Twenty-six years ago, Forte Group was one person with one idea: build a quality assurance practice and do it well. This year, the company marked that anniversary with an all-hands built around a single theme, an AI-native future. Leadership from across the organization, from the founder to the CTO to the engineering team, took the stage to talk about how the company got here and where it is headed next. What follows is not just a retrospective. It is a look at what AI-native delivery actually requires, told through the lens of a company that has already gone through several reinventions.
Forte Group's founder, Slava Kreynin, started the company as a test automation and quality assurance consultancy. From there it evolved into a software development company, then an agile delivery organization, then a managed services provider. Each shift was deliberate. As Kreynin put it, the constant across all of it has been an ability to change and to keep evolving.
CEO Mikael Carlsson framed that same history in numbers. What began as one founder and one vision has grown to more than 750 people, entirely through organic growth rather than acquisition or outside capital, which is unusual in a services industry where most peers scale by buying growth. The company's footprint expanded from Chicago-based local delivery to national reach, then to nearshore delivery centers in Ukraine, Poland, Argentina, and Colombia, with Mexico added late last year. Onshore presence in the United States, United Kingdom, and Ireland continues to grow alongside it.
That growth has been paired with strong client retention. Carlsson pointed to a client satisfaction score of 75, more than 20 new client logos won in the past 12 months, and net revenue retention well ahead of industry norms, meaning existing clients are spending more with Forte Group, not less. Increasingly, clients are also handing over ownership of outcomes rather than simply headcount, a shift that matters a great deal heading into an AI-driven market.
Forte Group began its AI transformation in earnest roughly three and a half years ago. Today, nearly all client deliveries are supported by AI tooling in some form, and about one in five assignments now involves building AI-native products or systems for clients outright, a share that is growing quickly. That shift has meant deepening partnerships with Anthropic, AWS, and Databricks, and pushing teams toward relevant certifications to demonstrate real capability rather than surface-level familiarity.
Engineering leadership offered a candid view of what AI-first delivery means in practice, and it goes well beyond using a coding assistant. Large software teams of fifty or a hundred people working on a single product are no longer the default. Smaller teams, paired effectively with AI, can now produce comparable output. Manual work is being automated wherever it can be, exploratory testing being one of the few exceptions.
Role definitions are shifting too. Engineers are moving away from narrow specialization, front end versus back end versus a single microservice, toward end-to-end ownership of a working feature. Quality assurance is following a similar path, shifting from manual test execution toward a hybrid role where engineers review and refine AI-generated test scripts rather than writing every case by hand. The framing that resonated most: competition is no longer between AI and people. It is between people who use AI well and people who do not yet.
On the client side, demand clusters into a few consistent categories: embedding AI into existing products, applying AI to internal operations such as back-office and support functions, accelerating software delivery itself with AI tooling, and, increasingly, full organizational transformation. Clients are not only asking Forte Group to build with AI. Some are asking how their own internal teams should be structured, using Forte Group's delivery model as a reference point.
That shift has driven sharp growth in demand for product management talent specifically, since product owners sit at the intersection of technical delivery and business value, a role that becomes more critical, not less, as AI collapses old team structures. Most engagements now begin with a discovery phase, move into a pilot, and scale from there. It is a deliberate, staged approach rather than a wholesale replacement of existing systems.
One example from the event grounds this in practice rather than promise. On a lending client's platform, Forte Group's teams applied AI to process mortgage applications submitted as PDFs, Word documents, and scanned images, formats that used to require manual review or brittle, rules-based extraction that broke the moment a document did not match the expected structure. AI-based ingestion instead converts that mix of formats into structured data and runs preliminary validation before a human ever looks at it.
The underwriting layer goes further. AI aggregates data from bank statements, credit profile reports, and other source documents, then produces a documented rationale for a decision that includes the specific risks it identified, rather than an opaque score. For a fintech CTO evaluating a delivery partner, that combination, structured extraction paired with a traceable rationale, is a meaningfully different claim than "we used AI to write code faster." It is AI touching a regulated decision and leaving a record behind.
Forte Group is also applying AI internally. An internal tool called C Vision uses natural language search to help recruiters and resource managers find the right employee or candidate profile, currently in limited pre-production use. A separate initiative is building a bot to conduct initial technical interviews. Internal analytics dashboards now pull data from GitHub, Jira, and calendar systems using AI rather than deterministic, rules-based logic, which tends to break on messy or incomplete data in ways AI tooling does not.
Not every client is fully bought in, and leadership was candid about that too. The concerns that do come up center on data ownership, vendor lock-in, and compliance in regulated industries like healthcare and financial services. Forte Group's answer is structural: clients own the custom software and data built for them, engagements are built to avoid locking clients into a single AI lab or cloud provider, and regulated-industry work follows the protocols those industries require, layered on top of whatever policies the client itself has in place. Most engagements start as a proof of concept in a controlled scope before expanding, which does most of the work of managing that risk before it ever touches a production financial system.
None of this is friction-free, and it is worth being direct about where the real limits sit.
Uneven readiness across environments. A modern, AI-ready tech stack and a legacy one require fundamentally different approaches, and teams working in older architectures cannot simply apply the same AI harness used on a greenfield project.
A real upskilling curve. Converting a manual QA engineer into an AI-enabled hybrid tester, or a specialized front-end developer into a full-stack product builder, requires deliberate training. It does not happen by exposure alone.
Tooling that resists standardization. AI harnesses have to be tailored to each project's repository structure, architecture, and technology stack. Knowledge-sharing sessions and demos help, but a reusable playbook across all projects is not yet realistic.
Orchestration risk. Without discipline, AI-generated code, documentation, and infrastructure create their own sprawl. Repository structures are already changing shape to accommodate AI tooling, and DevOps functions are taking on more responsibility for observability and traceability as a result, not less.
An AI-native future does not mean fewer meaningful roles. It means less repetitive toil and a higher premium on judgment, communication, and trust, the things AI cannot yet replicate. For Forte Group, turning 26 while pushing through this transition has meant staying true to the same trait that got the company here in the first place: the willingness to keep changing.
A few practical takeaways for engineering leaders navigating the same shift:
If AI is already touching decisions in your own pipeline, or you are still working out where it safely can, that is the exact scoped conversation Forte Group starts every engagement with, a proof of concept in a contained environment before anything reaches production. Reach out to the Forte Group team to talk through where that starting point would be for your organization.
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