Privacy engineering-as-a-service startup Gretel Labs offers a data categorization and identification platform designed to test anonymized versions of a data set automatically. The platform enables developers to synthesize, transform and classify data with an easy-to-use suite of tools and application programming interfaces that eliminate data privacy issues through safe data sharing. Gretel says its platform allows developers and data practitioners to implement intelligent, high-quality data privacy measures so they can quickly and safely innovate with data. The service is claimed to do so at a fraction of the time, cost and risk to their users’ privacy and brand. Under the hood, the platform users artificial intelligence and machine learning techniques to provide accurate, private and high-quality synthetic data with significant time savings for engineers. Machine learning is used to categorize data across names, addresses and other customer identifiers and features automatic data labeling, power testing and synthetics with support for experimentation, collaboration and building with customer data.
Powerlytics and Railz have partnered to provide banks and fintechs with a powerful new benchmarking product via more granular and actionable data to better support small business and mid-market customers’ success. As part of the agreement, Railz will integrate Powerlytics’ proprietary business financial data covering the complete financial statements and performance ratios on 30 million U.S. businesses to augment its Accounting Data-as-a-Service API and platform. The integration between Powerlytics and Railz provides banks and fintechs access to a deep financial profile of their business customers, along with corresponding performance opportunities based on the benchmark data of their customers’ competitors. Together, Powerlytics data and the Railz platform help financial services companies drive deeper and more impactful relationships with their business customers by better understanding their competitive landscape, providing more relevant financial products, and building the most effective credit models to improve decisions around small business loans. It will empower the banks and fintechs using the Railz platform to deliver the most relevant products and services that will best help fuel their business customers’ growth.
The Members Exchange, an upstart exchange, is putting its market data on a blockchain network in a nod towards the potential future of how Wall Street accesses its information. The equities exchange is offering free access to its real-time pricing data on the Pyth Network, a distributor of financial market-data that sits on the Solana blockchain. The move will bring MEMX data to new users and is a key step in spurring competition in the space. MEMX’s partnership with Pyth isn’t just about leveraging new tech to distribute data. It’s also about increasing access to market data to a wider swath of users. Market data — which includes everything from the bid and ask price of a particular stock to how much, or little, it is being traded — is the lifeblood of any trading organization. The information is key in helping traders, and their algorithms, make decisions in the market. But access to the data doesn’t come cheap. Global spending on financial market data and analysis totaled $33.2 billion in 2020, a 5.9% year-over-year increase. But despite how crucial market data is to participants, the industry is largely dominated by a handful of players like Bloomberg and Refinitiv.
Mindee offers an API that lets you turn raw data in a paper document into structured data. Many companies rely on OCR services (optical character recognition) from Google or Amazon. But turning text into raw digital text isn’t the most complicated step. After that, you want to turn raw digital text into structured data. This is much more complicated as companies often rely on humans to identify the right information and paste it in the right data field. Essentially, you can process a photo through Mindee API and turn it into relevant data for your product without any manual data entry. Many industries could use this kind of API-based product, from expense management to procurement, accounting, loan applications and more. What makes Mindee special is that the experience for the end user is much better. For instance, when you try to sign up to a service that requires your passport information, you usually have to enter your personal information, take a photo and wait. It’ll ping someone so that they can make sure it’s the same information in the registration system and on the photo. If Mindee manages to capture data in near real-time and with a near perfect level of accuracy, you could get a much snappier experience. And that could lead to more customers for Mindee’s clients.
Startup Duality is building tools to make it easier for companies to share data and collaborate with each other without compromising sensitive information. The startup uses homomorphic encryption — a relatively new technique that lets companies analyze and use encrypted data without needing to decrypt it — to build more privacy-centric, secure collaboration tools. Data breaches and other cybersecurity issues are occurring these days with increasing frequency while data protection regulations and consumer expectations are getting ever stricter, and so grows the need for better tools to handle how data is used. That is one reason why Duality has attracted the attention of so many strategic backers. Duality is ideally positioned to lead the applications of privacy-enhanced computing in numerous industries through this period of rapid change.
Panther Protocol who is leading the race for enabling interoperable blockchain privacy for DeFi and the open web, announces a new fintech platform offering a cross-chain, end-to-end solution to protect privacy on the blockchain. Panther Protocol differentiates itself by proposing a balance between privacy and compliance that gives users a choice to transact privately, while also being able to voluntarily disclose transactional data with select counterparties and trust providers. It also offers a novel type of financial disclosures called Zero Knowledge disclosures, where users can prove they are compliant without having to actually provide any underlying data. This technology could turn the compliance industry on its head and provide an end to the data breach nightmares that plague modern societies and cost institutions billions of dollars every year. This is a critical feature for financial institutions, providing a compliance ready approach for entering the DeFi space while not losing sight of the privacy preferences of end users of financial services. Currently, Panther is building on public blockchains such as Ethereum, Polygon, Flare, Near and Avalanche with the ambition to privately connect all EVM compatible smart contract platforms and become the private, scalable infrastructure for the internet of blockchains.
- Startup Osano is a data privacy platform that helps websites become compliant with international regulations. The Osano platform leverages a data privacy scoring graph dataset curated by U.S. bar-certified attorneys. AI models trained on billions of live data samples, both public and private, rate privacy practices and map out third parties with whom Osano users are sharing data.
- Osano’s machine learning system scans API endpoints for SaaS providers, determining whether documents contain personal data and classifying any personal data into one of 165 different buckets. The system, which is available as both a SaaS offering and a self-hosted Kubernetes container, also discovers and categorizes non-personal data to automate privacy rights fulfillment. Osano’s other products touch on consent and preference management, handling tasks like soliciting permission from visitors, employees, and vendors before processing their information. The platform ingests requests from users via the web and phone to delete, redact, or correct their data.
- Beyond this, Osano maintains a database that keeps track of the risk that companies introduce into a privacy program. Currently, companies rely primarily on assessments and manual reviews of security and privacy documents. Osano turns this into a real-time, auto-updating vendor monitoring program, [tracking] the privacy practices of vendors, litigation against vendors federally and in 28 states, and changes to documents such as privacy policies and other compliance statements.
Exabel, a data and analytics platform for investment teams, is partnering with Verbatim Advisory Group to deliver a powerful new insights platform for Verbatim’s investment clients. The Verbatim Data Insights Platform will give portfolio managers and analysts additional insights based on Verbatim’s channel survey data, which tracks the demand and performance trends across a broad range of retailers and restaurants. The platform delivers user-friendly dashboards, visualizations and KPI monitoring capabilities. This assists investors in idea generation by spotting trend shifts in Verbatim’s consumer data. Partnering with Exabel gives alternative data vendors a compelling presentation and monitoring layer that investors value, utilising Exabel’s unique Al analytics, financial modeling and data science platform. The platform empowers data vendors to discover new value-added insights in their datasets, demonstrate extra value to potential customers in easy-to-create report cards, and deliver a new, proven Insights product that appeals to a wide group of professional investors.
Startup Xata has a new take on managed databases. The company runs database for them and turns it into an API so that the companies can query and update it from their serverless app. Xata seems particularly well suited for Jamstack websites. Applications are deployed on a global edge network and most of the logic is handled by API calls. The result is a website or an application that loads quickly and can handle a lot of traffic. Deploying a Jamstack website is quite easy as it often integrates tightly with a users Git repository. Xata is focusing on databases and want to make it easier to integrate a database with serverless app. Users don’t have to take care of the underlying infrastructure as Xata can scale the database for them. They don’t have to update software, move data to a new server, etc. The database is distributed across multiple data centers to improve response times and redundancy. It supports many data types including images. After that, interacting with the database works like any RESTful API out there. Users can open their database in a web browser and interact with their data directly from there. For instance, they can filter the current view, sort data using a specific criterion and get the API query that they can use in their code. Users can search through their data using a free-text search feature. They can also leverage Xata for analytics by creating charts and visualizations.
Relyance AI, is an early-stage startup that is helping companies stay in compliance with privacy laws at the code level. Relyance takes an unusual approach to verifying that data stays in compliance working at the code level, while ingesting contracts and existing legal requirements as code to ensure that a company is in compliance. Relyance is actually embedded within the DevOps pipeline of customers’ infrastructure. So every time a new ETL pipeline is built or a machine learning model is receiving new source code, it does a compiler-like analysis of how personal sensitive data is flowing between internal microservices, data lakes and data warehouses, and then get a metadata analysis back to the privacy and compliance professionals [inside an organization]. Relyance also enables companies to define policy and contracts as code.