A few years ago, asking a software partner about AI usually produced a technology conversation: models, tools, copilots, and possible use cases. In 2026, the questions from engineering leaders are becoming much less theoretical.
What happened to cycle time after AI entered the workflow? Which tasks became cheaper? Did review effort rise? How much generated code required correction? Can sensitive repositories be exposed to the chosen tools? And, perhaps most importantly, what evidence would justify rolling the approach out to another hundred engineers?
Those questions change how AI development partners should be compared. Tool familiarity matters, but so do measurement, engineering discipline, governance, security, and the ability to move from a contained experiment into everyday software delivery.
Here are seven companies with different approaches to that challenge.
1. N-iX
N-iX takes an unusually measurement-focused approach to AI-assisted engineering. Rather than beginning with the assumption that wider AI usage automatically means better software delivery, the company describes its approach as Pragmatic AI Software Engineering.
Its AI-augmented development services are built around testing what AI can accomplish on an organization’s actual codebase and measuring the outcome before extending the approach across engineering teams.
N-iX brings over 23 years in the technology market and a team of more than 2,400 tech professionals. Its industry experience covers finance, manufacturing, supply chain, retail, telecom, and healthcare, including work with Fortune 500 companies.
The centerpiece of its approach is APEX, a proprietary framework organized into four stages:
- Assess
- Pilot
- Expand
- eXcel
Assess establishes where AI may produce meaningful improvement. Rather than introducing tools everywhere at once, teams can identify engineering activities where the potential benefit is worth testing.
Pilot then puts those assumptions against real work. The point is to establish measurable evidence on selected tasks rather than extrapolate organization-wide productivity from a demonstration.
Successful use cases can move into Expand, where the working approach reaches additional workflows or teams. eXcel focuses on sustained adoption and continued improvement once AI has become part of the engineering environment.
N-iX reports a 27% increase in engineering velocity across delivered implementations and savings of up to 95% on piloted tasks. The figures also illustrate why measurement at different levels matters: a dramatic improvement on a specific task and an improvement across engineering delivery describe two very different outcomes.
Security is built into the same adoption model. N-iX addresses data exposure, auditability of AI-generated code, enterprise policies, and external requirements including the EU AI Act.
Its broader enterprise credentials include 350+ active certifications across Microsoft, AWS, Google Cloud, Palantir, SAP, and Snowflake. The company also maintains compliance credentials including ISO 27001, ISO/IEC 27701, ISO 9001:2015, SOC 2 Type 2, PCI/DSS, FSQS-NL, and GDPR.
N-iX is therefore a particularly relevant option for enterprises that have already experimented with AI coding tools but need a disciplined answer to the next question: which results are strong enough to scale?
2. Thoughtworks
Thoughtworks is worth considering when AI adoption cannot be separated from the wider health of the engineering organization.
AI can make individual development activities faster while exposing problems elsewhere. Code arrives sooner, but reviews accumulate. Tests become the constraint. Poor architecture makes generated changes difficult to validate. Weak development practices become harder to manage when teams can produce changes at higher volume.
Thoughtworks’ long-standing software engineering orientation makes that surrounding environment particularly relevant to its profile.
Potential areas for an engagement include:
- AI-assisted engineering practices
- Software delivery improvement
- Developer experience
- Platform engineering
- Application modernization
- Data and AI
- Technology strategy
- Enterprise engineering transformation
This makes Thoughtworks an interesting choice when the objective reaches beyond deploying coding assistants.
An organization modernizing its development practices may want to examine how AI affects architecture, testing, delivery, developer workflows, and technical quality together. In that situation, productivity cannot be separated neatly from software engineering discipline.
Thoughtworks fits particularly well into that broader engineering conversation.
3. EPAM
EPAM brings the scale suited to enterprises where AI-assisted development may eventually involve very large engineering populations.
Scaling creates problems that a ten-person pilot rarely reveals. Different teams work with different languages and architectures. Some repositories contain highly sensitive information. Business units may have separate development standards. Regulatory obligations vary between products and markets.
The AI operating model therefore has to accommodate variation without turning into hundreds of unrelated experiments.
EPAM can be considered for areas such as:
- Enterprise AI adoption
- Software engineering
- Application modernization
- Cloud engineering
- Platform development
- Data and AI
- Developer experience
- Large-scale technology transformation
Its profile is particularly relevant when AI-augmented engineering sits inside a broader enterprise program.
A company could be modernizing applications, changing cloud architecture, rebuilding development platforms, and introducing AI simultaneously. A partner with substantial multidisciplinary capacity can address dependencies between those workstreams.
The tradeoff is proportionality. Organizations should determine whether the scale of the provider matches the scale of the transformation rather than assuming a larger engineering organization automatically creates a better fit.
4. SoftServe
SoftServe deserves consideration when developer AI is part of a wider enterprise AI and data agenda.
That distinction is becoming increasingly important. Engineering teams may want coding agents while security teams establish AI controls, data teams build new infrastructure, and business functions introduce their own generative AI applications.
Eventually, those initiatives need common technical and governance decisions.
SoftServe’s relevant capabilities span:
- AI and machine learning
- Generative AI
- Software engineering
- Data engineering
- Cloud development
- Enterprise applications
- Application modernization
- Technology consulting
This breadth can be useful when the organization wants to avoid treating engineering AI as a self-contained experiment.
The models and infrastructure available to developers may be influenced by cloud strategy. Data policies affect which context can enter AI environments. Enterprise AI governance can determine logging, access, and approval requirements.
SoftServe therefore makes sense when AI-assisted software delivery needs to connect with technology decisions already happening elsewhere in the enterprise.
5. Globant
Globant brings a product engineering perspective to the AI development conversation.
That matters because engineering productivity can be measured too narrowly. Producing code faster has limited business impact if requirements remain slow, testing creates delays, releases remain cumbersome, or the resulting functionality does not reach users any sooner.
Globant can be considered across areas including:
- AI-enabled development
- Digital product engineering
- Enterprise software
- Cloud development
- Data and AI
- Application modernization
- Product experience
- Technology transformation
The product angle makes Globant relevant to organizations interested in what AI changes across the delivery lifecycle.
Instead of asking whether developers complete individual coding tasks faster, teams can examine whether AI changes the time required to move from an idea through implementation, validation, and release.
For digital product organizations, that distinction can be far more useful than measuring lines of generated code or raw coding activity.
6. GlobalLogic
GlobalLogic is worth comparing for enterprises with broad and varied engineering portfolios.
A large organization may simultaneously maintain enterprise applications, cloud platforms, connected products, customer-facing software, and specialized systems. The same AI development approach will not necessarily perform equally well across all of them.
GlobalLogic’s digital engineering background gives it relevance in environments where AI adoption has to accommodate those differences.
Areas to explore include:
- AI-assisted engineering
- Digital product development
- Enterprise software
- Platform engineering
- Cloud solutions
- Data and AI
- Application modernization
- Engineering transformation
This type of portfolio creates an important adoption challenge.
One team may achieve strong results from AI-assisted test generation. Another could benefit primarily from documentation or refactoring. A third may work in an environment where extensive human verification remains necessary.
GlobalLogic can be worth evaluating when the enterprise needs AI adoption to fit several types of engineering work rather than standardizing one workflow everywhere.
7. Endava
Endava is another option for organizations connecting AI-assisted development with ongoing software delivery and modernization.
Many enterprises cannot create a separate AI transformation program and temporarily stop everything else. Teams still have product roadmaps, legacy applications need attention, cloud programs continue, and production systems require support.
AI has to enter while that work continues.
Relevant areas to discuss with Endava include:
- AI-assisted software engineering
- Application development
- Product engineering
- Application modernization
- Cloud engineering
- Data and AI
- Enterprise platforms
- Technology transformation
This makes Endava relevant when the objective is to introduce AI into an active engineering organization rather than construct a new delivery environment from scratch.
Evaluation should focus on how AI would be introduced without destabilizing existing delivery practices and how improvement would be measured once the initial experimentation period ends.
Ask for the denominator behind every productivity number
AI productivity claims can sound impressive while describing very different things.
“50% faster” could mean one developer completed one coding task in half the time. It could describe a particular category of work across several weeks. Or it could mean the engineering organization genuinely increased delivery capacity while maintaining quality.
Those are not interchangeable results. When a prospective partner presents a productivity figure, ask what was measured.
How many engineers participated? Which tasks were included? What was the baseline? Was review time counted? Was rework counted? How long did the measurement run? Did software quality remain acceptable?
N-iX’s reported results provide a useful illustration of why the denominator matters. Up to 95% savings on piloted tasks and a 27% increase in engineering velocity represent different levels of measurement and should be understood that way.
A credible partner should be comfortable explaining exactly where its numbers come from.
Watch what happens to senior engineers
One of the less obvious effects of AI adoption appears in senior engineering work. If junior and mid-level developers produce code faster, senior engineers may receive more output to review. They can become the verification layer for an increasingly productive generation layer. That can create a new bottleneck.
Measure where senior engineering time goes during pilots. Does AI reduce routine work for them as well? Are they spending additional hours reviewing plausible but flawed code? Has architecture work improved because they have more time, or declined because validation consumes their attention?
The answers can substantially change the economics of an AI workflow.
An enterprise that measures developer output without measuring expert review can conclude that productivity improved while its most expensive engineering capacity quietly became more constrained.
Make one team prove repeatability
A successful pilot often creates pressure to expand immediately. Run it again first.
Use the same workflow on another feature, repository, or comparable task. Then see whether the improvement survives.
AI performance can depend heavily on context. A neatly organized codebase with strong tests and good documentation may produce very different results from a legacy application with inconsistent architecture and limited test coverage.
Repeatability therefore deserves its own checkpoint.
This logic is reflected in N-iX’s APEX sequence: the move from Pilot toward Expand should follow evidence rather than enthusiasm.
If the second experiment produces a substantially different outcome, the organization has discovered an important condition that needs to be understood before scaling.
Compare the operating model, not the AI vocabulary
Thoughtworks is a strong option when AI adoption belongs inside a wider software engineering transformation. EPAM brings the organizational scale for large enterprise programs, while SoftServe is particularly relevant when development AI intersects with cloud, data, and enterprise AI initiatives. Globant offers a product-oriented perspective,
GlobalLogic can suit organizations with varied engineering portfolios, and Endava is worth examining when AI needs to enter ongoing development and modernization programs.
N-iX offers a distinctive route for organizations that want evidence before expansion. Its Pragmatic AI Software Engineering approach, APEX framework, reported engineering results, and attention to security and auditability make measurement part of adoption rather than something calculated after deployment.
When comparing AI-augmented development services, the decisive question is increasingly practical: what happens between giving engineers access to AI and proving that the organization actually builds software better? The strongest partner should have a detailed answer.