10 Best AI-Driven Legacy Modernization Companies in 2026

Picking an AI-driven legacy modernization company in 2026 carries more weight than it did two years ago. Deloitte’s 2026 Insights analysis found that technical debt accounts for 21% to 40% of an organization’s IT spending. Every dollar spent on the wrong vendor compounds against that budget.

Established codebases carry years of accumulated complexity. Running AI across them without first understanding how they behave introduces risk at the speed of automation. Standard selection criteria don’t surface that risk.

This guide is for engineering leaders at established software companies who need to pick a modernization partner. You will get a ranked list of the 10 best companies, a comparison table, a clear read on what each is best for, and a decision framework built around codebase fit, governance, and delivery proof.

Key Takeaways:

• GoGloby is the embedded option for PE-backed established platforms where code privacy and Agentic SDLC governance are non-negotiable.
• IBM is the first call for IBM Z mainframe estates. COBOL, JCL, PL/I, and Db2 at scale is where watsonx Code Assistant for Z has the strongest documented outcomes.
• HCLTech, Capgemini, Accenture, Infosys, and Wipro serve large, multi-system portfolios. Scale and geography are the differentiator, not codebase-level depth on a single platform.
• Cognizant carries sector-specific depth in BFSI and financial services mainframe. EPAM is the go-to option when Anthropic’s stack is the preferred AI tooling for data platform work.
• Hexaware trades depth for speed. RapidX is an agentic-first platform built for companies that want modernization moving without large enterprise engagement overhead.

What Is an AI-Driven Legacy Modernization Company?

An AI-driven legacy modernization company is a partner that uses AI inside the development process itself to make an established codebase safer, faster to change, and ready for modern delivery.

A traditional modernization vendor replaces technology. An AI-driven one changes how engineers work with it. It’s a team that ships faster with lower risk, backed by a documented process that proves it.

Core Legacy Modernization Services

Legacy modernization covers the full application lifecycle. It starts with assessment, dependency mapping, and business-logic discovery. Execution follows. Testing, cutover, and post-launch support close it out.

In practice, a team modernizing an undocumented .NET monolith with a shared database runs discovery first. That work maps which modules are safe to refactor independently and which ones have to wait for the database layer to stabilize. Decommissioning comes after the team owns the new system.

How AI Changes Legacy Modernization

AI handles the analytical and repetitive work faster than a manual review can. That includes reading the codebase, extracting rules, generating tests, and converting code under engineer oversight.

AI misses edge cases, makes incorrect dependency assumptions, and produces output that needs security review before it reaches production. Consider a COBOL system with no documentation. Domain experts still need to review what AI surfaces.

Modernization Company vs. Migration Provider

A migration provider moves applications, databases, or infrastructure to a new environment. A modernization company changes what the system does and how it’s maintained going forward.

Migration works when the codebase is sound, and the problem is infrastructure. When architecture, code quality, or business logic is the constraint, you need modernization. Broadridge’s 2025 Digital Transformation Study reported that 46% of firms agree their legacy technology and systems are limiting their resiliency strategy. Picking a migration provider for a maintainability problem delivers a system with the same issues running in a different place.

For a breakdown of both paths, including when to combine them, see Application Migration and Modernization.

What Are the 10 Best AI-Driven Legacy Modernization Companies in 2026?

The best AI-driven legacy modernization companies in 2026 treat AI as a development tool. Their workflows use it to map codebases, generate tests, and accelerate transformation under engineer oversight. The shortlist below ranks firms on AI workflow depth, stack evidence, code privacy controls, and verified client outcomes.

1. GoGloby: Embeds an AI Solutions Architect into the client’s engineering team to modernize established, business-critical software under a governed Agentic SDLC.
2. IBM: Provides first-party AI tooling for IBM Z mainframe environments, targeting COBOL, JCL, PL/I, and Db2.
3. HCLTech: Runs AI Force.Software.Mod, a dedicated modernization platform covering Java, COBOL, .NET, and mainframe environments at global delivery scale.
4. Capgemini: Uses GenAI and agentic AI to extract business rules from COBOL and mainframe systems inside a structured delivery methodology.
5. Accenture: Deploys Gemini Enterprise and GenAI accelerators across large multi-system portfolios in Java, COBOL, .NET, and cloud-native environments.
6. Infosys: Integrates OpenAI Codex into Topaz Fabric for AI-assisted code analysis and transformation across Java, .NET, and COBOL.
7. Cognizant: Runs 3 AI-built platforms with documented depth in BFSI, healthcare, and insurance mainframe environments.
8. EPAM: Pairs migVisor and Maestro with over 1,300 Claude-certified architects for data platform and cloud modernization.
9. Hexaware: Runs RapidX, a proprietary agentic AI platform covering the full development lifecycle from architecture through testing.
10. Wipro: Combines ModerniZ and FullStride Cloud for full-stack legacy transformation and cloud migration to AWS and Azure.

How We Evaluated These Legacy Modernization Companies

We evaluated 30 companies before narrowing to this list of 10. Ratings came from Clutch, G2, and Gartner Peer Insights. Company profiles were built from vendor documentation and published case studies. Companies with no publicly documented modernization outcomes were excluded.

• AI workflow depth: How far the firm has integrated AI into the modernization process, including code analysis, business-rule extraction, test generation, and review gates.
• Legacy stack evidence: Verified delivery in the environments this list covers, including COBOL, Java, .NET, mainframe, and proprietary databases, with named client outcomes where available.
• Governance and security controls: A documented model for code privacy, access restrictions, and audit trail per engagement.
• Business-logic preservation: Evidence that the firm maps what the system actually does before transforming it, including characterization tests or behavioral equivalence checks.
• Delivery model fit: Whether the engagement structure matches the codebase type, including embedded versus enterprise services and minimum scale requirements.

The table below maps each company’s primary fit, named AI capability, legacy environment coverage, and delivery model. Use it to shortlist 2 or 3 before reading the full profiles.

Read more: AI in Regulated Industries in 2026: Healthcare, Fintech, and Enterprise SaaS and 10 Best AI Test Automation Tools in 2026: A Complete Guide.

1. GoGloby | Applied AI Engineering Partner

Founded in 2021 and headquartered in Dover, Delaware, GoGloby helps established software companies modernize business-critical applications by forward-deploying an AI Solutions Architect into the existing engineering team. The firm works with 100+ companies across 10 industries.

Embedded in under 4 weeks, the Architect maps your codebase, builds characterization tests, then modernizes under the Agentic SDLC. The AI Development Intelligence Layer delivers board-ready sprint metrics.

Pros:

• Your code stays in your own environment throughout the engagement.
• Only 4% of GoGloby’s curated outbound pipeline clears the multi-layer assessment.
• 120-day performance guarantee built into every engagement.

Cons:

• Specialist model. Not suited for large multi-system portfolios or greenfield builds.
• Embedded-only model. Not suited for teams that require a fixed-bid or fixed-scope contract.

Best for: PE-backed mid-market companies on an exit clock, where code privacy, Agentic SDLC discipline, and board-level modernization proof are non-negotiable.

2. IBM | Enterprise AI and Mainframe Modernization

The original enterprise computing company, IBM was founded in 1911 and is headquartered in Armonk, New York. Mainframe modernization has been a core practice there for decades.

Their primary AI tool for this work is watsonx Code Assistant for Z, now at version 2.8. The 2026 update added an agentic workflow that chains dependency mapping, impact analysis, code generation, and compilation verification automatically. COBOL and Db2 are the native targets.

Pros:

• Extensive first-party expertise in IBM Z, COBOL, JCL, PL/I, and Db2, built from the platform’s origin.
• Agentic workflows remove manual coordination from long modernization chains.

Cons:

• Fit narrows sharply to IBM Z environments. Multi-platform estates need separate tooling.
• Enterprise model means long procurement cycles and large minimum engagement sizes.

Best for: Large enterprises running IBM Z mainframes where COBOL and Db2 are the primary modernization targets.

3. HCLTech | AI-Led Application Modernization

Founded in 1976 and headquartered in Noida, India, HCLTech is one of the largest IT services companies in the world. It operates across 60 countries.

For legacy modernization, HCLTech runs AI Force.Software.Mod, the dedicated modernization module inside its AI Force platform. It uses GenAI and agentic AI to analyze, transform, and validate legacy applications. Java, COBOL, .NET, and mainframe environments are all in scope.

Pros:

• AI Force.Software.Mod is a dedicated modernization module, separate from HCLTech’s broader AI Force platform.
• High-volume delivery capacity across multiple geographies.
• Broad legacy environment coverage from mainframe to modern stacks.

Cons:

• Not structured for a single codebase or targeted project. Minimum engagement size favors large, multi-system estates. 

Best for: Global enterprises managing large, multi-system legacy estates where scale and geography coverage matter as much as technical depth.

4. Capgemini | GenAI-Powered COBOL and Mainframe Modernization

Capgemini, founded in 1967 and headquartered in Paris, France, is one of the world’s largest technology and consulting firms. Enterprise application modernization has been part of its practice for decades.

For COBOL and mainframe work, Capgemini uses GenAI assistants and agentic AI to extract business rules and convert legacy databases to modern formats. Automated testing runs throughout. The approach combines AI tools and delivery methodology into a single practice.

Pros:

• Strong COBOL extraction capability with automated business rule discovery.
• Automated testing built into the workflow, not added on afterward.
• Cross-sector client base means documented experience across industries.

Cons:

• No proprietary platform. Delivery depends more on methodology than owned tooling.
• Engagement quality varies significantly at this delivery scale.

Best for: Large enterprises with heavy COBOL and mainframe exposure that want a global delivery partner with cross-sector depth.

5. Accenture | AI-Led Portfolio Modernization at Enterprise Scale

Founded in 1989 and headquartered in Dublin, Ireland, Accenture runs more than 700,000 employees across a global professional services practice.

Accenture applies GenAI accelerators across discovery, code analysis, and migration in Java, COBOL, .NET, and cloud-native environments. In April 2026, it expanded its Google Cloud partnership, adding Gemini Enterprise as the AI model powering large-scale delivery.

Pros:

• Portfolio-scale capability for enterprises modernizing multiple systems at once.
• Gemini Enterprise puts Google’s latest agentic AI at the center of delivery.
• Cross-industry experience from decades of global enterprise work.

Cons:

• Works best at portfolio scale. Single-codebase programs are a poor fit.

Best for: Enterprises with complex, multi-system modernization programs that need a global partner capable of operating at portfolio scale.

6. Infosys | AI-Led Legacy Modernization with Topaz Fabric and OpenAI Codex

From Bengaluru, India, Infosys has grown since 1981 into one of the world’s largest IT services firms, operating across more than 50 countries.

For legacy modernization, Infosys runs Topaz Fabric. It’s a cross-platform agentic AI suite covering code analysis and transformation across Java, .NET, and COBOL. In April 2026, Infosys integrated OpenAI Codex into Topaz Fabric, adding direct code generation to its existing analysis capabilities.

Pros:

• Topaz Fabric is model-agnostic. Your team isn’t locked into a single AI vendor.
• Codex integration adds direct code generation to a mature AI platform.
• Global delivery across 50+ countries.

Cons:

• Topaz Fabric is cross-domain, not modernization-only. Legacy-specific depth varies by engagement. 

Best for: Large enterprises that want AI-led modernization backed by a multi-vendor AI strategy and global delivery coverage.

7. Cognizant | AI-Led Modernization for BFSI and High-Volume Environments

Cognizant has built its reputation in BFSI, healthcare, and insurance since its 1994 founding in Teaneck, New Jersey. These industries run some of the oldest and most business-critical mainframe environments in the world.

Its modernization stack spans 3 platforms. Skygrade handles cloud transformation. Flowsource accelerates full-stack engineering. Ignition modernizes data and analytics. All 3 have AI built in, covering COBOL, Java, and mainframe environments with particular depth in financial services.

Pros:

• Separate tools for cloud, engineering, and data modernization mean you engage the right platform for the right layer.
• BFSI focus means documented experience in financial services mainframe environments.
• US-headquartered with strong domestic delivery presence.

Cons:

• Platform breadth adds complexity. You need to map the right tool to the right problem.
• Less relevant for verticals outside BFSI, healthcare, and insurance.

Best for: Financial services, insurance, and healthcare companies running legacy mainframe environments where sector-specific depth matters.

8. EPAM | Platform Modernization with Anthropic-Grade AI Tooling

EPAM Systems, founded in 1993 and headquartered in Newtown, Pennsylvania, is an engineering-led technology services firm. An Anthropic partnership in early 2026 put over 1,300 Claude-certified architects on staff.

Their tools include migVisor for data platform discovery, Maestro for hybrid cloud management, and Claude for AI-assisted engineering work. migVisor turns opaque legacy data platforms into documented, queryable knowledge.

Pros:

• Over 1,300 Claude-certified architects on staff, targeting 5,000 by the end of Q3 2026.
• Maestro adds dedicated hybrid cloud management tooling to the modernization stack.
• Engineering-led model means named technical ownership per engagement, rather than rotating consulting teams.

Cons:

• Primary depth is in data platform and cloud modernization. Application-layer legacy work sits outside that core.
• Claude-certified capacity is still scaling. Not yet at full depth across all engagements.

Best for: Engineering-driven organizations modernizing complex data platforms and cloud environments where Anthropic’s AI stack is the preferred tooling.

9. Hexaware | Agentic AI Modernization at Speed

Hexaware was founded in 1990 and is headquartered in Mumbai, India. Its core modernization tool is RapidX, a proprietary agentic AI platform.

RapidX drives the full development lifecycle with AI-native agents, from architecture and code analysis to automated testing and documentation. A US airline modernization case study shows it running in production.

Pros:

• Factory partnership (2025) extends RapidX with agent-native software development capabilities.
• Full SDLC coverage in 1 platform reduces handoff friction.
• Faster time-to-start than most enterprise services firms.

Cons:

• Newer platform. Less proven on IBM Z or heavy COBOL mainframe environments.
• Narrow scope outside the platform’s defined capabilities.

Best for: Companies that want agentic AI driving modernization speed, without the overhead of a large enterprise services engagement.

10. Wipro | Full-Stack Modernization with Microsoft and AI Partner Ecosystem

Wipro, founded in 1945 and headquartered in Bengaluru, India, is one of the oldest firms on this list. Its modernization practice runs on 2 platforms. ModerniZ handles legacy application transformation. FullStride Cloud covers the cloud migration path.

ModerniZ combines Wipro’s proprietary tools with partner solutions and claims 40-50% productivity gains on migration timelines. FullStride Cloud targets AWS and Azure as the primary destination environments.

Pros:

• Wipro reports ModerniZ is in production across multiple client deployments.
• FullStride Cloud has dedicated tooling for both AWS and Azure migration paths.
• Scale and global delivery capacity for large, multi-system programs.

Cons:

• Mainframe-to-mainframe COBOL depth is thinner than IBM or Capgemini.

Best for: Enterprises with a clear cloud migration path that need a large, production-verified modernization partner with full-stack platform support.

Which Company Fits Your Needs?

IBM is the first call for IBM Z and heavy COBOL estates. GoGloby fits established software products that want an embedded AI Solutions Architect and client-controlled delivery. HCLTech, Capgemini, Accenture, Infosys, and Wipro align with larger multi-system programs where scale and global reach matter more than codebase-level depth on a single platform. The table below maps the most common scenarios to the best starting point.

• Your situation type: Most situations fall into one of 3 categories. Those are single established platform, sector-specific mainframe, or multi-system portfolio. That category is your first filter.
• Stack depth over company size: Mainframe environments running COBOL or IBM Z need teams built for that infrastructure. A single Java or .NET platform fits a different engagement model.
• Sector-specific mainframe risk: BFSI, healthcare, and insurance carry decades of accumulated business rules in the mainframe, often undocumented. Vendors with proven depth in your vertical catch gaps during preservation testing that a generalist review misses.
• The “Verify Before Signing” column: It names the specific evidence to request from the vendor you shortlist. Ask for it before a proposal is submitted.

Request the evidence in that third column before any vendor submits a proposal. A vendor who produces it quickly has likely run that engagement before, at your stack type and scale.

Which Legacy Modernization Capabilities to Compare?

Compare discovery, AI-assisted code understanding, business-logic preservation, code transformation, cloud and data modernization, testing, and knowledge transfer. Most vendors list the same phases on paper. What differs is what they can produce and prove across each one.

Discovery and Portfolio Assessment

A real discovery phase ends with artifacts, not a proposal. That means a risk-classified application inventory, documented dependencies between systems, and a clear disposition decision for each application in scope. If the output is a slide deck with a modernization recommendation, the assessment wasn’t real.

Disposition decisions matter. Not every application should be transformed. A vendor who always recommends modernization regardless of system age or cost hasn’t done the analysis.

AI-Assisted Code Understanding

Surface-level code summaries are a starting point. The harder problem is recovering what the system actually does. Business rules buried in conditional logic, undocumented constraints, and accumulated exceptions won’t appear in a module description.

Ask to see a business-rule extraction example from a production system, not a prepared demo.

Code Translation and Refactoring

Deterministic transformation tools handle structured conversions consistently. GenAI takes over where transformation rules aren’t fully defined, such as modularization or architecture changes. Most vendors who do this well use both.

Ask how converted code is reviewed beyond compilation. Passing tests isn’t the same as passing a security review or fitting the target architecture.

Business Logic Preservation

This is the capability most evaluations underweight. When it fails, it fails in production.

Rule extraction tells you what the code is supposed to do. Characterization tests and golden datasets tell you what it actually does. Any system in production for several years has accumulated exceptions and workarounds that don’t appear in any specification. If preservation relies on rule extraction alone, that gap shows up in production. Deloitte’s 2025 Tech Value Survey found that nearly 60% of leaders believe another 21% to 50% of enterprise value is still latent. 

For example, take a billing module in production for 15 years. Rule extraction maps the fee logic but misses the undocumented exception that’s been waiving late fees for a specific account tier since 2008. The modernized system charges those accounts on the first billing run.

Ask how the vendor traces behavioral equivalence between the existing system and the modernized one. Domain experts, not just engineers, need to be in that review.

Cloud and Data Modernization

Moving an application to AWS or Azure without changing the code improves infrastructure flexibility. It doesn’t improve architecture or maintainability. Ask the vendor what changes in the target state beyond the hosting environment. Cloud-native work involves containers, managed services, and integration changes. Rehosting does not. The vendor should be able to tell you which approach fits your system and why.

Schema conversion and data migration carry their own risk profile. Confirm the vendor’s capability includes the data layer, not just the application.

Testing and DevSecOps

AI-generated and converted code should go through the same quality gates as human-written code. In practice, that means static analysis, software composition analysis, and secret scanning as standard CI/CD gates. Regression suites and a rollback path should also be in place before anything reaches production.

Ask about test generation separately. A vendor using AI to accelerate transformation should generate test coverage as part of that work, not as a separate activity afterward.

Knowledge Transfer and Support

Without a contractual obligation, knowledge transfer gets deprioritized as the engagement winds down. That means architecture records engineers can use, runbooks for operations, and enough pairing for the team to understand what changed and why.

Post-launch support terms reveal confidence. Hypercare periods and named incident ownership after cutover matter. A vendor who defines these upfront is a different risk from one who doesn’t.

How to Choose a Legacy Modernization Company?

The right choice depends on your stack, your codebase sensitivity, and the delivery model that fits your team. How much runway you have before the board expects proof narrows the field fast. There is no universally right firm, only the right fit for your platform and your risk profile. The 5 steps below turn a shortlist into a decision you can defend.

1. Verify Experience in Your Stack

Request 2 named client contacts from engagements in your exact stack before the conversation goes further. A portfolio slide doesn’t tell you whether they solved the same type of problem. COBOL experience doesn’t transfer to a Java monolith. Mainframe depth doesn’t cover your specific ERP patterns.

When you call those contacts, ask what they’d do differently. The answers reveal how the vendor handles what they didn’t expect.

2. Inspect the AI Workflow

Have the vendor walk you through a live sprint under their AI workflow. How work gets generated, reviewed, and rejected tells you more than any policy document. A governed workflow has defined limits. The team knows what AI can handle without approval and what needs a senior engineer in the loop.

Pin down where the line is. If they can’t name it specifically, the workflow isn’t governed yet.

3. Request a Bounded Pilot

A pilot scoped to one live workflow gives you something no reference call can provide. The evidence comes from your own codebase, measured against your own baseline. Pick a workflow with a blast radius small enough to stop safely if something breaks.

Define what it should produce before it starts. The right measures are defect rates, production incident data, and velocity against your actual baseline.

4. Define Ownership and Controls

Get clarity on who owns what before the engagement starts. That covers the code AI writes, the documentation it generates, and the changes that outlast the contract. Then define the controls. An approval gate sitting in front of any action the team can’t easily undo is the baseline. Find out which of their controls are contractual and which are just process.

5. Evaluate the Commercial Model

The commercial structure determines how much risk you carry when delivery falls short. A time-and-materials contract with no baseline and no exit trigger transfers that risk to you. Push for a defined performance period with an exit clause before the proposal becomes a contract. Then confirm what you own when the engagement ends.

Before those conversations start, knowing where your codebase stands sharpens every question you ask. Our Application Modernization Assessment guide covers how to map risk, dependencies, and readiness before the first vendor meeting.

How Do Regional and Technology Factors Affect Your Shortlist?

Geography filters for legal and operational fit. Stack specialization filters for whether the vendor has solved your type of problem before. Most evaluations skip both and go straight to demos. By the time they circle back, they’ve invested time in vendors that can’t clear either filter.

United States and Canada

Check which legal entity signs the contract before you shortlist. That entity also needs to be the one holding your code. A US-domiciled vendor with offshore delivery staff can still create data access exposure you haven’t accounted for.

Delivery staff access is the piece most vendors don’t volunteer. Ask where they connect from and which subprocessors handle your environment. Whether code or production data gets copied outside that boundary is a separate question. If your board requires onsite presence in specific cities, verify actual delivery capacity, not just a listed office.

Europe

Start with which legal entity you’re contracting with and where it’s registered. Source code access and data residency are two separate risks. Confirm who can reach your environment and where they do it from. Prompts, logs, and model artifacts matter too. Find out where they’re stored and whether any of that leaves the EU.

Requirements vary by country and sector. Map the privacy, residency, and sector-specific requirements that apply to your engagement. Your legal team needs those answers before you evaluate any vendor’s delivery model.

Legacy Technology Specialization

The table below maps common legacy environments to vendors with documented outcomes in each. Use it to filter by stack before brand recognition or geography enters the shortlist.

For common stacks like .NET or Enterprise Java, most vendors on this list have documented coverage. Selection is competitive. For Delphi, Ruby on Rails, or proprietary databases, named client references are the only evidence that counts.

Industry Experience

Industry experience matters most when business rules are the primary complexity. Insurance, banking, healthcare, and public sector platforms carry decades of accumulated logic. A team that hasn’t worked in your vertical won’t recognize what’s at risk during preservation testing.

On this list, IBM and Cognizant have the most BFSI experience. IBM brings mainframe depth. Cognizant comes from a financial services platform focus. For manufacturing, transportation, or the public sector, request a named client in your vertical.

What Are the Common Selection Mistakes?

Most selection mistakes in legacy modernization come from evaluating vendors on surface signals instead of delivery fit. What looks strong in a proposal conflicts with what the codebase requires once work begins.

• Ranking companies by size alone: Buyers default to larger vendors as a proxy for quality. Company size predicts resource capacity, not delivery fit on a single codebase. Evaluate the engagement model and team structure first.
• Believing generic AI claims: Some vendors rebrand standard coding assistance as AI-driven modernization. Before shortlisting, get named capabilities, a documented workflow, and case evidence from a live engagement.
• Treating translation as modernization: Language conversion addresses the syntax layer only. Architecture, security, and data design require separate work. Check the proposal covers all three before agreeing to a path.
• Transforming before behavioral baselines exist: A vendor who starts converting code before mapping what the system actually does will overwrite rules the business runs on. Require a documented behavioral baseline before transformation begins.
• Choosing one strategy for every system: A vendor who applies the same approach to every system skips the per-application assessment that determines the right path. Ask for the disposition decision on each application before agreeing to a strategy.
• Signing before a pilot is scoped: A proposal without defined scope and success criteria is a forecast, not a commitment. Require both before you sign.

Read more: How to Choose an AI-Native Engineering Partner for Your Business and 10 Best Claude Code Companies to Build Production AI Software in 2026.

Conclusion

The right modernization partner gives your team a clear path from evaluation to execution. They align with your stack, operating environment, delivery model, and business priorities, with a process that gives your team visibility as the work moves forward.

Start by defining the scope, success criteria, and evidence you expect from the engagement. Use those requirements to structure vendor conversations, evaluate their proposed approach, and establish clear accountability before the work begins. That gives your team a practical basis for selecting a partner and moving into modernization with confidence.

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Sergey Matikaynen / CTO

Article author

Sergey Matikaynen is Co-Founder and CTO of GoGloby, where he owns the engineering standard behind 4x Applied AI Engineering. He has spent 16+ years building and leading software teams for companies across the US, Canada, and Europe — software architecture, agile delivery, and engineering leadership. At GoGloby, he sets the technical bar that Applied AI Software Engineers are vetted against, including certified Agentic SDLC mastery. He is a LinkedIn Top Voice in software development.

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