The software development agency model is going through its biggest change since the shift to agile, and this one is moving faster.
Publicly traded firms are restructuring business models that worked for two decades. Globant, a 27,000-person consultancy with $2.1 billion in revenue, launched a subscription-based delivery model after their CEO acknowledged on their Q4 2025 earnings call that it was the first time in 22 years they could grow without proportionally hiring more people. EPAM, with 50,000 employees, is shifting away from time-and-materials billing. SoftServe says they've cut manual engineering effort by 90% through autonomous AI agents.
If you're evaluating development partners right now, the old playbook doesn't apply. The question isn't whether an agency uses AI. It's whether AI has actually changed how they work, or whether it's just on their homepage.
Who we are and why we wrote this: We're Airdev, a software development agency based in San Francisco. We've shipped over 1,000 projects for clients including Fortune 100 companies. We compete in this market every day, which means we have informed but not neutral opinions about it. We've put ourselves on this list alongside our competitors, evaluated with the same criteria. Where we have a perspective, we'll say so.
Two types of AI-powered agencies
After watching how agencies across the industry have adopted AI over the past year, we see two distinct approaches. They look similar on a website but feel very different when you're actually working with them.
AI-first agencies have rebuilt how they work around AI. It's not a tool they added to an existing process. It's the process itself. AI shapes how they scope projects, make architecture decisions, write code, and run QA. The humans on these teams spend their time on judgment and strategy rather than repetitive implementation.
Established firms adding AI to proven practices. These are big consultancies and well-known dev shops that have equipped their engineers with tools like Copilot and built internal AI workflows. They bring decades of trust and enterprise relationships. AI makes them faster at what they already do, but it hasn't changed what they do.
The distinction matters primarily because of cost structure. AI-first agencies tend to quote faster timelines and lower prices because their operations are designed around AI efficiency. Established firms bring predictability and bench depth, but their overhead hasn't changed much, and their billing models are still catching up.
Neither approach is automatically the right choice, but you should know which one you're hiring.
Here are 11 agencies worth knowing about, including us.
AI-first agencies
Airdev // San Francisco, CA
Disclosure: this is our company.
Airdev has been around since 2015 and has shipped over 1,000 projects. We're currently rebuilding our entire delivery process around AI, and we think we're well positioned to do that because we've spent years helping organizations implement AI into their own operations. We understand the change management, workflow design, and process integration challenges because we've solved them for clients. Now we're applying that same thinking to how we build software.
The model is straightforward: AI handles what it's good at, including writing code, automating repetitive work, moving through documentation, and rapid prototyping. Our team handles everything AI is bad at. That team includes project managers, product strategists, software engineers, and designers who know how to translate business problems into working software and how to manage the human parts of delivery: client communication, architecture trade-offs, scope decisions, and quality control.
We're focused on getting the human-AI mix right, building strong handoff processes between AI output and human refinement, and keeping the most talented people on the team. We haven't figured it all out. But we have a decade of shipping real products and a ground-up AI process, which is a combination most agencies can't claim from either direction.
HatchWorks AI // Atlanta, GA
HatchWorks was founded in 2016 as a traditional dev shop and pivoted toward AI after ChatGPT launched. They have about 130 to 150 employees across the US and Latin America, and they've been named to the Inc. 5000 and the Inc. AI Power Partner list.
Their proprietary methodology is called Generative-Driven Development (GenDD™), a framework that blends AI agents with human engineering across the delivery lifecycle. They also offer a fractional Chief AI Officer service, which addresses a gap many enterprises face: needing help figuring out where to apply AI before anyone starts building anything. They serve healthcare, financial services, energy, and retail, and they use a nearshore model with teams distributed across the US and Latin America.
AE Studio // Los Angeles, CA
AE Studio splits its time between production software development and an AI research lab. Their team includes researchers from Harvard, Caltech, and Yale, and they've published over 167 papers on topics including AI alignment and brain-computer interfaces.
On the delivery side, they've completed 323 projects for clients including Walmart, Samsung, and Berkshire Hathaway, building things like conversational AI avatars, multi-agent systems, and medical device software. Their pitch is that the research informs the client work and the client work stress-tests the research. Whether that research depth makes a meaningful difference on a typical custom software project is a fair question to ask.
First Line Software // Newark, DE
First Line Software takes a different approach from most agencies on this list. Their proprietary methodology, RACE Mode (Rapid AI-Coded Engineering), treats AI agents as the builders rather than assistants. An AI Principal Engineer runs specialized agents for code generation, testing, DevOps, and documentation, while humans set direction and review output through what they call Mini-Pods.
They claim to deliver production-ready systems in days rather than weeks or months. Whether that holds up across complex enterprise projects with legacy systems and unclear requirements is the question buyers should push on, since "production-ready" can mean very different things depending on the situation.
Established firms integrating AI
These are proven companies working out how to add AI to processes that already work at scale.
Globant // New York, NY (NYSE: GLOB)
Globant launched AI Pods in June 2025, a subscription model that uses token-based metered capacity instead of billing by the hour. They're reporting 45 to 60% margins on these engagements, well above traditional services delivery. Their GEAI platform connects to over 140 LLMs, and they've built agents for product definition, prototyping, design, testing, and code fixes.
The subscription model is new for professional services, and it's worth asking how well it works for complex, custom projects versus more standardized builds. Token-based pricing sounds efficient in theory, but most real software projects involve ambiguity and scope changes that don't fit neatly into a metered model.
Thoughtbot // Boston, MA
Thoughtbot has been around for two decades with over 1,000 clients, primarily known for Rails development and code quality. They've been publicly documenting their AI integration process through dozens of blog posts, with their CEO co-authoring many of them.
In February 2026 they launched ReadySetGo, an AI application generator that produces first versions of Rails apps following their own engineering standards. They've also built custom Claude Code Skills for tasks like FDA documentation, Postman collection generation, and sprint-scoped delivery. Their focus is narrower than most agencies on this list. They're strongest in the Rails ecosystem and with product-stage companies rather than large enterprise builds.
Slalom Build // Seattle, WA
Slalom operates at a different scale than most agencies here, with about 14,000 employees, over $3 billion in revenue, and 45 offices.
They report 95% Copilot adoption across pilot teams, with top performers seeing 40% productivity gains. Their 60-day Acceleration Program includes standardized prompt libraries and reusable workflows. A case study with a health insurer showed 2.5x code output with no quality drop, and one client is projecting up to $150 million per year in savings. These are self-reported numbers from pilot teams, though, so how broadly they apply across a 14,000-person organization is an open question.
EPAM Systems // Newtown, PA (NYSE: EPAM)
EPAM has roughly 50,000 employees and $3.6 billion in revenue. Their AI/Run™.Transform Playbook, launched in October 2025, bundles AI-native delivery with consulting and partner technologies into a repeatable approach.
The more notable development is their pricing shift. EPAM is moving from time-and-materials to outcome-based and fixed-price deals as a hedge against "AI deflation," the concern that AI will shrink the billable hours their revenue model depends on. Whether outcome-based pricing actually works in practice or just shifts risk to the client is something buyers should examine carefully. They were also recognized in The Forrester Wave™: Modern Application Development Services.
SoftServe // Austin, TX
SoftServe has over 12,000 employees and has been around since 1993. Their Agentic Engineering Suite, launched in February 2026, deploys autonomous AI agents across every phase of development. The company claims up to 90% reduction in manual effort, a number that deserves scrutiny. Buyers should ask what kinds of projects that applies to and how it's measured.
They've also created a new role called "Intelligence Engineer," a human whose job is overseeing AI agents, setting direction, and running quality checks. Their internal numbers since adopting AI tooling show dev time down 20%, QC effort down 25%, test automation down 20%, and documentation down 17%. They're an NVIDIA Elite Partner building on NVIDIA Blueprints.
Productive Edge // Chicago, IL
Productive Edge has focused exclusively on healthcare, positioning themselves as a "Healthcare AI Factory." They have about 240 employees with offices in Chicago, Poland, and Ecuador.
They embed what they call Factory Pods with client teams to build healthcare AI solutions using their proprietary Boost framework, which provides reusable agents, decision logic, and workflow patterns. Their products include Care Advisor, a productivity tool, and NexAuth for prior authorization. They've made the Inc. 5000 four consecutive years. The vertical focus is a deliberate trade-off: deep healthcare expertise, but limited applicability outside that space.
How to evaluate an AI-powered agency
After 1,000+ projects and a year of rebuilding our own process around AI, here's what we'd ask any agency on this list, ours included.
"Where exactly does AI touch your process?" Get specific. Which phases (scoping, architecture, development, testing, deployment), and what tools and agents are involved? If the answer is vague, the process probably is too.
"What do humans still own?" The best shops can draw a clear line between what AI handles and where human judgment takes over. Product strategy, architecture decisions, client communication, security review. If an agency says "everything is AI-assisted" but can't tell you where humans are essential, that's a red flag.
"How do you manage the handoff between AI and humans?" This is where most agencies are still figuring things out. AI can produce a first draft of code quickly, but someone has to review it, refine it, and decide whether it actually solves the right problem. The quality of that handoff process matters more than the speed of the initial generation. Ask to see a real project where AI produced the first version and humans refined it. What did AI get right, and what needed fixing?
"How does AI change what I pay?" If AI makes their developers 40% faster and they still charge the same hourly rate, that efficiency gain goes to the agency, not the client. Ask about outcome-based pricing, fixed-price deals, and tiered models. Agencies that are confident in their AI process should be willing to tie fees to results.
"How good are your people?" This might sound obvious, but in an AI-powered model the quality of the humans matters more, not less. AI can generate a lot of code quickly, but deciding what to build, managing trade-offs, communicating with stakeholders, and catching subtle errors all require experienced, talented people. Ask about the team you'll actually work with, not just the agency's overall headcount.
Where this is heading
The biggest question in this space right now isn't which AI tools an agency uses. Tools change and improve constantly. The real differentiator is how well an agency manages the relationship between AI and humans.
AI is very good at generating code, writing documentation, and moving through repetitive tasks at speed. It's bad at understanding business context, making judgment calls about trade-offs, communicating with clients, and knowing when a technically correct solution is the wrong answer for the situation. Every agency on this list is somewhere in the process of figuring out where that line falls for different kinds of work.
We think the agencies that come out ahead will be the ones that get three things right. First, finding the right human-AI mix for each type of project and each phase of delivery, rather than applying a one-size-fits-all ratio. Second, building strong processes for the handoff between AI-generated output and human refinement, because that handoff is where quality is either preserved or lost. And third, investing in having the most talented humans on the team. In a world where AI handles more of the mechanical work, the premium shifts to people who can think clearly about product strategy, anticipate problems before they surface, and make good decisions under ambiguity.
The billable-hour model is changing too. When developers are significantly faster with AI, the math behind hourly billing stops working for either the agency or the client. Some firms are already moving to outcome-based or subscription pricing. Buyers will have more pricing power than they've had in years, and they should use it to ask how AI-driven efficiency gains are being shared.
The "vibe coding" wave is real. Small shops are building MVPs with Cursor, Bolt, and v0 in days. These tools are impressive for getting something in front of users quickly, but there's a wide gap between a prototype that demos well and software that handles real user load, survives an audit, and holds up over time.
This is how we see a market that's changing fast. We tried to call it straight for every company on this list, ourselves included. If any of this is useful as you evaluate partners, we're glad it helped.
