- There are 8,705 startups and companies listed in Crunchbase today who are relying on machine learning for their main and ancillary applications, products, and services.
- 83% of machine learning startups Crunchbase tracks have had just three funding rounds or less with seed, angel and early-stage rounds being the most common.
- Artificial Intelligence-related companies raised $9.3B in 2018, a 72% increase over 2017, according to PwC/CB Insights MoneyTree Report, Q4 2018.
- Artificial intelligence deals increased in Q1, 2019 to 116 deals, up from 104 deals in Q4, 2018 according to the latest PwC/CB Insights MoneyTree Report Q1 2019.
- AI-based marketing patents are the fasting growing global category, reaching a Compound Annual Growth Rate (CAGR) of 29.3% between 2010 and 2018, according to EconSight.
From powering personalized career sites that recommend open positions that are ideal for a given candidate based on their capabilities as eightfold.ai does today, to scaling the complexity and volume of machine learning algorithms, so they’re more accessible as DataRobot does, machine learning startups are taking on many of business’ most significant challenges. AI and machine learning have the potential to create an additional $2.6T in value by 2020 in Marketing and Sales, and up to $2T in manufacturing and supply chain planning according to the McKinsey Global Institute. Please see the latest roundup of machine learning forecasts and market estimates, 2019 for more market data on machine learnings’ exponential growth.
25 Machine Learning Startups To Watch In 2019
Alation – Alation offers a machine learning data catalog to help people find, understand, and trust data across their organizations. They’ve defined their solution to align with the needs of four dominant personas, including Chief Data Officers, Analysts, Stewards, and IT and Engineering. Their Data Catalog is known for its usability and intuitive design. More than 100 organizations, including the City of San Diego, eBay, Munich Re, and Pfizer, have adopted the Alation Data Catalog. Alation is funded by Costanoa Ventures, DCVC (Data Collective), Harmony Partners, Icon Ventures, Salesforce Ventures, and Sapphire Ventures. Alation has raised a total of $82M in funding over four rounds. Their latest funding was raised on Jan 17, 2019, from a Series C round.
Anodot – Capitalizes on the innate strengths of machine learning by continually looking for patterns using constraint-based modeling across the diverse data sets, businesses are relying on to operate daily. Similar to many machine learning startups that capitalize on the technology’s ability to learn continually, Anodot’s AI platform looks to eliminate blind spots in data and quantify root-causes in diverse data sets. Anodot’s Autonomous Analytics platform leverages advanced machine learning techniques to constantly analyze and correlate every business parameter, providing real-time alerts and forecasts, in their context, lowering time to detection and resolution. Anodot raised a total of $27.5M in funding over four rounds. The latest funding came from a Series B round on Dec 19, 2017, from Redline Capital. The following screen from their app is an example of how Anodot provides real-time anomaly detection.
Ablacon – Ablacon is a fascinating startup that has built a premier machine intelligence system to quantitatively and qualitatively understand and treat atrial fibrillation (AF).Their technology visualizes in real-time what is going on in the heart. They compute the electrographic flow, which allows treatment of atrial fibrillation faster, more precisely, and more reliably. Ablacon has raised a total of $21.5M in funding over 1 round. This was a Series A round raised on Apr 30, 2019, with Ajax Health.
Biofourmis – Biofourmis is a fast-growing global digital health tech start-up that is reinventing remote patient monitoring by combining AI, machine learning, and real-time monitoring. Their platform is capable of detecting personalized patterns predictive of a patient’s health condition and can find leading indicators of potential health deterioration. Their Biovitals platform is one of the most sophisticated personalized physiological data analytics engine based on human physiology that formulates personalized health models, resulting in highly optimized post-acute patient monitoring solutions and accurate prediction of patient health deterioration before it happens. They use connected devices and bio-sensors to capture physiological signals and detect anomalies. This AI-empowered continuous monitoring platform alerts medical professionals to intervene days before a critical event. Their RhythmAnalytics™ Platform recently received FDA clearance for AI-based automated interpretation of cardiac arrhythmias. The startup has raised a total of $41.6M in funding over six rounds, the latest being on May 21, 2019, from MassMutual Ventures, and Sequoia Capital India.
Cinnamon – One of the most exciting machine learning startups to watch based on their unique approach to integrating AI techniques and machine learning to streamline the mundane tasks of automating data extraction from unstructured documents, Cinnamon has shown steady progress in the last year gaining new customers and adding new features to their solution. They’re able to attain 85% to 100% accuracy using AI and machine learning techniques alone and are gaining momentum with banking and insurance companies. They’ve succeeded in recruiting over 50 data scientists and have a goal of having 100 on staff by the end of the year. The company is based in Tokyo and Vietnam and is now expanding to the United States. New customers include global credit and financial services provider Japan Credit Bureau, Kansai Electric Power, Showa Denko, and many other large-scale enterprises throughout the Asia-Pacific region. The company’s continued success is also reflected in their winning the Business Innovation Development Award by Japan Creation Association for their work in AI. Cinnamon has raised a total of $17M in funding over six rounds. Their latest funding was raised on Jan 28, 2019, from a Series B round.
Compression.ai – One of the main benefits of machine learning is its innate ability to find anomalies in visual and numerical data. Compression.ai is relying on machine learning to improve the encoding and decoding densities achieved for images, averaging 95% compression rates of a raw image without significantly losing its quality. The algorithm uses deep neural networks to create a representation of the image, a technology the company calls its Machine Learning Visual Extension. The extension creates a compressed representation in an entirely new file format that has intelligence embedded within the file structure. Compression.ai raised one pre-seed round in September 2018, and they are funded by Victory Square and Village Global.
CrowdStrike – Applying machine learning to endpoint detection of IT network threats is how CrowdStrike is differentiating itself in the rapidly growing cybersecurity market arena today. Their Falcon platform stops breaches by detecting all attacks types, even malware-free intrusions, providing five-second visibility across all current and past endpoint activity while reducing cost and complexity for customers. CrowdStrike’s Threat Graph provides real-time analysis of data from endpoint events across the global crowdsourcing community, allowing detection and prevention of attacks based on patented behavioral pattern recognition technology. CrowdStrike has raised a total of $481M in funding over six rounds. Their latest funding was raised on Jun 19, 2018, from a Series E round. Accel, General Atlantic, IVP (Institutional Venture Partners) are the lead investors in the latest round.
Dataiku – Designed and launched their Data Science Studio platform to aggregate the process steps needed to transform raw data into data-driven applications that are easy to maintain. The Studios’ workspace is designed to be intuitive, interactive, and capable of shortening load-prepare-test-deploy cycles required to create data-driven applications. Customers include Unilever, GE, FOX News Group, Palo Alto Networks, SAP/CallidusCloud and many others who use Dataiku to gain higher intelligence and insights from their massive data sets aggregated over decades of operations. By removing roadblocks, Dataiku ensures more opportunity for business-impacting models and creative solutions, allowing teams to work faster and smarter. Dataiku has raised a total of $146.7M in funding over five rounds. Their latest funding was raised on Dec 19, 2018, from a Series C round led by ICONIQ Capital. Dataiku makes sample projects available on their site, and the on Forecasting Sales is worth a look. An example of the workflow created is shown below:
DataRobot – DataRobot is an enterprise machine learning platform designed for broad adoption and usability across the many skill levels in an organization. The platform provides a broad base of algorithms and tools for developing and deploying machine learning and AI projects, including libraries of hundreds of open source machine learning algorithms. A,s of April 2019 DataRobot customers have built over 1 billion models on the Amazon Web Services platform. DataRobot has raised a total of $224.6M in funding over seven rounds. Their latest funding was raised on Oct 24, 2018, from a Series D round from Meritech Capital Partners and Sapphire Ventures. The following is based on an analysis of which Disney movie will be the most significant commercial success. The blog post, The Summer Blockbuster Predictions: Battle of the Disney Favorites, is a fascinating look at how Qlik and DataRobot can be used to solve sophisticated forecasting and prediction problems.
https://www.datarobot.com/
Eightfold.ai – This startup’s energy and enthusiasm to turn concepts into code and products is exceptional. Tracking their customer wins and the depth of features they deliver in each release is impressive. They’re able to provide measurable, scalable results using AI to streamline talent management in a broadening base of companies. Using advanced AI and machine learning techniques, a company founded by former Google and Facebook AI Scientists is showing potential in meeting these challenges. Founders Ashutosh Garg and Varun Kacholia have over 6000+ research citations and 80+ search and personalization patents. What makes Eightfold.ai noteworthy is that it’s the first AI-based Talent Intelligence Platform that combines analysis of publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools, and spreadsheets then creates ontologies based on organization-specific success criteria. Each ontology, or area of talent management interest, is customizable for further queries using Eightfold’s intuitive user interface. Eightfold has raised a total of $51.8M in funding over three rounds. They raised $28M in their Series C round on Apr 24, 2019, with IVP (Institutional Venture Partners). The following is an overview of the Eightfold Talen Intelligence Platform:
H2O.ai – H2O.ai provides an open source machine learning platform that simplifies the development of data-driven smart applications. Data scientists and developers are using the H2O.ai platform to create, test, and scale algorithms that are the foundation of applications. H2O.ai apps are being used today to predict fraud, customer churn, and solve many other complex problems their customers have. Key clients include Cisco, PayPal, and Progressive. H2O.ai has raised a total of $73.6M in funding over five rounds. Their latest funding was raised on Nov 30, 2017, from a Series C round led by Nvidia and Wells Fargo.
HEALTH[at]SCALE – What fascinating about this startup is how it is developing machine learning and artificial intelligence solutions for health care’s most challenging problems, including matching every patient to appropriate treatment by relevant providers at the best time for patients to achieve optimal results. The startup develops products, such as HEALTH[at]SCALE Interception that identifies members within populations and enables selective and targeted early action to reduce this risk. HEALTH[at]SCALE Steerage builds the preferred networks and offers risk-adjusted insights to improve networks over time; HEALTH[at]SCALE Treatment delivers personalized predictions of benefit, harm, and adherence for members across treatment choices and guides the effective use of treatments to improve longitudinal member outcomes. It began operation in June 2015, with its headquarters in Cupertino in California. HEALTH[at]SCALE has raised a total of $16M in funding over 1 round. This was a Series A round raised on May 17, 2019, led by Optum.
Hunters.AI – What makes this startup unique in the rapidly growing field of AI and machine learning-based cybersecurity startups is the approach to provide real-time updates on Attack Intelligence, Hunting AI, and continuous automation with an enterprise’s existing security data. Hunters.AI generates and delivers visualized attack stories allowing organizations to more quickly and effectively identify, understand, and respond to attacks. Hunters.AI has raised a total of $5.4M in funding over 1 round. This was a Seed round raised on May 22, 2019, led by Blumberg Capital and YL Ventures.
Impact Analytics – Capitalizing on the inherent strengths of AI and machine learning to find anomalies, patterns and trends in legacy data and entirely new data sets from recently launched business models is where Impact Analytics makes its most significant contributions. They have a successful track record providing customer analytics, margin improvement, marketing analytics, merchandising optimization, operational improvements, and robotic process automation. They’re known for successful deployments across the retail industry with customer references in banking and financial institutions (BFSI), Consumer Packaged Goods (CPG), Healthcare, Hospitality, and Industrial Manufacturing. Impact Analytics has raised a total of $750K in a Seed round raised on Oct 31, 2016, led by Aarin Capital.
Innovaccer – Innovaccer develops AI- and machine learning-based systems for healthcare organizations, enabling them to integrate complex data across multiple distributed sources and provide valuable insights to healthcare professionals. Innovaccer’s Datashop application includes proprietary modeling algorithms that normalize data and links data across multiple disparate data sources. Innovaccer also provides solutions for care management, referral management, and patient engagement and has raised a total of $54.1M in funding over four rounds. Their latest funding was raised on Jan 16, 2019, from a Series B round led by Microsoft’s venture capital investment firm, M12.
Inspectorio – Inspectorio is a leader in the inspection software industry. Their cloud-based platforms are disrupting quality inspections by increasing productivity, transparency, and efficiency. Like many great solutions, Inspectorio was born from the frustration of three serial entrepreneurs who had to deal with the slow, manual process of quality inspections – while receiving little to no visibility. Today, Inspectorio is the platform used by some of the world’s most recognized retailers and brands, inspection agencies and vendors and factories. Target relies on them today to bring greater transparency to their supply chains. Please see the Forbes article, How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility for additional information on how Inspectorio is using machine learning to revolutionize product inspections, improve supply chain visibility and track & traceability. Inspectorio has raised a total of $13.7M in funding over three rounds. Their latest funding was raised on Jul 11, 2018, from a Series A round led by TechStars. The following is an overview of their platform:
https://www.inspectorio.com/
LogiNext – LogiNext is a fascinating startup using AI and machine learning to bring more significant innovation to field workforce and logistics optimization. The startup offers field workforce optimization, real-time tracking, route optimization, resource allocation automation, and on-demand management to more than 250 enterprise clients. They also have developed apps for last mile management, field workforce management, long-haul tracking and management, On-Demand, and Reverse Logistics Management. LogiNext has raised a total of $10.6M in funding over two rounds. Their latest funding was raised on Sep 22, 2015, from a Series A round led by Paytm.
People.ai – One of the most creative, insightful startups in revenue management, People.ai helps sales, marketing, and customer success teams uncover every revenue opportunities from every customer. Their system captures all customer contacts, activity, and engagement through real-time integration, then analyzes the aggregated data using AI and machine learning. It’s a brilliant idea to use AI and machine learning to solve one of CRM’s greatest challenges, which is getting enough data captured by customer and prospect over time to make higher quality sales decisions. They’ve also created their own sales performance analytics, personalized coaching, one-on-one feedback, and pipeline reviews, assuring their independence at the stack level. Marketers in the companies who have adopted People.ai use it to fine-tune buying personas and plan then measure marketing campaigns. People.ai has raised a total of $100M in funding over seven rounds. Their latest funding was raised on May 21, 2019, from a Series C round led by ICONIQ Capital. The following is an example of a People.ai dashboard:
PROWLER.io – PROWLER.io is an AI/machine learning platform for building autonomous agents for games and decision-support simulations. The startup is focusing on behavioral learning and simulation in virtual environments and seeing initial success in those use case areas. Their technologies and core intellectual property have the potential to redefine the video game and smart city simulation landscapes significantly. Their systems are being used to create collaborative bots who can mimic learned behaviors over time. PROWLER.io has raised a total of $38.9M in funding over five rounds. Their latest funding was raised on May 20, 2019, from a Series B round.
RAVIN – Using AI, machine learning and traffic monitoring cameras to gather a continuous real-time stream of data that is used to analyze a vehicles’ current condition, RAVIN provides greater transparency to rental car companies, fleet owners and user car sales networks.AI and machine learning are used to evaluate and immediately report any anomalies in the condition of a vehicle. They’ve designed the system to increase the level of trust and transparency in the state of any vehicle being monitored. For used car sales networks, this is like having a real-time visual equivalent of Carfax, for example. RAVIN has raised a total of $4M in funding over 1 round. This was a Seed round raised on May 21, 2019, led by PICO Venture Partners. A conceptual representation of their vehicle reporting is shown below:
Senso.ai – Created to serve financial service providers by helping them manage and grow their consumer credit portfolios, Senso.ai is a leading cloud-based AI platform in the financial services industry. Senso is on a mission to build the world’s most robust vertical-specific data infrastructure to fuel AI product innovation within the financial services industry. The company has raised a total of $1.9M in funding over four rounds. Their latest funding was raised on May 21, 2019, from a Seed round led by the BreakawayGrowth Fund.
SESAMm – SESAMm is an innovative startup competing in the fintech industry, specializing in Big Data and Artificial Intelligence for Asset Management. The company aggregates analytics and investment signals based on 250,000 textual data sources worldwide using Natural Language Processing and precisely emotions analysis. The company works with significant funds and asset managers worldwide in North America, throughout Europe and Asia. SESAMm has raised a total of €8M in funding over three rounds. Their latest funding was raised on Apr 4, 2019.
SymphonyRM – SymphonyRM relies on AI and machine learning to help break down the silos that make U.S. healthcare so expensive and difficult to deal with. The SRM Insights platform applies machine learning and predictive analytics across clinical and payer data silos, to identify Next Best Actions for every consumer in a Provider’s market. The SRM CRM platform drives action from this insight, enabling every member of the provider’s enterprise to proactively manage digital and traditional touch-points while being focused on the prescribed next best actions. SymphonyRM is delivered as a monthly subscription-based managed service to Provider Marketing, Care Coordination, Call Center, and Physician Teams with no upfront costs. The company has raised a total of $10M in funding over 1 round. This was a Series A round raised on May 16, 2019.
Tamr – Following the success of initial research at MIT Computer Science and Information Lab (CSAIL), the Tamr team began building a commercial-grade solution designed to tackle the challenge of connecting and enriching diverse data at scale using machine learning. Today TAMR can reduce the time required for data unification projects by 90% using advanced analytics, including machine learning algorithms. Amgen, GlaxoSmithKline, GE, HP, Roche, Toyota, and others are current clients. Tamr has raised a total of $69.2M in funding over five rounds. Their latest funding was raised on Sep 18, 2018.
Terramonitor – Terramonitor gives professionals the power to analyze, build, and organize geographical information into actionable insights by leveraging up-to-date satellite data, AI, and machine learning. The startup has devised an innovative approach to combining satellite data processing chains, automatic image scanning, and multi-source data merging. As a result of their unique approach to data capture combined with advanced AI and machine learning analysis of imagery, Terramonitor can quickly analyze broad geographic regions for agricultural, infrastructure, environmental, and forestry-related insights. Terramonitor has raised a total of $175K in funding over 1 round. This was a Pre-Seed round led by icebreaker.vc, a venture capital firm who specializes in Nordic and Baltic companies only.
Sources:
PwC/CB Insights MoneyTree Report Q1 2019 (PDF, 75 pp., no opt-in)
Roundup of Machine Learning Forecasts And Market Estimates, 2019, Forbes, March 27, 2019
Roundup Of Machine Learning Forecasts And Market Estimates, 2018, Forbes, February 18, 2018
Venture Pulse Q1, 2019: Global Analysis Of Venture Funding (PDF, 103 pp., no opt-in)