On the polished end of the artificial-intelligence market, a model writes a poem, generates a product shot, or answers a question in seconds. At the other end sits a global pharmaceutical company with decades of clinical data, a bank with regulators at the door, or a manufacturer whose factory cannot simply reboot. Marlabs works at that second end. Its job begins after the demo, when somebody must connect an AI system to old applications, clean the data, set permissions, redesign a workflow, test the result, train the users, and remain accountable when the novelty wears off.
That is an advantageous place for a 30-year-old technology-services firm to find itself. Marlabs was founded by Siby Vadakekkara in the mid-1990s with a straightforward brief: deliver useful, cost-conscious technology for businesses navigating a changing digital world. Today the privately held company has more than 2,200 employees, a New York headquarters, delivery operations spanning seven countries, and a client list that it says includes more than 30 Fortune 500 companies. CEO Thomas Collins, who took over in 2023 while Vadakekkara became chairman, is repositioning that machinery for the age of enterprise AI.
The difficult middle
Marlabs calls its new flagship suite AgilityAI. The name may sound like another platform arriving in a crowded market, but the substance is closer to an operating method. It combines a strategic discovery framework, a catalog of reusable agentic-AI accelerators, and a governance model. The company reduces the method to three verbs - Align, Build, Control. First identify a use case with economic value and prepare the people and data. Then engineer it with reusable components. Finally, put security, oversight, measurement, and continuous improvement around the system.
The premise is that most companies do not lack AI ambition. They lack the connective tissue required to turn an experiment into a dependable capability. A call-center assistant, for example, is not merely a language model in a chat window. It needs access to customer history, a reliable way to retrieve knowledge, rules about which actions it may take, monitoring for bad answers, escalation to a human, and a cost structure that still makes sense at millions of interactions. The model is only one piece of the bill.
“The problem isn't that companies lack AI ambition; it's that they lack a holistic process to deliver it.”Thomas Collins · Chief Executive Officer
PromptRouter, one of Marlabs' proprietary tools, makes that systems view tangible. Instead of sending every question to the largest and most expensive language model, it analyzes the request and routes it to an appropriate model while applying security rules. In a published engagement for a Workday consulting firm, Marlabs says the system cut language-model API costs by more than half while improving response quality. The larger lesson is simple: model intelligence without routing, policy, and economics is a science project.
A catalog, not a magic box
AgilityAI includes accelerators for call-center analysis, HR support, enterprise chat, application mapping, and Databricks migration. These are starting points, not off-the-shelf apps. Marlabs adapts them to a client's data, policies, and workflows, then sells the consulting and engineering required to put them into use. The company says reusable components can compress delivery by 40 to 60 percent and reduce proof-of-concept time by half. Those are company claims, but they explain the commercial logic: each successful engagement can leave behind a pattern that makes the next one faster.
Customers can hire Marlabs for an end-to-end transformation, a defined project, extra technical staff, or a managed service. That flexibility matters because an insurer modernizing claims has a different buying problem from a retailer prototyping personalization. The breadth is unusually wide: AI strategy and development, data engineering and governance, application modernization, cloud migration, product design, automation, ERP and CRM work, IoT, digital security, quality engineering, and talent services. The menu is not the differentiator by itself. Accenture, Cognizant, Capgemini, IBM Consulting, Infosys, and Wipro can all present longer ones.
Marlabs' sharper distinction is its place in the market. It is large enough to maintain cross-functional teams and global delivery centers, but smaller than the consultancies whose bureaucracy can become part of the problem. Company materials make the contrast bluntly: fewer handoffs, less red tape, senior people who communicate, and responsibility that continues beyond the roadmap. The promise is not a radical technology. It is a more direct path through a complicated organization.
Enormous benches, broad alliances, and major-program scale - often with more layers.
Deep focus and speed - often narrower in geography, operations, or platform coverage.
Mid-sized global delivery, broad engineering depth, proprietary accelerators, and flexible engagement models.
Powerful platforms that still require data preparation, integration, adoption, and ongoing operation.
The industry is the interface
Technical breadth becomes useful only when it meets industry detail. Marlabs concentrates on financial services, healthcare, life sciences, manufacturing, telecom, and media, with additional work in retail, logistics, energy, and technology. In healthcare, its public examples include medical-image analysis and patient-discharge prediction. In finance, agents can triage risk and compliance cases. In manufacturing, machine vision and predictive maintenance meet the factory floor. In telecom, analytics can anticipate network problems and customer churn. These are not generic chatbots with a new label; they are workflows where bad data or a missing control has a measurable consequence.
The company's partner ecosystem supplies much of the underlying infrastructure. Marlabs is a Salesforce Summit Partner, an official Microsoft Fabric partner, part of the AWS and Google Cloud partner networks, and works with Databricks, Snowflake, Profisee, Infor, and others. A 2025 alliance with ArchLynk connects SAP supply-chain data to Databricks through SAP Business Data Cloud. This is where Marlabs fits: between platforms that provide the building blocks and enterprises that need those blocks assembled around the reality of their operations.
Buying its way into the next chapter
Growth capital from BV Investment Partners arrived in January 2022, with the amount undisclosed. Acquisitions followed. Brazil-based Monitora joined in 2023, bringing nearshore delivery, customer-experience work, infrastructure operations, and data analytics. Indianapolis-based Onebridge arrived in 2024 with two decades of healthcare and life-sciences analytics experience, plus its own assessment frameworks. In 2025 Marlabs bought INDEAVR, a Swiss-Bulgarian engineering firm with more than 200 employees, extending its European footprint and adding data, cloud, and product-engineering capacity.
The sequence looks deliberate. Monitora added geography and engineering. Onebridge added an industry and a discipline. INDEAVR added European delivery and enterprise depth. Together they help Marlabs serve customers who want local conversation and distributed execution without coordinating a parade of vendors. Integration risk is real in any acquisition program, but Marlabs repeatedly cites cultural alignment and long client relationships as selection criteria.
Culture is not entirely corporate prose here. Employees are called “Martians,” a nickname cheerful enough to survive several eras of IT fashion. The company emphasizes curiosity, collaboration, training, inclusion, and teams that work across specialties. In 2026 it was named to U.S. News & World Report's Best Companies to Work For ranking. Analyst recognition has also accumulated: ISG placed Marlabs in two quadrants of its 2026 life-sciences digital-services study, while Everest Group has described it as a Major Contender in application automation.
What a customer can actually do
A customer could begin with an AI-discovery workshop, rank use cases by value and feasibility, and leave with a roadmap. It could bring Marlabs an aging application and have it moved to the cloud, rebuilt around modern data, and augmented with an agent. A bank could automate document review while keeping human approval and audit trails. A manufacturer could connect device data to predictive maintenance. A pharmaceutical company could improve clinical-trial operations without sending sensitive information into an uncontrolled tool. Or a business could simply add experienced engineers to an existing team.
The important question for any buyer is not whether Marlabs can produce a prototype. It is how success will be measured, which assets are reusable, who owns the resulting intellectual property, how the system is monitored, and what happens when models or platform prices change. PromptRouter's vendor-flexible design suggests that Marlabs understands the lock-in problem. Its governance language acknowledges the risk problem. The proof, as always in consulting, resides in delivery and in references that can withstand scrutiny.
Marlabs is not trying to invent the foundation model or become the next cloud. It is competing for the layer where value is either captured or lost: the messy distance between a model and a working business. That layer contains old databases, nervous compliance teams, API limits, procurement rules, skeptical employees, and customers who expect the service to work on Monday morning. Three decades ago, the company called this IT services. In 2026, it calls the package AgilityAI. The work beneath the label remains reassuringly concrete.