Million Archives - Business Intelligence Info

Tag Archives: Million

Cere Network raises $5 million to create decentralized data cloud platform

March 29, 2021 Big Data

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more.

Cere Network has raised $ 5 million for its decentralized data cloud (DDC) platform, which is launching today for developers. The company’s ambition is to take on data cloud leader Snowflake.

The investment was led by Republic Labs, the investment arm of crowdsourced funding platform Republic. Other investors include Woodstock Fund, JRR Capital, Ledger Prime, G1 Ventures, ZB exchange, and Gate.io exchange. Cere Network previously raised $ 5 million from Binance Labs and Arrington XRP Capital, amongst others, bringing its total raised to $ 10 million.

“Enterprises using Snowflake are still constrained by bureaucratic data acquisition processes, complex and insufficient cloud security practices, and poor AI/ML governance,” Cere Network CEO Fred Jin said in an email to VentureBeat. “Cere’s technology allows more data agility and data interoperability across different datasets and partners, which extracts more value from the data faster compared to traditional compartmentalized setup.”

The Cere DDC platform launches to developers today, which allows thousands of data queries to be hosted on the blockchain, the transparent and secure digital ledger.

The platform offers a more secure first-party data foundation in the cloud by using blockchain identity and data encryption to onboard and segment individual consumer data. This data is then automated into highly customizable and interoperable virtual datasets, directly accessible in near real time by all business units, partners/vendors, and machine-learning processes.

Cere Network raises $5 million to create decentralized data cloud platform

Above: Cere Network’s data cloud query.

Image Credit: Cere Network

The Cere token will be used to power its decentralized data cloud and fuel Cere’s open data marketplace that allows for trustless data-sharing among businesses and external data specialists, as well as staking and governance. The public sale of the Cere token will be held on Republic, the first token sale on the platform.

“We’ve been following Cere Network for some time and have been impressed with the team and the market fit – and need – for a decentralized data cloud,” said Boris Revsin, managing director of Republic Labs, in a statement. “We’re very excited to host Cere Network’s token sale on Republic, which will ensure a decentralized network and faster adoption in the enterprise space of blockchain technology. Their DDC improves upon Snowflake using blockchain identity and data encryption to onboard and segment individual consumer data.”

Developers can access the Cere DDC here. The public sale for Cere token is scheduled for March 31 on Republic. The company said it is working with a number of Fortune 1,000 customers.

“There’s a huge amount of opportunities in this rapidly shifting space for the coming years. We don’t plan to take on the likes of Snowflake head on, yet, but rather focus on specific solutions and verticals where we can bring more customization and efficiency. We are ok with chipping away at their lead while doing this,” Jin said. “We are bringing an open data marketplace which will open up data access beyond the limitation of traditional silo’d data ecosystems, which include Snowflake, and the likes of Salesforce.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Quality Match raises $6 million to build better AI datasets

March 2, 2021 Big Data

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more.

Quality Match, a Heidelberg, Germany-based quality data annotation provider, today announced that it raised a €5 million ($ 6 million) seed round from LEA Partners. The company says it’ll use the proceeds to expand its team and accelerate product development.

Training AI and machine learning algorithms requires plenty of annotated data. But data rarely comes with annotations. The bulk of the work often falls to human labelers, whose efforts tend to be expensive, imperfect, and slow. It’s estimated most enterprises that adopt machine learning spend over 80% of their time on data labeling and management. In fact, in a recent survey conducted by startup CloudFlower, data scientists said that they spend 60% of the time just organizing and cleaning data compared with 4% on refining algorithms.

Quality Match, which was bootstrapped in 2019 by a team of former Pallas Ludens, Apple, Google, Microsoft engineers, aims to improve the speed and quality of data labeling processes by disambiguating the potential sources of error. The platform explains the sources of errors in datasets, highlighting where edge cases originate and providing strategies on how to improve the data.

There’s no shortage of data labeling startups competing with Quality Match — the market was valued at $ 1.3 billion in 2020, according to Grand View Research. For instance, Scale AI has raised over $ 100 million for its suite of data annotation services. There’s also CloudFactory, which says it offers labelers growth opportunities and “metric-driven” bonuses. Hive, Alegion, Appen, SuperAnnotate, Dataloop, Cognizant, and Labelbox are other rivals of note.

But Quality Match uniquely begins building or enhancing datasets by optimizing the representativeness and diversity of the samples, ensuring they’re representative of the real world and contain difficult edge cases sprinkled throughout. Then, the platform exposes and quantifies ambiguity in the datasets before breaking the taxonomies into small, intuitive questions that form a fully automated decision tree. Quality Match runs multiple repeats of this decision tree to provide confidence scores on all of the annotations.

Moreover, Quality Match provides metrics including geometric, label, and definition accuracy that are intended to inform about wrong tags or attributes as well as missed or spurious detections of annotations. It also shows how factors like taxonomy version changes over time and varying criteria for quality scoring might be contributing to imbalances in the datasets.

“During the pandemic, our industrial customers, in particular, have increasingly realized that they will have to rely more on high-tech solutions in the future because, in these times, large groups of people can no longer work together in one room,” said cofounder and managing director Daniel Kondermann, who told VentureBeat that the goal this year is to reach €1 million in revenues. “To be successful, companies must adapt, which leads to an increasing demand for automation and therefore AI across a wide range of industries. Quality Match also started entering the market of medical technology. An industry that got even stronger due to the pandemic and therefore continued to develop new and improve existing AIs. All these industries are asking for our datasets and profiting from our work which is why so far, we have managed this pandemic very well.”

Twenty-employee Quality Match, which counts among its customers Mapillary, Bosch, and other companies engaged in health, 3D maps, autonomous driving, AR/VR, retail, and construction, received the whole of its latest funding from LEA Partners. Kondermann says that the immediate focus will be on hiring talent.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

TripleBlind raises $8.2 million for its encrypted data science platform

March 1, 2021 Big Data

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more.

TripleBlind, a Kansas City, Missouri-based startup developing a platform that enables companies to train models on encrypted data, today announced that it raised $ 8.2 million. The company says the proceeds will be put toward R&D as it looks to expand the international reach of its products.

In industries like financial services and health care, privacy regulations like the U.S. Health Insurance Portability and Accountability Act (HIPAA) prevent companies from sharing data. As a result, 73% of enterprise data goes unanalyzed, according to a recent analysis.

Earlier in his career, CEO Riddhiman Das was the product architect at EyeVerify, where he helped commercialize a software-only biometric method for verifying the identity of mobile users. EyeVerify was bought by payments giant Ant Financial, but it ran into privacy issues and wasn’t able to collect eye image data with which to improve its algorithms. Shortly after, cofounder Greg Storm and Das left Ant to devote themselves to creating a solution — TripleBlind — that focused on market-driven and regulatory concerns with regard to data storage and auditability. .

TripleBlind claims to have developed “next-generation” cryptology that allows companies to provide and use sensitive data and algorithms in an encrypted space, without compromising privacy. The startup aims to launch a marketplace that will let research institutes, businesses, and engineers collaborate around both sensitive data and algorithms.

TripleBlind’s product encrypts data during upload and affords users the option of encrypting their algorithms where intellectual property theft might be of genuine concern. The company’s digital rights management and auditability tools for transactions are designed to complement this by letting suppliers control who, when, how often, and for what purpose their models are used.

For example, in health care settings, TripleBlind — which has an ongoing partnership with the Mayo Clinic, Accentures, and about a dozen other partners and customers — enables health care providers to provide only encrypted data from partners, providers, and patients. That allows data scientists to perform operations they would not be able to do using raw electronic medical records without running afoul of HIPAA.

“The core thesis at TripleBlind is that privacy enforced data and algorithm interactions will unlock the tremendous value, currently trapped in private data stores and proprietary algorithms,” Das said. “We will move the world from ‘don’t be evil’ to ‘can’t be evil,’ by enabling everyone to freely collaborate around their most sensitive data and algorithms without compromising their privacy. Together we will create a new paradigm of compounded value.”

This latest investment in TripleBlind brings the company’s total raised to nearly $ 10 million, following a round led by Accenture in November 2020.

Enthusiasm for data encryption has given rise to a cottage industry of startups estimated to be worth a combined $ 268.3 million by 2027. Duality Technologies, which recently attracted funding from one of Intel’s venture capital arms, pitches its homomorphic encryption platform as a privacy-preserving solution for “numerous” enterprises, particularly those in regulated industries. In March 2019, Paris-based Cosmian raised €1.4 million (about $ 1.5 million) for a data encryption product that combines functional and homomorphic encryption. There’s also Enveil, which is developing “enterprise-scale” data encryption solutions, and security-conscious collaboration platform Cape Privacy.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

FundGuard raises $12 million to help manage assets with AI

February 23, 2021 Big Data

FundGuard, an AI-powered software-as-a-service investment management platform, today announced that it closed a $ 12 million funding round. The investment will spur product development to support existing partnerships, FundGuard says, in addition to helping to meet demand from alternative funds and insurers.

AI is increasingly being used to manage assets toward the goal of maximizing returns on investments. For example, trading algorithms leverage AI to devise novel signals and execute trades with lower overall transaction costs. AI can also bolster risk modeling and forecasting by generating insights from previously untapped data sources.

For its part, FundGuard offers a cloud-based investment funds solution powered by AI. It’s designed to help asset and fund managers administer investments across mutual funds, ETFs, hedge funds, insurance, and pensions with utilities that execute reconciliation, exception management, and reporting.

FundGuard can apply AI to automatically identify and resolve data mismatches and breaks for things like positions, general ledger, and trades. At a more basic level, the platform’s algorithms can perform portfolio validation checks and benchmarking as well as investment news research, anomaly detection, and exceptions resolution.

Using FundGuard, managers can add layers of oversight for more detailed reviews of cash, corporate actions, and holdings movements, with day-to-day change summaries. They also get access to sets of predefined controls and customizable thresholds for rules-based trading.

There’s growing interest from the private sector in such technology. A report the Bank of England published in October 2019 found that two-thirds of financial services in the U.K. use AI and that many expect to more than double the number of business areas to which they apply AI within the next three years. Be that as it may, skepticism abounds — Canada-based Horizons vowed to revamp its AI-powered exchange-traded fund after a year in which it “significantly underperformed.”

But FundGuard cofounder and CEO Lior Yogev is quick to beat back skepticism. “We are seeing growing demand for our innovative cloud-native and API-first platform from banks and asset managers globally,” he said in a press release. “Our team is excited to be able to accelerate our mission to drive efficiencies through AI, enhance investor transparency and digital engagement, and provide a single source of truth for funds.”

New York- and Tel Aviv-based FundGuard’s series A financing announced today was led by Team8, Blumberg Capital, LionBird Ventures, and JPMorgan operating committee members Heidi Miller and Jay Mandelbaum. It brings the company’s total raised to date to over $ 16 million.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Peak.AI raises $21 million to drive enterprise AI adoption

February 17, 2021 Big Data

Peak.AI, a startup developing AI solutions for enterprise customers, today announced that it closed a $ 21 million series B round. The funds, which bring Peak’s total raised to date to $ 43 million, will drive the company’s R&D and commercial expansion in the U.S. and India, according to CEO Richard Potter.

The global enterprise AI market size was valued at $ 4.68 billion in 2018 and is projected to reach $ 53.06 billion by 2026, according to Allied Market Research. But the corporate sector’s adoption curve hasn’t been as steep as some had predicted despite the promise of AI. A survey of publicly traded U.S. retailers’ earnings calls found that only 9 of about 50 companies had started to discuss an AI strategy, and a separate study from Genesys shows that 68% of workers aren’t yet using tools that leverage AI.

Peak aims to simplify the implementation of AI systems with a subscription-based software-as-a-service (SaaS) offering that spans infrastructure, data processing, workflow, and applications. Its customers — brands like Pepsi and Marshalls — supply their data, which Peak’s platform ingests through built-in connectors to accomplish things like optimizing supply and demand and supporting fulfillment processes, courtesy of a library of configurable AI engines.

Once AI engines go live, their predictive and prescriptive outputs can be exposed through APIs or explored, visualized, and exported with Peak’s Data Studio. The platform can handle datasets of virtually any size running on Amazon Web Services, and it serves models in an always-on fashion so that they self-improve over time. Peak also screens all ingested data through an algorithm to identify and anonymize any personally identifiable information.

Peak’s team optionally works with customers to define objectives, quantify opportunities using a sample of data, and scope out a business case for sign-off and launch. The company can take care of deployment and onboarding as well as operationalizing, and it can configure a solution to an individual user’s needs.

There’s no shortage of managed AI development platforms with venture backing. H2O recently raised $ 72.5 million to further develop its platform that runs on bare metal or atop existing clusters and supports a range of statistical models and algorithms. Cnvrg.io — which recently launched a free community tier — has raised $ 8 million to date for its end-to-end AI model tracking and monitoring suite.

But Peak, which claims that revenues doubled over the past 12 months thanks to customer wins in Europe, the U.S., the Middle East, and Asia, asserts that its platform is more performant. The company says it has helped customers achieve a 5% increase in total company revenues, a doubling of return on advertising spend, a 12% reduction in inventory holdings, and a 5% reduction in supply chain costs.

“It’s becoming impossible to run a business without AI. Modern businesses are complex and operate in an ever-changing world,” Potter said in a statement. “Our software empowers day-to-day decision makers across businesses, and we’re proud to be working with household names such as PrettyLittleThing, KFC, and PepsiCo, and other industry leaders like Marshalls and Speedy Hire. We’re delighted to have secured this new funding in an oversubscribed round.”

Oxx led Peak’s latest fundraising round with participation from existing investors MMC Ventures and Praetura Ventures and new investor Arete. The company, which was founded in December 2014 by Potter, David Leitch, and Atul Sharma, has additional offices in Jaipur and Edinburgh and plans to hire 130 employees in the coming year.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Insightin Health raises $12 million for AI that uses big data to guide patients’ decisions

February 10, 2021 Big Data

Insightin Health, a company delivering personalized health care guidance, today announced it has raised $ 12 million. A spokesperson told VentureBeat the round will support Insightin Health’s plan to bring on more plan providers that service primarily Medicare, Medicaid, and accountable care organization members.

The global big data analytics market for health care was valued at $ 16.87 billion in 2017 and is projected to reach $ 67.82 billion by 2025, according to a recent report from Allied Market Research. It’s believed that health care organizations’ implementation of big data analytics might lead to an over 25% reduction in annual costs in the coming years. Better diagnosis and disease predictions, enabled by AI and analytics, can lead to cost reduction by decreasing hospital readmission rates, among other factors.

Baltimore, Maryland-based Insightin Health, which was founded in 2016 by Enam Noor, is a cloud-based marketing platform that allows organizations to develop data hubs that promote health plan sign-ups and retention. Prior to founding Insightin Health, Noor cofounded Insightin Technology, a cloud computing and enterprise software development company, and Desme, an interactive marketing company.

Insightin Health’s campaign automation system can create personalized and rule-based content delivery in real time, with a machine learning- and AI-driven approach that provides recommendations for touchpoints of member communications and activities. The company claims to combine medical, clinical, cognitive, and social determinants of health to recommend the next best action for each health plan member. For example, the platform can encourage sick members to make outbound calls and schedule telehealth or in-person appointments when available.

Insightin Health can also identify social needs at both a member and population level and offer a dashboard visually detailing members’ responses, activities, and challenges. The company cites research from startup HealthMine that found 60% of surveyed Medicare Advantage members say their health plan doesn’t encourage actions to improve health.

“Our proprietary platform and world-class team have put us in a position to transform the way health care is experienced. The continued interest from our investors and customers is a great validation [of] our company’s momentum since our last raise in 2018,” Noor told VentureBeat via email. “With the continued investment we’re putting into our platform, we’re hoping that more and more health insurance providers will be able to communicate with consumers on a one-to-one level. This way, we can get closer to humanizing the health care experience to improve satisfaction, increase engagement, and ultimately improve health.”

Late last year, Insightin Health teamed up with students from the University of Pittsburgh’s Swanson School of Engineering to predict the start of flu season, with the goal of optimizing flu shot timing and reducing health care costs in the process. More recently, Insightin Health launched a solution to support COVID-19 preparedness efforts. It scrubs infection and mortality data, creating a risk scoring model to assess information at a state and county level across the U.S.

According to Noor, Insightin Health has been profitable since launch and experienced 170% year-over-year revenue growth from 2019 to 2020. With over 5 million people on its platform, the company anticipates similar growth from 2020 to 2021.

Blue Venture Fund and Blue Heron Capital co-led the series A round Insightin Health announced today. It brings the company’s total raised to over $ 13 million. Insightin has 45 employees.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Pony.ai raises $100 million more to advance its autonomous vehicle tech

February 7, 2021 Big Data

Self-driving car startup Pony.ai today announced that it raised $ 100 million in an extension of its series C round. The funds bring the company’s total raised to date to over $ 1 billion at a post-money valuation of $ 5.3 billion, up from $ 3 billion as of February 2020.

Some experts predict the pandemic will hasten the adoption of autonomous transportation technologies. Despite needing disinfection, driverless cars can potentially minimize the risk of spreading disease. For example, Pony.ai says it’s delivered more than 15,000 packages of food and health kits in California during the COVID-19 health crises.

Former Baidu chief architect James Peng cofounded Pony.ai in 2016 with Tiancheng Lou, who worked at Google X’s autonomous car project before it was spun off into Waymo. The two aim to build level 4 autonomous cars — cars able to operate without human oversight under select conditions, as defined by the Society of Automotive Engineers — for “predictable” environments such as industrial parks, college campuses, and small towns, with a tentative deployment window of several years from now.

Pony’s full-stack hardware platform, PonyAlpha, leverages lidars, radars, and cameras to keep tabs on obstacles up to 200 meters from its self-driving cars. PonyAlpha is the foundation for the company’s fully autonomous trucks and freight delivery solution, which commenced testing in April 2019 and is deployed in test cars within the city limits of Fremont and Beijing in addition to Guangzhou.

Pony.ai, which has offices in Guangzhou, China and Fremont, California is one of the few companies to have secured an autonomous vehicle testing license in Beijing. In California, it has obtained a robo-taxi operations permit from the California Public Utilities Commission. The only other companies to have secured such a license in California are Cruise, AutoX, Aurora, Voyage, Waymo, and Zoox.

Last October, Pony.ai partnered with Via and Hyundai to launch BotRide, Pony.ai’s second public robo-taxi service after a pilot program (PonyPilot) in Nansha, China. BotRide allowed riders and carpoolers to hail autonomous Hyundai Kona electric SUVs through apps developed with Via, sourcing from a fleet of 10 cars with human safety drivers behind the wheel.

In August, Pony.ai inked an agreement with Bosch to “explore the future of automotive maintenance and repair for autonomous fleets.” Pony.ai and Bosch’s Automotive Aftermarket division in North America plan to develop and test fleet maintenance solutions for commercial robo-taxi programs. Pony.ai says it began piloting a maintenance program with Bosch in the San Francisco Bay Area in early July.

Among other potential advantages, autonomous driving promises the continuous operation of fleets and reduction of downtime. According to a 2017 McKinsey report, robo-taxis could reduce a fleet operator’s total cost of ownership by 30% to 50% compared with private vehicle ownership and by about 70% compared to shared mobility, significantly disrupting the market. But robo-taxis will need vastly different maintenance infrastructure than cars, in part because they might lack regular monitoring, have only minutes between passengers, and sport expensive, sensitive, and unconventional parts like lidar sensors.

Pony.ai has competition in Daimler, which in summer 2018 obtained a permit from the Chinese government that allows it to test self-driving cars powered by Baidu’s Apollo platform on public roads in Beijing. And startup Optimus Ride built out a small driverless shuttle fleet in Brooklyn. Waymo, which has racked up more than 20 million real-world miles in over 25 cities across the U.S. and billions of simulated miles, in November 2018 became the first company to obtain a driverless car testing permit from the California Department of Motor Vehicles (DMV). Other competitors include Tesla, Aptiv, May Mobility, Cruise, Aurora, Argo AI, Pronto.ai, and Nuro.

However, only Nuro, Waymo, and Cruise, and Argo rival Pony.ai’s fundraising. With the exception of Argo, all have raised more than $ 3 billion in venture capital at valuations ranging from $ 7.5 billion (Argo) to $ 175 billion (Waymo).

Fortunately for Pony.ai, it has partnerships with Chinese state-owned auto group FAW and GAC Group, a Guangzhou-based automobile maker, to develop level 4 robo-taxi vehicles. It also has a joint collaboration with On Semiconductor to prototype image sensing and processing technologies for machine vision. And Pony.ai has driven over 1.5 million autonomous kilometers (or about 932,056 miles) as of year-end 2019, putting it within striking distance of Yandex (2 million miles) and Baidu (1.8 million miles).

Brunei Investment Agency, Brunei’s sovereign wealth fund, and CITIC Private Equity Funds Management participated in the series C extension. Previous and existing investors in Pony.ai include Toyota, video game publisher Beijing Kunlun Wanwei, Sequoia Capital China, IDG Capital, and Legend Capital. The Ontario Teachers’ Pension Plan Board’s Teachers’ Innovation Platform led this latest round, with participation from Fidelity China Special Situations PLC, 5Y Capital, ClearVue Partners, Eight Roads, and others.

“Technological innovation is constantly transforming traditional industries, and autonomous driving will change traditional ways of transportation in the near future,” a CPE spokesperson told VentureBeat via email. “CPE believes that Pony.ai will lead this trend, and will help it seize the opportunity for faster growth.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Pinecone emerges from stealth with $10 million to accelerate AI workloads

January 28, 2021 Big Data

Pinecone Systems, a company developing a database platform for AI workloads, today emerged from stealth with $ 10 million in funding. A company spokesperson told VentureBeat the funds will be put toward customer acquisition and product R&D as Pinecone’s platform launches for self-onboarding.

AI models take data, whether images, sentences, or user behavior, and convert them into complex collections of numbers called vectors. Using a machine learning model is often a question of finding out which vectors are nearest or most similar to others, but existing ways of doing this can be slow and inaccurate.

Pinecone aims to make AI queries faster, more accurate, cheaper, and easier to use with fully managed serverless vector database technology. Founded by developers from Facebook and Google — along with CEO Edo Liberty, who ran Yahoo’s Scalable Machine Learning Platforms group and later Amazon AI Labs — Pinecone’s database supports deployments of personalization, semantic text search, image retrieval, data fusion, deduplication, recommendation, anomaly detection, and other real-time applications.

Developers can use Pinecone to configure and launch services with custom machine learning models, transformations, and rankers. They’re also able to add or update data in batches or streams; data is turned into vectors and the index is updated in real time. Moreover, they can monitor and track deployments for operational health and support queries to find similar or top-ranking items.

Liberty claims Pinecone, which connects to third-party data sources, can scale to billions of high-dimensional vectors while keeping latencies below 50 milliseconds for queries, updates, and embeddings with horizontal container distribution. Pinecone ostensibly has a real-time indexing speed 30 times higher than open source libraries.

“One of the largest retailers in the world reports using Pinecone to serve real-time shopping recommendations based on their own deep learning models. They saw an immediate 18.5% lift in revenue per recommendation compared with their previous solution,” a Pinecone spokesperson told VentureBeat. “Pinecone’s database is 100% serverless and API-driven, which means customers always have the computing resources they need, when they need them, without having to worry about infrastructure or maintenance.”

Wing Venture Capital led the seed investment in Sunnyvale, California-based Pinecone announced today.

“The modern enterprise is built on data and powered by AI,” Peter Wagner, a founding partner at Wing Venture Capital and the newest member of Pinecone’s board of directors, said in a statement. “The data cloud has emerged as its foundation with the ascendance of Snowflake. Pinecone is poised to unleash data teams and their ML-based applications in a similar fashion.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Kili Technology unveils data annotation platform to improve AI, raises $7 million

January 26, 2021 Big Data

Poor or uncategorized raw data can be a major impediment for enterprises that want to build high-quality artificial intelligence that has a meaningful impact on their business. Organizing unstructured data such as images and audio can present a particularly daunting obstacle in this regard.

Today, Paris-based Kili Technology unveiled its service that allows enterprises to annotate raw data such as video, drone aerial images, contracts, and emails. The company’s collaborative platform enables employees to make the data labeling process more efficient.

The company also said it had raised its first outside funding in a round led by Serena Capital and e.ventures, which invested along with business angels such as Datadog CEO Olivier Pomel, Algolia CEO Nicolas Dessaigne, and PeopleDoc founders Stanislas de Bentzmann and Gus Robertson. After a fast start, the company has ambitious plans to expand its international reach.

“The mission is super simple,” said Kili CEO and cofounder François-Xavier Leduc. “To build AI, you need three things. You need the computing power that you can buy easily on Amazon, you need an algorithm that is available as open source, and you need training sets. We are making the bridge between the raw data and what is required to build AI at scale for companies. Our mission is to help our customers turn this raw data into training data so that they can scale AI applications on their internal challenges to solve their issues.”

The company is part of a fast-moving and competitive data annotation sector. Dataloop last year raised $ 16 million for its data annotation tools. SuperAnnotate raised $ 3 million for its AI techniques that speed up data labeling. And earlier last year, IBM released annotation tools that tap AI to label images.

All of these companies have identified similar issues with developing high-quality AI: Getting data that can be readily processed to train AI. According to Kili, 29,000 Gigabytes of unstructured data are published every second, but much of it remains useless when it comes to training AI.

Founded in 2018 by Leduc and CTO Édouard d’Archimbaud, Kili offers a stable of experts to complement a company’s internal teams and help accelerate the annotation process.

Kili builds on work d’Archimbaud did while at BNP Paribas, where he ran the bank’s artificial intelligence lab. His team was trying to build models for processing unstructured data and ended up creating their own tools for data annotation.

Kili’s system, as d’Archimbaud explained, relies on a basic concept, similar to tagging people in a photo on Facebook. When users click on an image, a little box pops up so they can type in a name and attach a label to the image. Kili uses AI to allow enterprises to take this process to an industrialized scale to create higher-quality datasets.

“Before, people were thinking that AI was about algorithms, and having the most state-of-the-art algorithm,” d’Archimbaud said. “But it’s not the case anymore. Today, AI is about having the best data to train models.”

Kili’s cofounders bootstrapped the company for its first two years. But Kili has already attracted large customers in Europe, China, and the U.S. across a variety of industries.

As Kili gained more traction, the confounders decided to raise their first outside round of funding to accelerate sales and marketing. But they also intentionally sought out business angels who worked in other data-related startups to help provide practical guidance on building a global company to seize a growing opportunity.

“Two years ago, the data annotations market was estimated to be $ 2 billion in four years,” Leduc said. “And now it’s estimated to be $ 4 billion. It’s going to go fast, and it will definitely be huge. And it’s a new category. So there is an opportunity to be a worldwide leader. Today, we are positioned to be one of them.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Soci raises $80 million to power data-driven localized marketing for enterprises

January 22, 2021 Big Data

Soci, a platform that helps brick-and-mortar businesses deploy localized marketing campaigns, has raised $ 80 million in a series D round of funding led by JMI Equity.

The raise comes at a crucial time for businesses, with retailers across the spectrum having to rapidly embrace ecommerce due to the pandemic. However, businesses with local brick-and-mortar stores will still be around in a post-pandemic world. By focusing on their “local” presence, including offering local pages (e.g. Facebook) and reviews (e.g. Google and Yelp), businesses can lure customers away from Amazon and its ilk. This is where Soci comes into play.

Founded in 2012, San Diego-based Soci claims hundreds of enterprise-scale clients, such as Hertz and Ace Hardware, which use the Soci platform to manage local search, reviews, and content across their individual business locations. It’s all about ensuring that companies maintain accurate and consistent location-specific information, which can be particularly challenging for businesses with thousands of outlets.

“For multi-location enterprises, the ability to connect with local audiences across the most influential marketing networks like Google, Yelp, and Facebook was critical to keeping their local businesses afloat through the pandemic,” Soci cofounder and CEO Afif Khoury told VentureBeat.

Moreover, Soci offers analytics that can help determine which locations are performing best in terms of social reach and engagement, integrating with all the usual touchpoints where businesses typically connect to customers, such as Facebook, Yelp, and Google.

“Soci is now housing and analyzing all of the most critical marketing data from every significant local marketing channel, such as search, social, reviews, and ads,” Khoury continued.

Soci raises $80 million to power data driven localized marketing for enterprises

Above: Soci: Local marketing data

Soci had previously raised around $ 35 million, and with its latest cash injection the company plans to double down on sales and M&A activity. Its lead investor hints at the direction Soci is taking, given that JMI Equity is largely focused on enterprise software companies like financial planning platform Adaptive Insights, which Workday acquired a few years ago for more than $ 1.5 billion.

Looking to the future, Soci said it plans to enhance its data integrations, spanning all the common business tools used by enterprises, to build a more complete picture that meshes data from the physical and virtual worlds.

“As Soci continues to integrate with other important ecosystems and technologies such as CRM, point-of-sale, and rewards programs, it will begin to effectively combine online and offline data and deliver an extremely robust customer profile that will enrich the insights we provide and enable much more effective marketing and customer service strategies,” Khoury said.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

up-to-date information on the subjects of interest to you
our newsletters
gated thought-leader content and discounted access to our prized events, such as Transform
networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Tag Archives: Million

Quality Match raises $6 million to build better AI datasets

VentureBeat

TripleBlind raises $8.2 million for its encrypted data science platform

VentureBeat

FundGuard raises $12 million to help manage assets with AI

VentureBeat

Peak.AI raises $21 million to drive enterprise AI adoption

VentureBeat

Insightin Health raises $12 million for AI that uses big data to guide patients’ decisions

VentureBeat

Pony.ai raises $100 million more to advance its autonomous vehicle tech

VentureBeat

Pinecone emerges from stealth with $10 million to accelerate AI workloads

VentureBeat

Kili Technology unveils data annotation platform to improve AI, raises $7 million

VentureBeat

Soci raises $80 million to power data-driven localized marketing for enterprises

VentureBeat

Recent Posts

Categories

Archives