If you searched for "Infopulse" expecting to evaluate a Ukrainian nearshore engineering firm, stop: that company no longer exists as an independent entity. Infopulse was fully absorbed into Tietoevry Create in 2025, and the parent announced a further rebrand back to Tieto in November 2025. What you're actually evaluating today is a delivery organization inside a 24,000-person Nordic IT services group, and that distinction matters enormously for how you should think about engaging them. The bottom line: Tietoevry Create (formerly Infopulse) is a credible, stable choice for large European enterprises buying multi-year managed services programs. It is a poor fit for engineering leaders who want to select, interview, and directly control individual engineers.

The brand confusion here is real and worth resolving before anything else. The Infopulse factsheet positions the former organization as a delivery network within Tietoevry Create, contributing multi-country capabilities across Poland, Germany, Bulgaria, Ukraine, and Brazil. The infopulse.com domain now redirects. If you're referencing old case studies or a previous vendor relationship with "Infopulse," you're now dealing with a business unit inside a publicly listed Nordic group, not a nimble specialist outsourcer. Tietoevry operates across more than 90 countries with customers spanning banking, telecom, manufacturing, and the public sector. Its Create division handles digital engineering and software development, which is where the former Infopulse talent base now lives. The engineering relationship that might have felt direct and boutique in 2022 now sits inside a broader corporate services portfolio with correspondingly more process layers.

Core service lines inherited from Infopulse and now delivered through Tietoevry Create include:

The Tietoevry Create model is explicitly built around large-scale, long-term engagements. Think 18-month minimum commitments, enterprise procurement cycles, and multi-team delivery units allocated to a program, not individual engineers placed against a specific role description. One reference point that anchors their enterprise credibility: Tietoevry Create holds an 18-year engagement with Bosch. That kind of relationship tenure signals something real about delivery consistency and enterprise trust. It also signals exactly what kind of buyer this model is optimized for.

This is where the Tietoevry Create model diverges most sharply from what most engineering leaders mean when they say "I need to hire an engineer." In a program-based services model, resource allocation happens internally. Tietoevry Create draws from its existing delivery bench, meaning engineers already employed by the organization, and assigns them to your program based on availability and skill matching done by the services organization itself. You do not run a search. You do not choose from a candidate slate. You do not conduct a technical interview that determines whether this specific person gets the role. For many enterprise buyers, that's fine. Procurement teams in banking and telecom are accustomed to this. The vendor assumes delivery accountability and you hold them to SLAs. The engineering relationship is a business relationship. For a CTO at a growth-stage company who wants an AI-native backend engineer who can decompose a product brief and ship autonomously, this model creates a fundamental mismatch. You're not getting a person. You're getting an allocated resource within a program structure, and changing that person if it isn't working requires renegotiating the engagement, not conducting a new interview.

To be fair: Tietoevry's 30-year operating history and the depth of its delivery network across Eastern Europe and Brazil means its engineer bench has genuine depth in enterprise-grade infrastructure, legacy modernization, and systems integration. If you're running a bank and need a team to own a multi-year SAP migration, these are not amateur operators. The honest limitation is evaluability. Because buyers don't select individual contributors, it's difficult to assess talent quality at the engineer level before the engagement starts. Review sentiment on platforms like G2 for legacy Infopulse reflects generally positive marks on delivery consistency and technical depth for long-running infrastructure projects, with more friction noted around responsiveness and flexibility when scope changes mid-program. That pattern is typical of large services organizations, where the economic model is optimized for stability, not pivoting.

Enterprise program initiation timelines are measured in weeks to months, not days. Procurement, legal review, SOW negotiation, onboarding of a delivery team, and ramp-up to productive output: these are real timelines that large organizations accept because the engagement size justifies the overhead. For a company that needs a senior engineer contributing within two weeks, that cycle is a dealbreaker. This is not a criticism of execution quality at Tietoevry Create. It's a structural property of how enterprise services organizations work, and it's worth being honest about it.

The most substantive issue for engineering leaders evaluating this model is not delivery quality. It's opacity and control at the individual level. Large services-organization economics load management overhead, account management, delivery coordination, and margin into every engineer-hour billed. The cost you see does not reflect what the individual engineer earns or what productivity you should expect on a per-person basis. More importantly, you cannot evaluate that individual before they start, you cannot easily replace them if they aren't performing, and you are directing your feedback to a program manager rather than to the engineer directly. In an era where AI-native development capability varies enormously between individual engineers, buying a "team" allocated by a vendor is a significant information disadvantage. The engineer who prompted through a complex feature in three hours and the one who took three days may both exist within the same delivery team, and you may never know the difference.

Nextdev was built for a different buyer profile and a different era of engineering. The core difference is individual selection with real evaluation signal. Every engineer in Nextdev's 10,000+ vetted pool goes through a live 30-minute build interview that evaluates how they decompose a product brief and execute through AI-native prompting. You see the actual person work. You decide if they get the role. That's not a feature a program-based services organization can replicate structurally, because their model depends on internal allocation, not client-facing individual selection. Nextdev's sourcing is also fresh-search rather than bench-based. Proprietary LinkedIn sourcing and response-learning data runs a new search against your specific role requirements rather than drawing from whoever is currently available on an existing delivery roster. That matters because the engineer who fits your stack, your team culture, and your current AI toolchain is rarely the first person off an internal bench. On the engagement model: Nextdev employs the engineer and gives you one contract and one invoice for a person you direct daily. That's EOR-backed simplicity compared to the SOW, governance, and program-management overhead of an enterprise services engagement. It's not a trade-off that makes sense for every buyer, but for engineering leaders who want a specific senior engineer shipping product within two weeks, it's the only model that actually delivers that.

Be honest with yourself about what you actually need:

If that's your situation, Tietoevry Create is a credible, stable choice with a track record that earns the business.

The profile where Tietoevry Create is the wrong answer is equally clear:

Tietoevry Create (the entity that was Infopulse) is a legitimate, well-resourced enterprise IT services organization. Its 30-year track record, listed-company stability, and multi-country delivery network are real advantages for a specific buyer: large enterprises that buy programs, not people. The mistake is treating it as a hiring platform or a way to find and select individual engineers. It was never that, and the integration into Tietoevry has moved it further from that model, not closer. If your 2026 engineering strategy involves building AI-native product teams where individual capability is the competitive variable, you need a model that lets you see, evaluate, and select each engineer before they join. The best engineering teams in 2026 will be smaller, more capable, and AI-augmented. Finding the right individuals for those roles matters more than it ever has. That's a search problem, not a procurement problem, and it calls for a search approach built for the AI era.