Machine learning in the US is now a story about companies that ship, not just ideas that trend. Platform builders keep data clean and models deployable. Cloud providers tighten the pipes. Product teams turn ML into everyday wins in healthcare, finance, retail, and logistics. The players differ, but the pattern is similar to smaller focused use cases, shipped fast, then scaled once the value shows up.

It also comes down to people. The strongest companies blend research depth with practical engineering, pair MLOps with governance, and design interfaces that non technical teams can use without a manual. In this guide, we look at the top companies moving the field forward - the ones building the rails, the ones running on them, and the ones proving impact where it counts.

1. OSKI Solutions

We focus on building well engineered software that solves real problems. In the US market, we see teams moving from ML pilots to production systems that support daily work. We plug machine learning into the stacks clients already use, including .NET and Python, and deploy on AWS, Azure, or hybrid setups. The aim is steady impact in logistics, e-commerce, healthcare, and manufacturing.

Our approach to AI is practical. We embed models into workflows for automation and insight, not as one off demos. We work with agile, remote first teams and often partner with mid sized organizations where ML can live inside core products without extra noise. We ship features that help people do their jobs and keep them easy to maintain.

Key Highlights:

• Experience with ML integration in real business workflows
• Cloud native deployments across AWS, Azure, and hybrid setups
• Focus on industries like e-commerce, logistics, healthcare, and energy
• AI solutions built with Python and .NET
• Workstyle aligned with agile, remote first teams
• Mid sized company partners are our sweet spot

Services:

• Custom software development
• Machine learning and AI integration
• Cloud migration and infrastructure setup
• Frontend and backend development (.NET, Node.js, React, Vue)
• CMS based web development (Umbraco, WordPress)
• DevOps and CI/CD automation
• API development and third party integrations
• Legacy system modernization and digital transformation strategies

Contact Information:

• Website: oski.site 
• E-mail: contact@oski.site 
• LinkedIn: www.linkedin.com/company/oski-solutions 
• Address: Estonia, Tallinn, Kaupmehe tn 7-120, 10114
• Phone: +48571282759

2. Chalk

Chalk operates in a space that’s become more central to how machine learning actually works in production: infrastructure. They focus on the less flashy, more foundational side of ML, the part where features are computed, served, and monitored in real time. Their platform allows teams in the US to work with up-to-date data directly from APIs or databases, making it easier to move fast without breaking things. While a lot of ML platforms talk about experimentation, Chalk is more about making sure that once you’ve got something that works, it keeps working reliably.

What stands out about their approach is the way they unify online and offline data workflows. Instead of dealing with messy handoffs between environments, ML engineers can define everything in Python, and the system takes care of the orchestration. That’s not just good for efficiency, it's the kind of real-world simplification that makes a big difference when you’re trying to scale machine learning across teams. Chalk fits into the broader trend in the US of moving ML from isolated research teams into core business systems where uptime and latency actually matter.

Key Highlights:

• Real-time feature engineering built directly into production ML stacks
• Python-first approach for fast iteration and deployment
• Unified feature store for training, testing, and live inference
• Data source agnostic: works with APIs, databases, and live systems
• Emphasis on observability, telemetry, and traceability
• Infrastructure runs inside the customer’s own cloud

Services:

• Real-time feature computation and serving
• Feature store for ML and GenAI pipelines
• Unified online and offline ML workflows
• Infrastructure deployment inside customer VPC
• Integration with structured, unstructured, and live data sources
• Monitoring, lineage tracking, and drift detection for ML features

Contact Information:

• Website: chalk.ai
• E-mail: support@chalk.ai
• Twitter: x.com/chalk
• LinkedIn: www.linkedin.com/company/chalkai
• Address:  2390 Mission St, Suite 200, San Francisco, CA 94114, USA

3. Zensar

Zensar takes a broader enterprise-first view on machine learning and AI, especially in how these tools can be embedded across large systems. Their work with ML in the US spans industries like finance, utilities, and tech, with a focus on automating workflows and unlocking decisions that previously required too much manual effort. It’s not about standalone ML models. It’s about getting AI into the places where things already happen: billing systems, customer support, risk scoring and letting the system learn and adapt from the inside.

Their strategy leans heavily into responsible AI, which has become a big theme in the US, especially in regulated industries. It’s not just about performance anymore, it’s about accountability. Zensar’s emphasis on explainable ML, ESG risk integration, and compliance frameworks reflects that shift. They also put a lot of effort into making sure AI ties into business KPIs, not just technical success metrics. The move toward AI systems that are adaptable, trackable, and role-aware is becoming more common, and Zensar seems to be positioning for that type of environment.

Key Highlights:

• Focus on enterprise AI adoption with governance and KPIs in mind
• Offers both traditional ML and generative AI capabilities
• Experience across finance, utilities, and large-scale tech
• Builds self-learning systems to automate and optimize processes
• Invests in responsible AI practices across lifecycle stages
• Offers vertical-specific AI strategies and solutions

Services:

• AI and ML system development for enterprise workflows
• Generative AI services and infrastructure setup
• Smart Advisor platform for financial decision support
• AI governance and risk management frameworks
• MLOps pipelines and cloud-based deployment
• Data engineering, visualization, and automation services

Contact Information:

• Website: www.zensar.com
• E-mail: dpo@zensar.com
• Facebook: www.facebook.com/ZensarTech
• Twitter: x.com/Zensar
• LinkedIn: www.linkedin.com/company/zensar
• Instagram: www.instagram.com/zensar.technologies
• Address: 2 Research Way, 1st Floor, Princeton, NJ 08540, USA
• Phone: +1-609-452-1414

4. SDG Group - Orbitae

Orbitae by SDG Group is less about the tools and more about the roadmap. They work with clients to define where AI fits in their business, and then follow through with everything from strategy to operations. That sort of full-spectrum approach fits a growing trend in the US where companies don’t just want AI components they want a game plan. Orbitae acts like a guide that helps organizations prioritize use cases, structure their data, and build systems that scale with purpose instead of patchwork.

They emphasize responsible AI and long-term infrastructure, which has become a common concern for American companies navigating both technical debt and ethical obligations. Rather than pushing any single technology, Orbitae is more about integration making sure AI supports existing business goals instead of pulling focus. They also work on AI migration and intelligent process automation, helping companies move from old systems to modern pipelines without creating chaos. It’s less about fast wins and more about building the kind of durable ML infrastructure that can evolve as tech and regulations shift.

Key Highlights:

• Focus on AI strategy and alignment with business goals
• Covers full lifecycle: from AI foundations to deployment and automation
• Emphasis on ethical AI adoption and governance
• Supports AI migration from legacy to modern stacks
• Industry-specific accelerators for areas like supply chain and market analytics
• Works closely with clients on long-term co-innovation

Services:

• AI strategy development and prioritization
• AI infrastructure and platform design
• Responsible AI governance setup
• Custom AI solutions for business intelligence and operations
• Intelligent automation and process mining
• AI system migration and engineering services

Contact Information:

• Website: www.sdggroup.com
• E-mail: contact.usa@sdggroup.com
• Twitter: x.com/sdggroup
• LinkedIn: www.linkedin.com/company/sdg-group-usa
• Instagram: www.instagram.com/sdggroup.usa
• Address: 203 N Lasalle, Chicago, Illinois - 60601 , USA

5. Alteryx

They approach machine learning in the USA from the data up. Instead of treating ML as a separate island, they focus on getting data into shape and keeping it consistent across teams. Their platform unifies prep, analysis, and orchestration so analysts and engineers can move from raw inputs to usable features and models without juggling a dozen tools. It fits how many US teams actually work today: some cloud, some on prem, lots of hybrid, and a constant need for governance.

They lean into simple interfaces with room for depth. Low code workflows sit next to Python, and natural language tools help non technical users explore questions. The bigger story is operational impact. Models only matter when they are fed with clean, well tracked data and deployed in a way that people can trust. Their integrations with common data clouds and BI tools reflect that push for end to end reliability.

Key Highlights:

• Unified workflows from data prep to prediction
• Low code plus code options for mixed teams
• Built in governance and security controls
• Works across cloud, hybrid, and on prem setups
• Integrations with major data platforms and BI tools
• Support for both ML and generative AI use cases

Services:

• Data access, cleansing, and enrichment
• Analytics automation and ML model workflows
• Generative AI features within analytic pipelines
• Reporting, explanation, and sharing of insights
• Scheduling and orchestration of production jobs
• APIs, connectors, and administration tooling

Contact Information:

• Website: www.alteryx.com
• E-mail: privacy@alteryx.com
• Facebook: www.facebook.com/alteryx
• Twitter: x.com/alteryx
• LinkedIn: www.linkedin.com/company/alteryx
• Address: 3347 Michelson Drive Suite 400 Irvine, CA 92612, USA
• Phone: +1 888 836 4274

6. NineTwoThree

They operate like a product studio that happens to know ML inside and out. In the US market, that means turning ideas into shipped software that mixes classic machine learning with newer agent and chat workflows. Their work spans conversational AI, vision, and the kind of predictive models that power recommendations or anomaly alerts. Instead of over designing, they push for fast validation, then build what users will actually use.

Their scope covers more than models. They design interfaces, wire data sources, and handle mobile and web builds around the ML core. That blend is useful for teams that need one accountable group to carry a product from discovery to launch. Their portfolio shows a bias toward practical wins like knowledge bases, lead scoring, and computer vision that plugs into real devices or security flows. The theme is steady: less talk, working software.

Key Highlights:

• Product first mindset across ML, UX, and engineering
• Conversational AI, agents, and classic ML side by side
• Computer vision and on device scenarios
• Mobile and web development around the ML layer
• Iterative delivery with clear checkpoints
• Experience integrating with common data and cloud stacks

Services:

• Conversational AI and chatbots
• Generative AI agents and workflow automation
• Predictive modeling and recommendation systems
• Computer vision, OCR, and object detection
• AI for IoT and edge monitoring
• Mobile apps, custom web apps, and system integration
• Product strategy, design sprints, and GTM support

Contact Information:

• Website: www.ninetwothree.co 
• E-mail: contact@ninetwothree.co
• Facebook: www.facebook.com/NineTwoThreeStudio
• Twitter: x.com/923_studio
• Instagram: www.instagram.com/ninetwothree.studio
• Linkedin: www.linkedin.com/company/ninetwothree
• Address:  923 Digital, LLC 250 Independence Drive Danvers MA, 01923, USA

7. FS Studio

They bring ML into immersive and simulation heavy environments that many US teams now rely on for training, testing, and operations. Their work with digital twins, AR and VR, and synthetic data supports scenarios where real world testing is risky or slow. By building simulated spaces with physics and behavior, they help teams prototype workflows and generate data that trains models before anything goes live.

Beyond software, they can stand up facilities and staff them with engineers who run robotics, simulation, or lab operations. That is useful for industries where ML meets hardware or regulated processes. They also support aerospace projects with documentation, automated testing, and lifecycle help. The common thread is realism: build it, simulate it, measure it, then move to production when the system proves itself.

Key Highlights:

• Digital twin and immersive environments for ML workflows
• Synthetic data generation for training and testing
• XR based training and demos for safer iteration
• Facility setup and staffing for labs and operations
• Robotics and simulation expertise across engines
• Support for aerospace and complex systems engineering

Services:

• Product design with AR, VR, WebXR, and digital twins
• Three step process for discovery, prototyping, and build
• Synthetic data pipelines and 3D simulation
• Facility management, setup, and staffing
• Robotics, automation, and systems integration
• Documentation, testing, and lifecycle support for aerospace

Contact Information:

• Website: www.fsstudio.com
• Email: info@fsstudio.com
• Address: 11001 West 120th Avenue, Suite 400 Broomfield, CO 80021
• Phone: +1(888) 404-6115
• Linkedin: www.linkedin.com/company/fs-studio
• Instagram: www.instagram.com/fsstudiodev
• Twitter: x.com/fsstudiodev
• Facebook: www.facebook.com/FSStudioDev

8. Dualboot Partners

They treat machine learning as part of product delivery, not a side project. In the US market, that shows up as careful discovery, clear scope, and ML features that fold into web and mobile apps people already use. Their teams pair data prep and model work with solid engineering, so features move from proof of concept to production without spinning in place.

They also support different ways of working. Some clients want a full build, others need added hands or system upgrades. Dualboot fits ML into those paths with attention to security, MLOps, and the basics that keep systems stable over time. The outcome is straightforward: ship useful ML, keep it maintainable, and make sure the business can live with it day to day.

Key Highlights:

• Product first ML inside real apps
• Discovery to reduce risk before building
• Attention to MLOps and security
• Options for full builds or added team capacity
• Experience modernizing older systems
• Work across finance, healthcare, retail, and tech

Services:

• AI and machine learning feature development
• Product strategy and technical discovery
• Web and mobile engineering around ML use cases
• Data pipelines, integration, and deployment
• Staff augmentation for AI and engineering roles
• Legacy system modernization and cloud moves

Contact Information:

• Website: dualbootpartners.com
• Email: hello@dualbootpartners.com
• Linkedin: www.linkedin.com/company/dualbootpartners
• Twitter: x.com/DUALBOOT_PTRS
• Instagram: www.instagram.com/dualbootpartners

9. Six Feet Up

They come from a strong Python background and apply it to practical ML work across US teams that need results, not noise. Their focus areas map to common needs: predicting events, catching anomalies, classifying text or images, and standing up chat or virtual assistant flows. It sits on top of cloud, data pipelines, and the tooling that keeps models updated.

They tend to connect the dots from code to operations. That means CI/CD for ML, observability, and pipeline tuning so models stay healthy after launch. The approach is simple and useful: get the data moving, ship the model, monitor it, and iterate without breaking the rest of the stack.

Key Highlights:

• Python centric ML with cloud depth
• Predictive, anomaly, and classification use cases
• Chatbots and LLM powered assistants
• CI/CD and observability for ML systems
• Work with Databricks, Airflow, and Kubernetes
• Focus on stable, maintainable pipelines

Services:

• Predictive analysis and anomaly detection
• Text and image classification
• Chatbots and virtual assistants
• Data engineering and pipeline optimization
• Cloud deployment on AWS, GCP, or Azure
• CI/CD, monitoring, and model lifecycle support

Contact Information:

• Website: sixfeetup.com
• Email: info@sixfeetup.com
• Address: Six Feet Up, Inc. 11208 Windermere Blvd.Fishers, IN 46037 – USA
• Phone: +1 (317) 861-5948
• Twitter: x.com/sixfeetup
• Linkedin: www.linkedin.com/company/sixfeetup
• Facebook: www.facebook.com/sixfeetup

10. Azumo

They support US companies with nearshore teams that build and run AI products end to end. The core offer is flexible delivery: add engineers to an existing group, spin up a dedicated team, or hand over a managed project. Across those models, they cover classic ML, LLM work, vision, and data engineering, with attention to process and handoff.

Their method is plain and structured. Start with discovery, prototype early, and move to MVP only when the path is clear. From there, they handle hosting, monitoring, and steady improvements. It is meant to reduce thrash while still moving fast, which is what most teams want when ML touches live users or revenue.

Key Highlights:

• Nearshore teams aligned to US time zones
• Flexible models for staffing or full delivery
• Work across LLMs, classic ML, and vision
• Strong focus on discovery and MVP clarity
• Experience with common cloud and data stacks
• Security minded practices and steady cadence

Services:

• Staff augmentation for AI, data, and platform roles
• Dedicated development teams for ML products
• Managed project delivery with vCTO support
• MVP and proof of concept builds
• Semantic search, anomaly detection, and computer vision
• Data engineering, back end, and cloud operations

Contact Information:

• Website: azumo.com
• Phone: 415.610.7002
• Twitter: x.com/azumohq
• Linkedin: www.linkedin.com/company/azumo-llc
• Facebook: www.facebook.com/azumohq

11. 3Advance

They treat machine learning as part of building products, not a side quest. In the US market, they lean into LLM integrations, RAG setups, and agent workflows that plug into real apps users already touch. Their teams move from discovery to MVP with a clear roadmap, then fold ML features into web and mobile without turning the stack upside down.

They also handle the practical bits that make ML usable day to day. That includes voice and speech features, vector databases, and model fine tuning when off the shelf is not enough. The throughline is simple: ship something real, learn from usage, and keep iterating without ceremony.

Key Highlights:

• Focus on LLM integrations, RAG, and agent workflows
• Product minded delivery from idea to MVP to scale
• Web and mobile engineering wrapped around ML features
• Support for speech, voice cloning, and multimodal flows
• Clear roadmaps that keep scope and priorities grounded

Services:

• AI and LLM engineering for live products
• AI agents, bots, and GPT style experiences
• Retrieval and vector database implementation
• Model fine tuning and evaluation
• Web, mobile, and cloud integration around ML

Contact Information:

• Website: 3advance.com
• Email: apps@3advance.com
• Phone: 202.709.3238
• Instagram: www.instagram.com/3advance
• Twitter: x.com/3advance
• Linkedin: www.linkedin.com/company/3advance

12. Turing

They operate at the intersection of model advancement and production delivery. In the US landscape, that means helping labs train and evaluate models while also giving enterprises the people and systems to deploy those models responsibly. Their playbook covers data tooling, human in the loop review, and post training steps that raise model quality before anything hits users.

On the deployment side, they set up teams that plug into existing stacks and move pilots into production with KPIs that matter. Safety, alignment, and factual checks show up as part of the workflow rather than a late add. The net effect is a bridge from research to outcomes that teams can maintain over time.

Key Highlights:

• Support for training, evaluation, and post training
• Human in the loop processes for quality and safety
• Multimodal and coding oriented model work
• Pods that integrate with enterprise delivery rhythms
• Emphasis on alignment and measurable outcomes

Services:

• LLM evaluation and improvement
• Model training and reasoning enhancement
• Multimodal integration across text, image, and video
• Safety, alignment, and factuality checks
• Deployment of AI systems and AI native teams
• AI strategy and roadmap support

Contact Information:

• Website: www.turing.com
• Email: support@turing.com
• Address: 1900 Embarcadero Road Palo Alto, CA, 94303
• Facebook: www.facebook.com/turingcom
• Twitter: x.com/turingcom
• Linkedin: www.linkedin.com/company/turingcom
• Instagram: www.instagram.com/turingcom

13. BlueLabel Labs

They focus on agentic AI in real business settings. In the US, that looks like multi agent workflows, RAG applications, and conversational interfaces that sit on top of clean data pipelines. Their teams start with use case mapping, prototype quickly, and measure value early so projects do not drift.

They pair strategy with hands-on engineering. Data and LLM work lands alongside product design, which helps complex flows feel usable. They commit to clear metrics and long term upkeep, which matters once AI becomes part of daily operations rather than a one off build.

Key Highlights:

• Agentic AI focus with practical workflows
• RAG based apps for faster, grounded responses
• Conversational AI tied to business systems
• Data pipelines and LLM engineering under one roof
• Strategy first, delivery focused approach with clear metrics

Services:

• AI strategy and use case prioritization
• Multi agent workflow design and build
• Retrieval augmented generation implementation
• Conversational AI for support and operations
• Data engineering for model readiness
• End to end AI product development and rollout

Contact Information:

• Website: www.bluelabellabs.com
• Email: privacy@bluelabellabs.com
• Linkedin: www.linkedin.com/company/blue-label-labs
• Address: 18 West 18th St New York, NY, 10011, USA

14. Serokell

They approach machine learning as a mix of applied math and practical engineering. In the US market, that shows up in work that pairs core models with real products, like computer vision for visual data, NLP for search and assistants, and chatbots that can handle support at scale. They tend to focus on the full path from problem framing to deployment, with attention to security, QA, and maintainability so teams are not stuck after the first release.

They also provide a clear consulting model that fits how companies buy ML today. Some want fixed scope builds, others prefer time and materials, and some need a dedicated team that can move alongside in-house engineers. Across those setups, they bring BI, recommender systems, and dashboards into the mix so results are visible and actionable, not just a model sitting in a repo.

Key Highlights:

• Focus on NLP, computer vision, and conversational AI
• Emphasis on security, QA, and reliability in ML delivery
• Options for fixed price, time and materials, or dedicated team
• BI and recommender systems to connect ML with decisions
• Attention to deployment, scalability, and long term support

Services:

• Natural language processing and smart search
• Computer vision for detection and classification
• Conversational AI and chatbots
• Business intelligence and data visualization
• Recommender systems and personalization
• ML consulting, integration, and MLOps support

Contact Information:

• Website: serokell.io
• Email: hi@serokell.io
• Phone: (+372) 699-1531
• Twitter: x.com/serokell
• Facebook: www.facebook.com/serokell.io
• Linkedin: www.linkedin.com/company/serokell
• Address: Pille tn. 11/1-32, Kesklinna linnaosa, Tallinn, Harju maakond, 10138, Estonia

15. Simform

They position machine learning as part of a larger engineering system. For US teams, that means tying GenAI, classic ML, and data engineering to product and platform work, not running them in isolation. Their approach starts with strategy and carries through architecture, model work, and production rollout, with MLOps and cloud governance built in from the start.

They lean on accelerators and a repeatable toolkit to move beyond proofs of concept. That includes data readiness, RAG patterns, agent workflows, and monitoring that keeps models useful after launch. The result is a path that lines up with how enterprises actually deploy AI in production while staying close to user experience and measurable outcomes.

Key Highlights:

• End to end coverage from strategy to deployment
• GenAI, classic ML, and agent patterns under one roof
• Strong focus on data readiness and MLOps
• Cloud depth with governance and reliability practices
• Product centric delivery that tracks real outcomes

Services:

• AI strategy and roadmap
• Custom ML and GenAI development
• Autonomous agents and RAG implementation
• Data engineering and analytics platforms
• MLOps pipelines, monitoring, and governance
• Cloud and DevOps for scalable AI workloads

Contact Information:

• Website: www.simform.com 
• Email: contactus@simform.com
• Facebook: www.facebook.com/simform
• Linkedin: www.linkedin.com/company/simform
• Twitter: x.com/simform

Conclusion

Machine learning in the USA is past the novelty stage. The real story now is less about model names and more about how teams stitch ML into everyday work. Companies are pairing rock solid data foundations with smaller, focused use cases, then letting results pull the next investment. It is not glamorous, but it is how impact shows up in customer support queues, supply chains, field operations, and finance dashboards.

Talent is changing too. The strongest teams blend researchers, data engineers, and product minded developers who think in systems, not slides. They are comfortable with boring but critical plumbing like data quality, governance, and MLOps. They also keep an eye on what is next: agent workflows, multimodal inputs, real time features, and a growing role for synthetic data when the real thing is hard to capture.

If there is a pattern, it is this: start with a clear problem, keep the data honest, and ship in small loops. Treat safety and explainability as part of delivery, not an afterthought. Build simple interfaces so non technical users actually adopt what you launch. Measure the outcome that matters and retire what does not move the needle.

The pace will not slow. New tools will arrive, budgets will shift, and regulations will keep evolving. Teams that do well will stay practical, keep their stacks tidy, and invest in people who can cross the aisle between research and production. That is where the next wave of real world impact will come from, one shipped workflow at a time.