DataRobot Raises $21M Series A

Even before launching its full-scale service that cranks out algorithms for use by data scientists, DataRobot Inc. has nabbed a $21 million Series A round at a $60 million pre-money valuation, Venture Capital Dispatch has learned.

Data scientists are in high demand as companies seek to glean insights from the masses of data they collect.

DataRobot


 is a predictive analytics platform used by data scientists to help construct and deploy predictive models. Because the deployment process can drag on for months, models can grow obsolete by the time it takes them to be deployed. This is a critical pain point in data science, and one that DataRobot seeks to mitigate by speeding up the process.
DataRobot builds, tests and refines hundreds of models to help data scientists find the ones best suited for their data. The investigation is transparent, allowing users to trace and verify the validity of its results. Platform users can also code, train, test and compare their own models on the platform.
DataRobot also finds key drivers in business metrics, identifies key words and phrases within unstructured text, and can output basic visualizations of its findings.
As DataRobot is built to enhance rather than replace the work of a data scientist, DataRobot users need to have at least a basic understanding of data science methodologies and business intelligence tools like Tableau or Excel.

Features and Capabilities

• Predictive Analytics: Yes
• Automatic: Partially, targets need to be set manually, the system cannot generate insights without guidance
• Data types: Structured only, except for a basic identification of key phrases within unstructured text
• Natural Language Generation: Yes, concisely
• Professional Training: Yes, “DataRobot University” trains customers in data science
• Transparency: Users can track DataRobot’s model selection process, from its features to its algorithms
• Data Visualization: Yes, as a supporting feature
• Delivery Model: Cloud and on-Premise
• Implementation Time: Details unavailable

Loom Systems-Raised $6M on May 3, 2017

Automated Data Science, DataRobot, Loom Systems, Narrative Science, Storytelling

Loom Systems


Loom delivers an advanced AI-powered analytics platform to predict and prevent problems in the digital business. Loom stands alone in the industry as an AI platform requiring no prior math knowledge from its users, leveraging your existing staff to succeed in the digital era.
Loom Systems automatically ingests and analyzes all types of logs and metrics, learns their unique behavior over time, detects anomalies and trends, and reports these along with the root cause.
The entire cycle is fully automatic, requiring no data pre-processing or manual setting of parameters and thresholds. Incidents are accompanied by recommended resolutions from a proprietary resolutions database, which also includes internal resolutions filled in by the platform users.
Built for low-touch operational simplicity and usability, our solution empowers IT, DevOps, System Admins, NOC teams and Security specialists by transforming reactive users into proactive power-users.

Companies today are turning to data science to extract actionable insight from their big data. But data scientists are scarce and are expected to grow even scarcer as this demand rises.
Software companies are looking to bridge this human resource gap by weaving mathematical and statistical capabilities into automated data science tools.
But data science is an expansive field, matched in scope by the diverse methodologies developed to derive insight from big data.
Today’s we’ll be examining three radically different and technologically visionary solutions.


The first is DataRobot, a platform designed to help data scientists expedite their predictive model selection and deployment. It allows data scientists to deploy their models with ease and speed. DataRobot seeks to address the cross-industry shortage of data scientists by enabling data scientists to accomplish more in less time.
The second is Quill by Narrative Science. Quill transforms metric data into natural language, narrative-driven and highly customizable reports.The company has recently celebrated its 6^th year and 10th patent, a testimony to its (relative) longstanding in the field of AI-powered predictive analytics.
The third solution comes from Loom Systems. Loom Systems seek to radically simplify how organizations derive insight from their data by fully automating humans’ analytical process and combining it with computational speed, accuracy and diligence. It presents insights and recommendations in simple, actionable reports anyone can read and act on.
These automation tools utilize artificial intelligence and machine learning methodologies to provide services to a wide variety of industries, agnostically deriving insights from unlimited amounts of datasets. Happily, this is more or less where the similarities end and each tool’s unique value comes into play.

DataRobot
 is a predictive analytics platform used by data scientists to help construct and deploy predictive models. Because the deployment process can drag on for months, models can grow obsolete by the time it takes them to be deployed. This is a critical pain point in data science, and one that DataRobot seeks to mitigate by speeding up the process.
DataRobot builds, tests and refines hundreds of models to help data scientists find the ones best suited for their data. The investigation is transparent, allowing users to trace and verify the validity of its results. Platform users can also code, train, test and compare their own models on the platform.
DataRobot also finds key drivers in business metrics, identifies key words and phrases within unstructured text, and can output basic visualizations of its findings.
As DataRobot is built to enhance rather than replace the work of a data scientist, DataRobot users need to have at least a basic understanding of data science methodologies and business intelligence tools like Tableau or Excel.


Features and Capabilities

• Predictive Analytics: Yes
• Automatic: Partially, targets need to be set manually, the system cannot generate insights without guidance
• Data types: Structured only, except for a basic identification of key phrases within unstructured text
• Natural Language Generation: Yes, concisely
• Professional Training: Yes, “DataRobot University” trains customers in data science
• Transparency: Users can track DataRobot’s model selection process, from its features to its algorithms
• Data Visualization: Yes, as a supporting feature
• Delivery Model: Cloud and on-Premise
• Implementation Time: Details unavailable

Narrative Science- has raised over $32 million

With over 22 years of experience in a variety of industries including Retail and Financial Services, Chetan leads product strategy, development and delivery at Narrative Science. Previous to Narrative Science, Chetan held the role of Chief Product Officer at ShopperTrak, a retail analytics company that was sold to Tyco International in January 2016. Chetan’s past accomplishments include launching new business units, transforming product offerings and leading large teams at companies such as Accenture, Capgemini, and CCC Information Services. Chetan holds a Bachelor’s degree in Biomedical and Electrical Engineering degree from Duke University.

Based in Chicago, Narrative Science is a rapidly growing artificial intelligence company with over 70 enterprise customers. Incorporated in 2010, Narrative Science has 90 employees and has raised over $32 million from leading investors Battery Ventures, Sapphire Ventures, In-Q-Tel, Jump Capital and USAA.
AI Business was curious to know more about how Narrative Science technology enables an AI-powered business?
One opportunity that Artificial intelligence (AI) presents the opportunity to augment people’s workloads and intelligence by automating basic tasks so that individuals can focus on higher-value work. From this perspective, Narrative Science enables an AI-powered business because our advanced natural language generation platform, Quill, transforms data into narratives therefore alleviating the time-consuming task of analyzing data and communicating the relevant insights. Advanced NLG is a subfield of artificial intelligence.

For example, our technology is used to automate the generation of data-driven, personalised customer communications at scale; automate the narrative portion of required reports mandated by regulators and internal compliance procedures and has been integrated with BI platforms so users can immediately gain insight from their data by transforming it into intelligent narratives.
Powered by Artificial Intelligence, Narrative Science uses Quill, an advanced natural language generation platform for enterprise organisations that goes beyond reporting the numbers, creating perfectly written, meaningful narratives for any intended audience.




Quill, an advanced natural language generation platform, goes beyond reporting the numbers and creates perfectly written, meaningful narratives for any intended audience. Chetan Ghai outlines a couple of key enterprise use cases for the ‘Quill solution’:


“Quill can be applied in many ways but a few main areas that we’re focused on right now include improving operational efficiency. Employees spend countless hours gathering data, analyzing it, interpreting insights, and then writing and sharing the results. Quill reduces the time associated with these manual processes by automatically generating narratives that are relevant to each recipient. Our work with Credit Suisse HOLT, a full suite of interactive tools that helps investors make investment decisions, is a good example of this. By integrating Quill’s NLG capabilities into HOLT, Credit Suisse has achieved full analyst coverage on the companies profiled within its platform through generating high quality investment research content, freeing its teams to focus on more clients and higher-level types of content. Another way that our technology improves operational efficiency is our work with asset management firms like Franklin Templeton. Quill enables Franklin Templeton’s global marketing team to extensively scale standard fund reporting coverage and frequency and lets Franklin Templeton’s team of investment experts focus on more complex analysis and forward-looking perspectives for customers”.


“Our technology also helps companies enhance customer engagement. E-Commerce companies use Quill to automatically write engaging, variable product descriptions for their websites that include key SEO-friendly terms and that can also be tailored for mobile or desktop consumption.  So for example, apparel retailers feed their SKU data into Quill and automatically generate an detailed and visual descriptions about the retailer’s available inventory. With Quill-generated descriptions, our clients attract online visitors with unique content that increases traffic and improves SEO ranking”.
“We also help companies with their ability to meet regulatory compliance. Organisations struggle to keep up with the growing reporting needs mandated by increasing regulatory requirements and internal compliance procedures. With Quill, organisations automate the writing of regulatory reports, increasing transparency in analytical processes and enabling humans to review that content instead of creating it”.


As with many companies implementing artificial intelligence into their business, there can be challenges to overcome. We asked Chetan what challenges Narrative Science have faced when looking to implement solutions in the enterprise:

“One of the most common challenges for any company building technology driven by artificial intelligence is the customer adoption curve. While AI technologies have been around for many years, we’re finally at a point where they are commercially viable, available and practical. It’s early days, though, and enterprises are still educating themselves on how the technology can fit into their business operations”.


“Finding that first use case can be a bit of a challenge as well.  We have mentioned some examples of where our technology has driven value.  One of the aspects of our approach is that we augment our technology with a services team whose role is to provide guidance to enterprise customers around the right use case and assist in implementing them. Our work with Deloitte Catalyst is a good example of this approach. This service has been a key part of our recent success”.

Quill Narrative Science | Natural Language Generation Technology

Turn your data into better decisions with Quill

We created Quill to help you realize the untapped potential of your data—to make it easier for more people in your enterprise to access and understand it, and make it an actionable asset. Quill transforms data into automated, human-sounding Intelligent Narratives that empower your people with insights to improve every aspect of your business.

https://narrativescience.com/Platform

Asset management commentary creation cut from weeks to seconds
19 May 2016, London: Vermilion Software, a global supplier of client reporting and communications software for the financial asset management industry, and Narrative Science, the leader in advanced natural language generation (Advanced NLG) for the enterprise, today announce a game-changing new partnership that for the first time enables asset management firms to fully automate the writing of portfolio commentary and embed this capability directly within a client reporting suite.
The commentary generation capability powered by Quill, the Advanced NLG platform offered by Narrative Science, will be integrated within the Vermilion Reporting Suite (VRS). Quill writes portfolio commentary in seconds, automatically customised to adhere to a firm’s brand guidelines. In this way Quill serves to dramatically decrease costs stemming from a process that traditionally takes firms a few weeks to complete.
“Bottlenecks in client reporting workflow stem from a variety of sources and an integration like this solves more than one challenge,” said Denise Valentine, senior analyst of Aite Group. “Technology firms are increasingly partnering to add greater value to clients to help save time, resources, and costs. These partnerships are becoming game-changers for the industry, in this case empowering companies to fully automate their reporting function.”
The integration provides a range of benefits including the ability to:

• Fully streamline the portfolio commentary process – creating, gathering and distributing client commentary is greatly enhanced by placing all client information needs in one location.
• Exponentially scale reporting coverage and frequency – end-to-end automation addresses the needs of high volume or rapidly scaling investment management businesses.
• Enhance employee productivity – firms can cut commentary creation and reporting time from weeks to seconds, freeing up valuable staff to focus on higher-value tasks.

“This fully integrated offering will be invaluable to our joint clients as writing portfolio commentary is a universal pain point in the reporting process,” said Ben McCormack, Senior Vice President, Client Services of Vermilion Software Inc. “After licensing the commentary generation capability, clients will experience immediate return, be positioned to deliver compliant, high-quality client experiences and be equipped for rapid growth.”
“This partnership represents a milestone for both companies and a major innovation for the asset management industry,” said Nick Beil, COO of Narrative Science. “Embedding Quill into Vermilion’s Reporting Suite offers our clients a unique competitive advantage – the ability to get to market faster by fully automating the processes around commentary creation and distribution.”

Automated Insights' CEO Robbie Allen

"The writing process has largely been untouched," Automated Insights' CEO Robbie Allen tells us over the phone. "The way that you write now is not much different than how you did it 30 years ago." It's isn't as much about commoditizing writing as it is admitting that so much of what we write is rote and repetitive (as the AP told us in January, no jobs have been lost as a result of its "robot journalists"). Allen said he's interested to see what nonconventional uses people find for it, citing a Game of Thrones battle report generator that some employees made during an internal hackathon


Automated Insights' technology is already being used by companies like the Associated Press and Yahoo to autogenerate data-heavy articles about quarterly earnings, college sports, and even fantasy football recaps. Today, the company is unveiling a public-facing version of its Wordsmith platform for anyone to use. You can sign up for access to the beta, with general availability being planned for sometime in January.
The Wordsmith platform is designed to automatically generate natural language reports based on large data sets. Within each project (defined by the data set, uploaded as a CSV file), you can create multiple "narratives," which are in a sense high-powered Mad Libs. Write the basic structure, swapping key words for the variables available, and then add logic


The key feature of Wordsmith is how it enables you to create branching paths, conditionally adding / modifying certain words, phrases, or entire sections based on the data available. Is a 60-inch TV considered "large," "massive," or "run-of-the-mill"? If Company A misses market expectations by $1.2 million, is that a massive failure, a minor hiccup, or something else entirely? Branches can also be embedded within other branches, allowing for more complexity, and you can choose to "randomize" what phrase is presented at any given time, for sake of variety

Wordsmith's 'robot journalist' has been unleashed

Anyone can  own robot journalist


Wordsmith, a platform that provides so-called robot journalists to organisations, is now available to the public.
The platform, owned by Automated Insights, provides "auto generated, data-heavy articles" on topics such as quarterly earnings and college sports. A beta version is available now on Wordsmith's website, with a full launch expected in January 2016. The technology is already used by companies such as the Associated Press and Yahoo


Wordsmith works by creating "branching paths" -- conditionally adding words, phrases and sections that can be added, modified and removed depending on the article. Users enter data -- such as quarterly figures or sports results -- around which these branches are built. This story structure, once created, can be used as a template for an unlimited number of articles. All users have to do is enter new data to create a unique story.


 Robot  never replicate the style of a human


But the technology hasn't been without its critics. NPR ran a feature in which they pitted their White House correspondent against Wordsmith -- while technology beat human on speed, the human written article was, according to NPR listeners who voted online, richer and more engaging. Some argue a robot journalist could never replicate the style of a human journalist in terms of style or insight. But Automated Insights argues that Wordsmith can be stylistically modified so stories can be written as if "a person wrote each one of them individually."


Wordsmith isn't just limited to articles, though


"We focus on personalised content," said Automated Insights CEO Robbie Allen in a press release. "Instead of writing one story and hoping a million people read it, Wordsmith can create a million stories targeted at each individual user and their preferences. It’s a story that is totally unique to each user because it is powered by their data."


Wordsmith isn't just limited to articles, though -- it can also produce client reports, financial summaries and product descriptions.
Journalists nervous about being out of a job needn't worry, though -- The Associated Press, which has published more than 3,000 financial reports each quarter using the system, claimed that 'robot journalists' had led to zero human job losses.


By Emily Reynolds

Wordsmith platform

Get started today and join the many Fortune 100 enterprises already using Wordsmith’s natural language generation technology to revolutionize their industry


1. Add your data

If you don’t have data yet, don’t worry. You can add a few placeholders to get started

2. Write your template          

Go way beyond plugging values into static sentences using rules and dynamic synonyms

3. Preview your stories

Preview your output and edit your NLG template until you’re happy with the results

4. Publish your stories          

Download content right from the app or publish content anywhere using our API or one of our integrations.