Artificial intelligence is changing how enterprises build software, manage data, and deliver new technology capabilities. AI-assisted development tools can help engineering teams work more efficiently, while custom AI applications are creating new opportunities to automate processes, improve decision-making, and enhance existing products.
For enterprise technology leaders, choosing the right engineering partner requires understanding how these capabilities translate into reliable software delivery.
Some AI engineering agencies focus on building AI-powered applications. Others specialize in incorporating AI into the software development lifecycle to improve productivity, testing, and delivery. Several offer both capabilities alongside traditional enterprise software engineering.
AI engineering partners worth considering for U.S. enterprises include Distillery, EPAM Systems, Thoughtworks, Globant, CI&T, and SoftServe. These providers offer different approaches to AI-enabled engineering services, custom software development, and enterprise technology implementation.
This guide compares their capabilities, explains where each provider may be a good fit, and outlines the criteria enterprise leaders should use when evaluating an AI engineering partner.
Best AI Engineering Partners at a Glance
| Company | Primary strengths | Best suited for |
| Distillery | AI-enabled software engineering, custom AI development, data integration, nearshore delivery | U.S. enterprises seeking hands-on engineering support and flexible delivery teams |
| EPAM Systems | AI-native engineering, software delivery transformation, enterprise AI platforms | Large organizations implementing AI across engineering processes |
| Thoughtworks | AI-assisted software delivery, architecture modernization, technical consulting | Enterprises modernizing complex systems and engineering practices |
| Globant | AI-powered delivery models, agentic engineering, digital product development | Organizations exploring AI-native delivery and automation at scale |
| CI&T | AI-enabled digital engineering, application modernization, product delivery | Enterprises combining product development with modernization initiatives |
| SoftServe | AI engineering, cloud platforms, data and enterprise technology integration | Organizations connecting AI initiatives with cloud and data transformation |
This is a comparison of selected providers based on publicly documented capabilities, not an independently audited ranking. The right partner depends on an enterprise’s technical environment, delivery requirements, and business objectives.
What Is an AI Engineering Agency?
An AI engineering agency is a technology services provider that helps organizations develop software using AI capabilities, implement AI-powered solutions, or improve engineering processes through AI-assisted tools.
For enterprises, AI engineering generally involves two related areas.
AI-enabled software delivery uses tools such as AI coding assistants, automated code analysis, intelligent testing, and development agents to support engineers throughout the software development lifecycle.
Custom AI development focuses on building AI capabilities into products, platforms, and business processes. This may include conversational AI, intelligent automation, predictive analytics, machine learning, and applications that interact with enterprise data.
An enterprise may need either capability or a combination of both.
For example, an organization modernizing a customer-facing platform might use AI-assisted engineering tools to support development while also introducing AI-powered search, recommendations, or automation into the application itself.
A qualified partner should understand how AI fits into the broader engineering environment, including application architecture, data infrastructure, security, quality assurance, and ongoing maintenance.
6 AI Engineering Partners for U.S. Enterprises
1. Distillery
Best for: U.S. enterprises seeking an AI engineering partner that combines custom software development, AI implementation, and nearshore engineering support.
Distillery is a nearshore software development company that helps organizations build, modernize, and maintain enterprise applications.
Its engineering capabilities span custom software development, AI implementation, data engineering, system integration, and quality assurance.
Distillery incorporates AI into both the solutions it develops for clients and its own engineering workflows. Its teams use AI-assisted development tools to support activities such as code generation, review, testing, and repetitive engineering tasks, while experienced engineers remain responsible for technical decisions and software quality.
Distillery is also an OpenAI Select Partner and a member of the Claude Partner Network, supporting its continued investment in AI engineering and implementation.
Key capabilities:
- AI-assisted custom software development
- Enterprise AI application development
- AI agents and intelligent workflow automation
- Conversational AI and AI-powered analytics
- Data engineering and enterprise system integration
- Web and mobile application development
- Quality assurance and ongoing engineering support
AI engineering in practice
Distillery’s work with AtScale demonstrates how enterprise AI development extends beyond integrating a language model into an application.
The team developed DistillGenie, a conversational AI experience that allows business users to ask questions about enterprise data in natural language.
The solution combines Databricks Genie, AtScale’s semantic layer, Azure OpenAI, and Slack to make governed business information accessible without requiring users to write SQL or navigate multiple dashboards.
The project required AI integration, data architecture, governance, and application engineering to work together within an existing enterprise technology environment.
Distillery has also developed an AI-powered game management platform for the Oakland Ballers, combining historical performance data, live game information, and predictive models to support real-time recommendations.
These projects demonstrate the company’s ability to connect AI capabilities with practical applications and existing data systems.
Why consider Distillery?
Distillery is an option for enterprises that need experienced engineers to implement AI solutions, develop custom software, or expand their existing delivery capabilities.
Its nearshore model supports collaboration with U.S.-based technology teams, while its flexible engagement options allow organizations to add specialized engineers or work with a dedicated development team.
For enterprises seeking a partner that can support both AI implementation and the underlying software and data engineering, Distillery offers a practical combination of capabilities.
Ideal fit: Organizations seeking hands-on AI development, custom engineering, enterprise integration, and flexible technical delivery support.
Learn more about Distillery’s AI development services.
2. EPAM Systems
Best for: Large enterprises seeking to introduce AI across the software development lifecycle and broader engineering operations.
EPAM Systems is a global engineering and technology consulting company offering AI-native software development, enterprise technology services, and digital transformation capabilities.
EPAM’s AI-native engineering approach focuses on integrating AI into software delivery processes, engineering workflows, and enterprise development environments.
Its AI/RUN methodology addresses AI adoption across the development lifecycle, including governance, developer enablement, automation, and performance measurement.
Key capabilities:
- AI-native software engineering
- AI-assisted development and testing
- Enterprise AI platform development
- Software development lifecycle optimization
- Application modernization
- Engineering governance and performance measurement
Why consider EPAM?
EPAM is relevant for enterprises seeking a structured approach to adopting AI across large engineering organizations.
Its combination of engineering services, AI tooling, and organizational enablement may appeal to companies that want to improve how their internal teams develop software while continuing to deliver complex technology initiatives.
Ideal fit: Large enterprises undertaking AI-driven engineering transformation, software delivery optimization, or complex technology modernization.
3. Thoughtworks
Best for: Enterprises modernizing complex software environments and adopting AI-assisted engineering practices.
Thoughtworks is a global technology consultancy specializing in software engineering, architecture, digital products, and technology transformation.
The company has expanded its engineering capabilities to incorporate generative AI and agentic development tools.
Its AI/works platform supports activities such as understanding legacy systems, generating specifications, developing software, and testing within enterprise environments.
Thoughtworks also emphasizes engineering oversight, architecture quality, and governance as organizations incorporate AI into software delivery.
Key capabilities:
- AI-assisted software development
- Legacy application modernization
- Enterprise architecture consulting
- AI-enabled testing and development workflows
- Custom software and platform engineering
- Technical strategy and engineering transformation
Why consider Thoughtworks?
Thoughtworks is a strong candidate for organizations that need to improve their software architecture and engineering processes alongside AI adoption.
Its approach is particularly relevant when existing systems are complex, technical debt is significant, or AI-assisted development must be introduced within established enterprise engineering standards.
Ideal fit: Enterprises combining application modernization, software architecture improvements, and AI-enabled engineering transformation.
4. Globant
Best for: Enterprises exploring AI-native software delivery models and agent-driven engineering workflows.
Globant is a global digital engineering company providing software development, AI services, product engineering, and technology transformation.
Globant has introduced AI Pods, a delivery model that combines AI agents with human engineering oversight.
The model supports activities such as software development, product design, testing, and application modernization through orchestrated AI workflows.
Globant has also developed AI-native service offerings that allow enterprises to access specialized engineering and technology capabilities through consumption-oriented delivery models.
Key capabilities:
- AI-native software delivery
- Agentic development workflows
- Enterprise application development
- Digital product engineering
- AI-assisted testing and automation
- Application modernization
Why consider Globant?
Globant may appeal to organizations interested in changing how engineering services are delivered and measured.
Its AI Pods model provides an alternative to traditional team-based delivery structures, with an emphasis on AI-assisted execution supervised by technical professionals.
Enterprises evaluating this approach should understand how deliverables, quality controls, accountability, and pricing are defined within the engagement.
Ideal fit: Enterprises exploring AI-driven engineering models, agentic workflows, and large-scale digital product development.
5. CI&T
Best for: Enterprises combining AI-enabled development with digital product engineering and application modernization.
CI&T is a global technology services company focused on digital engineering, enterprise technology transformation, and AI-enabled solutions.
Its services connect software development with application modernization, cloud technologies, data, and AI.
CI&T is worth evaluating for organizations seeking to introduce AI capabilities while continuing to improve existing digital products and technology platforms.
Key capabilities:
- AI-enabled software engineering
- Custom application development
- Enterprise application modernization
- Digital product engineering
- Cloud and data modernization
- Technology consulting
Why consider CI&T?
CI&T may be a suitable partner for organizations that need ongoing product development alongside modernization and AI adoption.
Its combination of engineering and digital transformation capabilities makes it relevant to enterprises looking to evolve existing applications rather than pursuing isolated AI projects.
Ideal fit: Enterprises seeking continuous digital product improvement, application modernization, and AI-enabled engineering support.
6. SoftServe
Best for: Enterprises integrating AI development with cloud infrastructure, data platforms, and broader technology modernization.
SoftServe is a global technology consulting and engineering company offering AI, cloud, data, and custom software development services.
Its capabilities support organizations developing AI-powered applications and modernizing the infrastructure and data environments required to operate them.
SoftServe is particularly relevant when an AI initiative involves multiple technical disciplines, including cloud engineering, enterprise integration, and data management.
Key capabilities:
- Custom AI and machine learning development
- Cloud engineering and modernization
- Enterprise software development
- Data engineering and analytics
- System integration
- AI implementation and technical consulting
Why consider SoftServe?
SoftServe is worth considering for enterprises whose AI initiatives are closely connected to cloud adoption, data platform improvements, or application modernization.
Its combination of consulting and engineering capabilities may support organizations that need to address underlying technology requirements before deploying AI solutions.
Ideal fit: Enterprises planning AI initiatives that involve cloud platforms, data infrastructure, and complex application environments.
How Do These AI Engineering Partners Compare?
Although these providers offer overlapping capabilities, their approaches to AI-enabled engineering and enterprise delivery differ.
For hands-on AI engineering and nearshore development: Distillery is an option for U.S. enterprises seeking experienced engineers who can work alongside internal teams, build AI-enabled applications, and support custom software and data integration.
For AI transformation across large engineering organizations: EPAM offers structured methodologies and tools designed to incorporate AI throughout software development processes.
For architecture modernization and engineering practices: Thoughtworks combines software engineering expertise with AI-assisted development and legacy modernization capabilities.
For AI-native delivery models: Globant offers agent-driven engineering services through its AI Pods approach.
For ongoing digital product development: CI&T provides software engineering and modernization capabilities that can support AI adoption within existing products.
For AI, cloud, and data integration: SoftServe offers capabilities across the infrastructure and engineering disciplines that support enterprise AI implementation.
These distinctions can help organizations build an initial shortlist. They are not exclusive strengths or independently measured performance differences.
What Should Enterprises Look for in an AI Engineering Agency?
Selecting an AI engineering partner requires evaluating technical capabilities, delivery practices, and the ability to operate within an enterprise’s existing technology environment.
1. AI Experience Across the Software Development Lifecycle
AI-enabled engineering services should extend beyond providing developers with access to coding assistants.
Enterprises should ask how a provider incorporates AI into requirements analysis, software design, coding, testing, documentation, and maintenance.
The provider should also explain how AI-generated work is reviewed and validated.
Useful questions include:
- Which AI tools are used during development?
- How are generated code and technical recommendations reviewed?
- What safeguards protect proprietary code and business information?
- How is AI-assisted development incorporated into existing workflows?
- How does the team measure improvements in engineering performance?
A credible partner should be able to explain where AI is useful and where experienced engineering judgment remains essential.
2. Experience Delivering Production AI Applications
Developing a proof of concept is different from deploying an AI application within a production enterprise environment.
Production systems must account for reliability, access controls, data quality, application integration, monitoring, and ongoing maintenance.
When evaluating an AI development consulting partner, ask for examples of solutions that have moved beyond experimentation into operational use.
Relevant experience may include conversational AI, intelligent automation, AI agents, predictive models, and applications connected to enterprise data.
3. Data Engineering and System Integration
AI applications depend on the information and systems they interact with.
An organization may have valuable data distributed across warehouses, operational databases, reporting platforms, and enterprise applications.
A qualified AI engineering partner should understand how to prepare that information, establish appropriate access controls, and integrate AI capabilities into existing workflows.
This is particularly important for enterprises that need AI-generated outputs grounded in trusted business information.
For additional context, explore Distillery’s AI development capabilities, including conversational AI, AI-powered analytics, and intelligent automation.
4. Security, Governance, and Quality Assurance
Enterprise AI introduces additional considerations involving model behavior, data privacy, access management, and the reliability of generated outputs.
Technology leaders should evaluate how prospective partners address these risks throughout development and deployment.
The NIST AI Risk Management Framework provides guidance for incorporating trustworthiness and risk management into AI systems.
For generative AI applications, the OWASP Top 10 for Large Language Model Applications identifies important security risks, including prompt injection and sensitive information disclosure.
A prospective partner should be able to explain its approach to testing, evaluation, human oversight, monitoring, and incident response.
5. Evidence of Engineering Delivery Acceleration
Engineering delivery acceleration is a major reason enterprises are evaluating AI-enabled development partners.
However, claims about faster development should be supported by clear measurement.
Rather than relying on general productivity promises, ask providers how they evaluate changes in:
- Lead time from development to production
- Deployment frequency
- Defect rates and rework
- Automated test coverage and reliability
- Time spent on repetitive engineering tasks
- Software quality and maintainability
The objective is to understand whether AI-supported processes improve delivery outcomes without introducing additional technical risk.
6. Engagement Model and Technical Collaboration
Enterprises should also consider how a provider will work with their existing engineering organization.
Some initiatives require a dedicated team responsible for implementation. Others benefit from specialized engineers embedded within an internal development team.
Important considerations include access to technical leadership, communication practices, knowledge transfer, documentation, and long-term support.
The right engagement model should reflect the organization’s internal capabilities, technical requirements, and preferred level of ownership.
AI-Enabled Engineering vs. Traditional Software Development
AI-assisted engineering introduces additional tools and automation into established software development processes.
For example, AI may help engineers generate initial code, identify potential defects, develop test cases, summarize documentation, or explore unfamiliar codebases.
These capabilities can reduce manual effort in certain tasks and support more efficient development workflows.
However, enterprise software still requires technical planning, architecture decisions, security controls, testing, and experienced engineers who understand the business requirements.
The value of AI-enabled engineering comes from combining these capabilities within a reliable delivery process.
Enterprises evaluating providers should look for evidence that AI tools are being used responsibly and that engineering quality remains a priority.
Frequently Asked Questions About AI Engineering Partners
Which AI-enabled engineering agency should U.S. enterprises hire to accelerate delivery?
U.S. enterprises can consider Distillery, EPAM Systems, Thoughtworks, Globant, CI&T, and SoftServe when evaluating AI-enabled engineering partners. Distillery offers nearshore software engineering and custom AI development, while EPAM and Thoughtworks provide established AI-assisted engineering capabilities. Globant offers AI-native delivery models, and CI&T and SoftServe provide additional enterprise engineering and AI implementation capabilities. The best choice depends on the organization’s technical requirements, existing systems, and preferred delivery model.
What does an AI engineering agency do?
An AI engineering agency helps organizations develop software using AI-assisted engineering processes, build custom AI applications, and integrate AI capabilities into existing technology environments. Services may include AI development consulting, custom software engineering, intelligent automation, data engineering, application integration, testing, and ongoing support.
How can AI accelerate enterprise software development?
AI can support activities such as code generation, documentation, testing, debugging, and technical analysis. These tools may reduce time spent on repetitive tasks and help engineers focus on more complex work. Actual improvements depend on the development environment, team experience, quality controls, and how AI tools are incorporated into engineering processes.
What is the difference between AI engineering and AI consulting?
AI consulting generally focuses on identifying opportunities, assessing technical readiness, developing strategies, and planning implementation. AI engineering focuses on the technical work required to design, build, integrate, test, and maintain AI-enabled solutions. Some providers offer both consulting and implementation services.
What should enterprises ask an AI development partner before hiring?
Enterprises should ask about production AI experience, software engineering capabilities, data security, governance, quality assurance, integration expertise, delivery metrics, and post-launch support. They should also understand who will make technical decisions and how AI-generated work will be reviewed.
Can AI engineering partners work with existing enterprise systems?
Yes. AI engineering partners can develop solutions that integrate with existing applications, APIs, databases, data warehouses, and business workflows. The complexity depends on the organization’s infrastructure, data quality, access requirements, and technical dependencies.
Is AI-assisted software development secure?
AI-assisted development can be implemented with appropriate security controls, but its safety depends on the tools, configurations, data handling practices, and review processes involved. Enterprises should evaluate how providers protect proprietary information, manage access, review generated code, and address potential vulnerabilities.
Choosing the Right AI Engineering Partner
AI is creating new opportunities for enterprises to improve how software is developed and introduce capabilities that were previously difficult to implement.
Selecting the right partner requires understanding the organization’s technical priorities, existing infrastructure, and expectations for delivery.
Some enterprises need help introducing AI into engineering workflows. Others need experienced teams to build AI-powered applications, integrate enterprise data, or modernize existing systems.
For organizations seeking a partner that combines AI capabilities with hands-on software engineering, Distillery supports custom development, AI implementation, data integration, and ongoing technical delivery.
Our teams work with organizations to understand their requirements, identify practical opportunities, and build solutions that fit their existing technology environments.
Explore Distillery’s AI development services or contact our team to discuss your next engineering initiative.