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Machine learning shows up in products as different as travel recommendations, speech interfaces, insurance workflows and recycling equipment. Built In’s September 17, 2026 list names 38 companies using the technology, from specialist vendors to large platforms. The examples below explain the use case each company is associated with; they are not a ranked list or a complete census of the market.
How to read this company list
The companies do not all sell the same thing. Some offer machine-learning platforms, APIs or business software; others build the technology into consumer services or physical systems. A useful way to compare them is to ask what task machine learning supports, who uses the product and how it is delivered. Built In does not publish a ranking method, common performance benchmark or market definition for this selection, so its order should not be read as a measure of effectiveness.
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Machine learning companies by application
Platforms, cloud services and enterprise operations
- DataRobot — An enterprise AI platform for building, deploying and governing predictive and generative AI models, including automated machine learning and agentic AI capabilities.
- Palantir Technologies — Its Foundry and AIP offerings support data preparation, model development, deployment, monitoring and governed enterprise applications.
- IBM — Offers machine-learning tools for automation, predictive analytics, model deployment, risk detection and healthcare analytics.
- Microsoft — Applies machine learning across consumer and enterprise products, developer tools and Azure cloud services.
- Amazon Web Services — Provides services for conversational AI, fraud detection, document extraction and generative-AI applications.
- Google — Embeds machine learning in consumer products and cloud services.
- Striveworks — Its AIOps platform supports dataset preparation, model training and deployment, and monitoring model performance as operating environments change.
- Dropbox — Develops workflow software and is building machine-learning engineering capacity for AI product development.
Speech, language and document processing
- SoundHound — Develops conversational-intelligence and speech technology that processes spoken language and user intent. Its Houndify platform lets developers build voice assistants.
- Deepgram — Provides speech-to-text, text-to-speech and voice-agent APIs for several industries. Built In’s profile also displays an “over 50,000” processing claim without identifying what was processed, so the figure cannot be interpreted as a complete statistic.
- Hyperscience — Uses machine learning to extract and classify data from complex documents, including handwritten forms.
- AKASA — Applies AI to healthcare revenue-cycle administration, including processing clinical and financial documents.
- Canoe — Extracts and classifies information from alternative-investment documents.
- Kensho Technologies — Uses machine learning and language processing to structure unstructured data and extract insights, including from finance-related text.
- OpenAI — Develops machine-learning models used in ChatGPT and offers enterprise products and models through APIs and cloud platforms.
Recommendations, matching and consumer services
- KAYAK — Uses machine learning and real-time travel data for search and recommendations. Built In also describes an Ask AI tool introduced in 2026.
- Hinge — Uses machine learning for matchmaking, including its Most Compatible feature.
- Fora — Uses AI systems for travel-advisor onboarding, payments, hotel bookings and itinerary creation.
- Upside — Personalizes cashback offers for gas, groceries, restaurants and hardware stores with machine learning.
- Rokt — Uses machine learning to personalize recommendations, upsells and offers in ecommerce.
- Metropolis Technologies — Applies computer vision and machine learning to checkout-free payment experiences at parking and aviation facilities, identifying vehicles and passengers and supporting dynamic pricing and operational data insights.
Advertising and marketing
- Liftoff — Uses machine learning for mobile user acquisition, ad monetization and creative optimization.
- OpenX Technologies — Applies machine learning in programmatic advertising for inventory quality, audience modeling and campaign optimization.
- System1 — Operates a digital marketing platform using machine learning, intent data and auctioning technology.
- Smartly — Uses machine learning to automate digital ad creation and optimize advertising spend.
- Klaviyo — Uses data science and machine learning for predictive analytics and marketing personalization.
- Snap Inc. — Its Lens Studio uses machine-learning models to help developers create augmented-reality experiences.
Prediction, finance and insurance
- Upstart — Uses machine learning in digital lending and credit decision tools.
- TrueML — Analyzes consumer behavior and payment patterns to tailor digital debt-collection outreach.
- Gradient AI — Applies AI to insurance underwriting and claims workflows.
- Monte Carlo — A data-observability company that uses machine learning to identify anomalies.
- Gusto — Uses predictive, statistical and machine-learning models in payroll, HR and benefits software.
- AMP — Uses AI, machine learning and computer vision in recycling sortation systems.
Travel, logistics, safety and public-sector applications
- Agero — Provides accident management, roadside assistance, warranty programs and electric-vehicle support. Built In reports that the company collects more than 60 terabytes of data each year.
- Samsara — Uses models trained on video footage for fleet dash-camera collision and risk detection, alongside telematics and predictive analytics.
- Air Space Intelligence — Uses simulation and machine-learning models for predictive situational awareness and decisions in aviation, logistics and military contexts.
- IDEaS — Uses machine-learning forecasting and pricing recommendations in hospitality revenue-management software.
- MORSE Corp — Develops algorithms, analytics and AI testing and evaluation for national-security applications.
Which industries use machine learning in these examples?
The list spans consumer technology and enterprise software, as well as travel, advertising, finance, healthcare, insurance, logistics, hospitality, retail and recycling. The underlying task varies by sector: a travel service may recommend options, a healthcare company may process administrative documents, and a fleet platform may flag collision risk from video. These are examples of applications, not evidence that every company in an industry uses machine learning in the same way.
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How to compare the companies
Start with the job the technology performs, then consider the intended user and delivery model. A speech API for developers is a different kind of offering from an AI-enabled consumer app or enterprise workflow platform, even if each relies on machine learning.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
- Application: Is the system used for speech, document extraction, recommendation, advertising, prediction, anomaly detection or computer vision?
- Buyer and user: Is it aimed at consumers, developers, enterprises or public-sector organizations?
- Delivery: Does the company provide a cloud service or API, business software, or a consumer service with machine learning built in?
- Industry: Which field or operational workflow does the product serve?
Built In’s profiles provide examples along these dimensions, but not a shared measure of accuracy, cost or business results. The list therefore helps identify areas of use; it cannot establish which company’s system performs best.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Other companies named by Built In
Built In’s opening “Top Machine Learning Companies” bullets also name Databricks, while its FAQ lists Unity Interactions alongside AKASA, Kensho Technologies, Metropolis Technologies, OpenX Technologies and KAYAK. These names appear in the page’s introductory and FAQ material rather than as additional profiles in its 38-company list.
Source: Built In, “38 Machine Learning Companies You Should Know,” updated September 17, 2026.
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