was valued at $3.1 billion in 2020 and is expected to grow significantly due to the increasing adoption of tools for automating data processes.