• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Tag Archives: raises

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

March 29, 2021   Big Data

From TikTok to Instagram, how’s your creative working for you?

In digital marketing, there’s no one-size-fits-all. Learn how data can make or break the performance of creative across all platforms.

Register Now


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.

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

Read More

Avvir raises $10M for AI that spots construction site errors

March 27, 2021   Big Data

From TikTok to Instagram, how’s your creative working for you?

In digital marketing, there’s no one-size-fits-all. Learn how data can make or break the performance of creative across all platforms.

Register Now


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


Avvir, a startup using laser scans and AI to catch construction mistakes, today announced that it raised $ 10 million in a funding round led by Trust Ventures. The New York-based startup, which is valued at $ 40 million, says it’ll use the funds to expand its workforce while improve its technology platform.

Mistakes often prove to be costly in the construction industry. According to a study commissioned by Autodesk, 5% of construction professionals’ time is spent on nonproductive activities including looking for project information, conflict resolution, and dealing with errors and rework. It’s estimated that these activities cost the U.S. construction sector alone over $ 177 billion in labor in 2018.

Avvir, which was founded in 2017 by CEO Raffi Holzer and CTO Tira Odhner, provides a platform that monitors construction progress for budget and schedule overruns. Customers send Avvir fabrication models and then deploy mobile lasers that can scan up to 30 square kilometers in an hour, even while construction is ongoing. Custom computer vision algorithms compare photographs and the scans with the building plans and catch errors and delays. Avvir claims it can identify construction problems like unlevel floors to one-eighth inch of accuracy.

Avvir can refresh bills of materials with site laser scans to document real-world modifications as they occur. The platform automatically pushes updates to models and enables monitoring of the percent completion of projects. Avvir tracks the progress on systems with color renderings that show which portions of the building geometry are finished and which aren’t — actual progress can be mapped against the schedule to highlight where the timeline might be off target.

 Avvir raises $10M for AI that spots construction site errors

Avvir competes with companies including Swapp, which automates construction planning using AI. Another rival is SiteAware, a startup that tracks construction zone progress using a combination of drones and machine learning systems. Buildots, OnSiteIQ, Disperse, OpenSpace, and Indus.ai are among the other firms vying for a slice of the the AI in construction market, which is projected to be worth $ 2.31 billion by 2026, Mordor Intelligence reports.

But according to Holzer, Avvir, which has 18 full-time employees, has gone from $ 300,000 in annual recurring revenue to $ 1.4 million and roughly 12 customers and partners — including robotics firm Boston Dynamics — in less than 12 months. He expects that number to reach $ 4.4 million by 2022.

“Facility managers and building owners typically [don’t] have an accurate set of building plans off of which they could manage or plan renovations of their built assets,” Holzer told VentureBeat in an interview over email. “Avvir plans to become a true construction information platform, taking unstructured information from the field, structuring and analyzing it, and then enabling clients to take action on it either within Avvir or in any of the software tools they already use.”

Tekfen Ventures, Khosla Ventures, and MetaProp also participated in Avvir’s fundraising round announced today. It brings the company’s total raised to date to nearly $ 15 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 2021: Learn More
  • networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

Read More

OctoML raises $28M for machine learning deployment optimization

March 17, 2021   Big Data
 OctoML raises $28M for machine learning deployment optimization

The power of audio

From podcasts to Clubhouse, branded audio is more important than ever. Learn how brands are increasing customer loyalty and personalization with these best practices.

Register Now


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


It’s well known that most businesses face challenges deploying AI in production, and that’s led to the rise of markets serve those needs. In the latest news of advance for such a company, OctoML today raised a $ 28 million Series B funding round.

OctoML helps businesses accelerate and deploy AI and relies on the open-source technology Apache TVM machine learning compiler framework. The funding will be used for OctoML to continue building out products like the Octomizer platform and invest in the company’s go-to-market strategy and customer service teams.

“We started the TVM work as a research project at the University of Washington about five years ago, and all the key people in the project are part of, they all got their PhDs and are part of the company now,” OctoML CEO and cofounder Luis Ceze told VentureBeat. “We’re focused on making inference fast on any hardware, and support cloud and edge deployments.” 

Last month, OctoML joined more than 20 startups who have banded together to create the AI Infrastructure Alliance, an effort involving startups like Algorithmia and Determined AI for interoperability between the offerings from AI startups and advance alternatives to popular cloud AI services.

The $ 28 million funding was led by Addition Capital led the round with participation from existing investors Madrona Venture Group and Amplify Partners.

OctoML has raised $ 47 million to date. A $ 3.9 million seed funding round was held in October 2019.

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

Read More

Delivery startup Refraction AI raises $4.2M to expand service areas

March 9, 2021   Big Data

The power of audio

From podcasts to Clubhouse, branded audio is more important than ever. Learn how brands are increasing customer loyalty and personalization with these best practices.

Register Now


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


Refraction AI, a company developing semi-autonomous delivery robots, today announced that it raised $ 4.2 million in seed funding led by Pillar VC. Refraction says that the proceeds will be used for customer acquisition, geographic expansion, and product development well into the next year.

The worsening COVID-19 health crisis in much of the U.S. seems likely to hasten the adoption of self-guided robots and drones for goods transportation. They require disinfection, which companies like Kiwibot, Starship Technologies, and Postmates are conducting manually with sanitation teams. But in some cases, delivery rovers like Refraction’s could minimize the risk of spreading disease. Recent market reports from Allied Market Research and Infiniti  estimate that annual growth in the last-mile delivery sector over the next 10 years will exceed 14%, with the autonomous delivery segment projected to grow at over 24%, from $ 11.9 billion in 2021 to more than $ 84 billion globally by 2031.

Launched in July 2019, Refraction was cofounded by Matt Johnson-Roberson and Ram Vasudevan, both professors at the University of Michigan. Working alongside several retail partners, people within a few-mile radius can have orders delivered by Refraction’s REV-1 robot. After customers order through a dedicated website, Refraction’s employees load the vehicles at the store, and recipients receive text message updates, along with a code to open the robot’s storage compartment when it arrives.

REV-1, which is approximately the size of an electric bicycle and is legally categorized as an ebike, weighs approximately 100 pounds and stands roughly 4 feet tall, including its three wheels. It travels an average 10 to 15 miles per hour with a very short stopping distance, and the compartment holds about six bags of groceries.

 Delivery startup Refraction AI raises $4.2M to expand service areas

REV-1’s perception system comprises 12 cameras, in addition to redundant radar and ultrasound sensors — a package the company claims costs a fraction of the lidar sensors used in rival rovers. The robot can navigate in inclement weather, including rain and snow, and it doesn’t depend on high-definition maps for navigation.

Prior to a partnership with Ann Arbor, Michigan-based Produce Station, REV-1 had been delivering exclusively from Ann Arbor restaurants, including Miss Kim and Tio’s Mexican Cafe, during lunchtime as part of a three-month pilot. The company charges the restaurant a flat $ 7.50, and Refraction’s over 500 customers pay a portion of that fee if the business chooses. (Tips go directly to Refraction’s partners.)

As of May 2020, Refraction had eight robots running in Ann Arbor, and it expects to have over 20 within the next few weeks. The latest investment brings its total raised to date to over $ 10 million.

“Last-mile delivery is the quintessential example of a sector that is ripe for innovation, owing to a powerful confluence of advancing technology, demographics, social values and consumer models. Conventional approaches have left businesses and consumers with few choices in this new environment as they struggle to keep pace with surging demand — burdened by the costs, regulatory, and logistical challenges of a legacy infrastructure,” Refraction CEO Luke Schneider, who took the helm in fall 2020, said in a press release. “Our platform uses technology that exists today in an innovative way, to get people the things they need, when they need them, where they live. And we’re doing so in a way that reduces business’ costs, makes roads less congested, and eliminates carbon emissions.”

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

Read More

ETL company Airbyte raises $5.2M to integrate open source data

March 3, 2021   Big Data
 ETL company Airbyte raises $5.2M to integrate open source data

The power of audio

From podcasts to Clubhouse, branded audio is more important than ever. Learn how brands are increasing customer loyalty and personalization with these best practices.

Register Now


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


Airbyte today announced it has raised $ 5.2 million in seed funding as part of an effort to make open source tools for managing and integrating data more accessible.

The company, which offers an open source extract transform and load (ETL) tool used to create data pipelines, is now seeking to further democratize that process. This includes, for example, building complementary open source tools to govern and secure data, Airbyte cofounder and CEO Michel Tricot told VentureBeat.

Internal IT teams have historically employed ETL tools to move data between repositories. In recent years, however, data analysts have been using these tools to load data into warehouses without requiring any intervention on the part of an IT team.

All of those tools are licensed by individuals. Airbyte plans to eventually provide versions of its tools licensed by organizations, along with an option to access those tools via a service hosted by Airbyte. Tricot said the company is also planning a managed integration service. “We won’t be focusing on monetization until 2022,” he said.

Accel led the current round of funding, with participation from Y Combinator; 8VC; Segment cofounder Calvin French-Owen; former Cloudera GM Charles Zedlewski; Datavant cofounder and CEO Travis May; Machinify president Alain Rossmann; and Auren Hoffman, cofounder and CEO of LiveRamp and CEO of Safegraph.

As of the end of January, more than 600 organizations are using Airbyte ETL tools, including Safegraph, Dribbble, Mercato, GraniteRock, Agridigital, and Cart.com. Many of those organizations are attracted to Airbyte because they don’t have to wait for a provider of commercial ETL tools to create connectors for various data sources. Instead, the community collaboratively builds and supports the connectors it deems most critical, Tricot said. The community has thus far certified 50 connectors. Those connectors are encapsulated Docker containers, which enables them to be deployed on any platform.

ETL processes, along with other classes of data preparation tools, are being reevaluated as organizations increasingly realize that the quality of any AI model they build is dependent on how reliable the data used to train machine learning algorithms is. Data scientists also want to be able to easily update data needed to retrain models as business conditions evolve, which usually entails having more direct control over what data sources are employed to train those models.

As critical as control over any dataset may be, data scientists are discovering that much of the data stored within enterprise systems is not all that consistent or reliable. Data science teams can easily find themselves spending more time addressing data plumbing issues than they do constructing AI models. Successfully building an AI model, as a consequence, can often require months of time and effort.

ETL tools are not going to resolve that issue on their own. But the easier data becomes to manipulate, the less time it will take to build an AI model and then continuously maintain it as new data sources become available.

It’s not clear what impact the availability of open source ETL tools is having on providers of the rival commercial offerings some organizations have been employing for decades. But at a time when many organizations are under pressure to reduce the total cost of IT, the allure of open source software has proven undeniable.

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

Read More

Quality Match raises $6 million to build better AI datasets

March 2, 2021   Big Data
 Quality Match raises $6 million to build better AI datasets

Data: Meet ad creative

From TikTok to Instagram, Facebook to YouTube, and more, learn how data is key to ensuring ad creative will actually perform on every platform.

Register Now


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

Read More

TripleBlind raises $8.2 million for its encrypted data science platform

March 1, 2021   Big Data
 TripleBlind raises $8.2 million for its encrypted data science platform

Data: Meet ad creative

From TikTok to Instagram, Facebook to YouTube, and more, learn how data is key to ensuring ad creative will actually perform on every platform.

Register Now


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

Read More

FundGuard raises $12 million to help manage assets with AI

February 23, 2021   Big Data
 FundGuard raises $12 million to help manage assets with AI

Data: Meet ad creative

From TikTok to Instagram, Facebook to YouTube, and more, learn how data is key to ensuring ad creative will actually perform on every platform.

Register Now


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

Read More

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

February 17, 2021   Big Data
 Peak.AI raises $21 million to drive enterprise AI adoption

Data: Meet ad creative

From TikTok to Instagram, Facebook to YouTube, and more, learn how data is key to ensuring ad creative will actually perform on every platform.

Register Now


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

Read More

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

February 10, 2021   Big Data
 Insightin Health raises $12 million for AI that uses big data to guide patients’ decisions

Data: Meet ad creative

From TikTok to Instagram, Facebook to YouTube, and more, learn how data is key to ensuring ad creative will actually perform on every platform.

Register Now


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

Read More
« Older posts
  • Recent Posts

    • Accelerate Your Data Strategies and Investments to Stay Competitive in the Banking Sector
    • SQL Server Security – Fixed server and database roles
    • Teradata Named a Leader in Cloud Data Warehouse Evaluation by Independent Research Firm
    • Derivative of a norm
    • TODAY’S OPEN THREAD
  • Categories

  • Archives

    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited