Nexus vs TCS: Platform vs IT Outsourcing
TCS has $30B+ revenue and 600K+ employees. Nexus deploys AI agents in weeks with embedded FDEs. Orange deployed in 4 weeks. Lambda chose platform. Full comparison.
Last updated: February 2026
Quick honest summary
TCS (Tata Consultancy Services) is one of the most established IT services companies in the world. Over $30 billion in annual revenue. Roughly 600,000 employees. A global delivery network spanning dozens of countries. They have deep relationships with many of the world's largest enterprises, and their offshore delivery model offers competitive rates for large-scale technology projects. TCS has made significant investments in AI, including $1.8 billion in annualized AI services revenue, 5,500+ AI projects executed, and platforms like TCS AI WisdomNext and TCS MasterCraft with agentic AI capabilities. They are a serious organization with real enterprise credentials.
There is, however, a structural tension at the heart of the IT outsourcing model that is worth naming honestly. TCS generates revenue by billing for time and headcount: day rates, FTEs, project durations. The longer an engagement runs and the more people it requires, the more TCS earns. This is not a criticism of TCS specifically; it is the economics of every IT outsourcing firm. But it creates a misalignment between the client's goal (results, fast) and the provider's incentive (more people, longer).
Nexus is an enterprise AI agent platform paired with white-glove service: Forward Deployed Engineers embedded with your team, change management support, and ongoing optimization. It is not just software you license and figure out on your own. Nexus is built for enterprises that need AI agents completing real business workflows in production, with business teams owning the outcome. Critically, Nexus is incentivized to deliver results quickly, not to extend timelines or inflate team sizes.
The comparison here is not "which company is bigger." TCS is orders of magnitude larger. The question is more specific: when your goal is deploying AI agents that complete business workflows autonomously, do you want a partner whose revenue grows when projects take longer, or one whose model depends on getting you to production fast?
These are genuinely different approaches to the same problem. TCS excels at broad technology services, long-term managed relationships, and large-scale transformation programs. Nexus is purpose-built for one thing: getting AI agents into production fast, with business teams owning the outcome, and with measurable results before you commit to anything long-term.
Side-by-side comparison
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When TCS is the better choice
TCS is a formidable partner, and there are scenarios where their model is the right fit. The key is going in with clear awareness of how the FTE-based billing model shapes incentives, so you can manage the engagement accordingly.
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You need a broad IT services partner, not just AI agents. If your organization needs help across infrastructure, cloud migration, application development, ERP implementation, cybersecurity, and AI as one part of a larger technology transformation, TCS offers that breadth. They can be a single partner across your entire IT landscape. Nexus does one thing: AI agents.
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You are optimizing for cost through offshore delivery at massive scale. TCS's offshore delivery model in India offers competitive rates for large development teams. If you need 50 or 100 engineers working on a multi-year technology program, TCS's global delivery network and blended rate model is hard to match on a pure cost-per-engineer basis. Just be precise about scope and deliverables upfront, because the FTE model can naturally expand to fill whatever boundaries you leave open.
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You already have a deep TCS relationship and want to extend it to AI. Many enterprises have worked with TCS for years or decades. If TCS already understands your systems, your organization, and your processes, extending that relationship to AI projects has real advantages in context and continuity. The risk is that familiarity can also make it easy to approve additional headcount without scrutinizing whether the project actually requires it.
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Your AI needs are part of a larger systems integration effort. If deploying AI agents is deeply intertwined with migrating legacy systems, rebuilding data infrastructure, or re-architecting your application landscape, TCS can staff a team that handles the full scope. These are large, complex programs where having a single systems integrator managing the whole effort reduces coordination risk.
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You need AI capabilities embedded in long-term managed services. TCS's managed services contracts often span 5-10 years. If you want AI capabilities woven into an ongoing operational partnership where TCS runs parts of your technology operations, their model supports that naturally. Be aware that long-term contracts with FTE-based pricing give the provider limited incentive to reduce the team size over time, even as the work becomes more routine.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they have already tried other approaches (including working with large IT services firms), watched timelines stretch and teams grow without corresponding results, and concluded that the outsourcing model's incentives were working against them.
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You need AI agents in production in weeks, not in a year. The typical enterprise AI journey with a large IT services firm follows a familiar pattern: assessment phase (4-8 weeks), strategy and architecture (8-12 weeks), pilot development (12-16 weeks), production deployment (12-24 months including scaling). Each phase generates more billable hours. With Nexus, most agents go live within 2-6 weeks. Orange went from zero to production agents in 4 weeks. Lambda deployed in days. One Nexus client had an outsourcing firm spend an entire year in "project management mode," only finalizing planning for a first knowledge assistant. Nexus came in, scraped the relevant data, built the agent, and pushed to production in 4 weeks. The difference is not incremental; it is a fundamentally different speed, driven by fundamentally different incentives.
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You want your business teams to own the agents, not be dependent on external consultants. With a services model, knowledge lives with the consulting team. This dependency is not accidental; it is the mechanism that sustains ongoing billing. When TCS consultants rotate off or the engagement ends, your organization is left with something it did not build and may not fully understand. With Nexus, business teams build and own the agents with FDE support. When Lambda's Head of Sales Intelligence needed to adjust data sources or account segmentation, he did it himself. No consulting engagement. No change request. No backlog.
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You want to pay for outcomes, not for time and headcount. IT outsourcing pricing is fundamentally FTE-based: you pay per person per month. The incentive to staff heavily and keep projects running is structural. Day rates multiplied by headcount multiplied by duration. The provider earns more when projects require more people for longer periods. Nexus pricing is per-agent, tied to value delivered. The 3-month POC is structured around measurable business outcomes defined upfront. You see results before committing to anything long-term. You never pay for FTEs.
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The problem is deploying AI agents, not broad IT transformation. If what you need is specifically AI agents that complete business workflows (sales operations, customer support, HR, marketing), you do not need a 600,000-person IT services company. You need a platform built for that purpose, with engineers who deploy AI agents every day. Nexus is purpose-built for this. TCS is purpose-built for broad IT services, with AI as one of many offerings. Using TCS for AI agents is like hiring a general contractor to change a lightbulb: they can do it, but the overhead, staffing model, and timeline will not match the simplicity of the task.
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You have already experienced the limitations of custom AI builds through outsourcing firms. Many enterprises that come to Nexus have already tried the custom build route through IT services partners like Infosys or Cognizant. The pattern is consistent: 6-12 month timelines, rigid outputs, ongoing dependency, teams that grow over time, and difficulty iterating when business requirements change. These outcomes are not failures of execution. They are the predictable result of a model where the provider is paid for effort, not outcomes. Nexus exists because that pattern keeps failing for AI agent deployment specifically.
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You need more than software, but less than a full outsourcing engagement. This is the gap that the build vs buy analysis reveals. Nexus embeds Forward Deployed Engineers with your team. They help identify the highest-impact use cases, design agents that fit your specific reality, handle integration complexity, manage organizational change, and optimize continuously. This is more support than buying software, but far more focused and faster than a traditional IT services engagement. There is no incentive to overstaff, because Nexus does not bill by the person. Deploying AI at scale is 10% technology and 90% organizational change. FDEs are built for that reality.
What enterprises experienced
The 1-year vs. 4-week story
This is worth telling because it illustrates the incentive gap concretely. A Nexus client had previously engaged an outsourcing firm to build a knowledge assistant. The outsourcing firm spent a full year in "project management mode": staffing the team, conducting workshops, building architecture documents, holding status meetings. After 12 months, they had finalized planning for a first version. No production deployment. No working agent. Just a plan and a growing invoice.
Nexus came in. Within 4 weeks, the team had scraped the relevant knowledge sources, implemented the assistant, and pushed it to production. The difference was not that Nexus engineers were smarter. It was that Nexus had no incentive to extend the timeline. There were no FTEs to bill. No reason to stretch planning into a year-long exercise. The goal was a working agent in production, and that is what the business model rewards.
Orange: $4M+ yearly impact, 4-week deployment
Orange Group is a multi-billion euro telecom operator with 120,000+ employees across Europe and Africa. They have significant internal engineering resources. They have the budget to hire any IT services firm in the world, including TCS.
Where the typical IT services path involves 12-24 months before enterprise-scale deployment, Orange's business team built customer onboarding agents using the Nexus platform. Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. 100% adoption. 100% compliance. Business teams own the agents. No engineering dependency. No external consulting team maintaining the solution.
The key detail: this was not a year-long custom development project staffed with dozens of FTEs. Business teams, supported by Forward Deployed Engineers, went from concept to production agents handling real customer interactions in 4 weeks. No project management phase. No architecture committee. No headcount ramp.
Lambda: a $4B+ AI company chose platform over custom build
Lambda is a $4B+ AI infrastructure company with $500M+ ARR. They build supercomputers for AI training and inference. Their customers include the world's top AI labs. If any company had the capacity to engage a large IT services firm (or build internally), it was Lambda.
Lambda, a $4B+ AI company, chose a platform approach over building with frameworks or outsourcing. The reasoning was telling: Lambda calculated the opportunity cost of a services engagement. An IT outsourcing firm would need to staff a team, go through discovery, build custom solutions, and maintain them. The timeline would be months. The team would bill continuously. Lambda needed results, not a staffing plan.
Joaquin Paz, Lambda's Head of Sales Intelligence, built an autonomous research agent that monitors 12,000+ enterprise accounts, identifies buying signals, and synthesizes competitive intelligence. Joaquin is not an engineer. He built this in days.
The results:
- $4B+ in cumulative pipeline identified across accounts Lambda was not actively monitoring
- 24,000+ research hours added annually (equivalent to 12 full-time analysts)
- 12,000+ enterprise accounts analyzed with deep intelligence
- Deployed in weeks, not the months a custom build would require
"I'm not an engineer. I built this in days. With the automation tools we looked at before, I would have needed to spec everything out and wait months for development."
Joaquin Paz, Head of Sales Intelligence, Lambda
Lambda has since expanded from a single agent to a fleet across sales and marketing. Anticipated value: more than $7M by 2026. Each new agent deploys in days, not months, because there is no project scoping, no staffing request, no new team to onboard.
Multi-billion euro European telecom operator
A 13,000+ employee European telecom operator built a multi-purpose agent suite: support agents, compliance agents, registration agents, data harmonization, and escalation handling. 40% of support capacity freed. 100% compliance assurance. 12-week deployment for a coordinated multi-agent system handling millions of customer interactions. Under an outsourcing model, a project of this complexity would typically require a large dedicated team billing for 12 to 18 months before reaching production.
Key differences explained
Platform vs. services: fundamentally different models and incentives
This is the core distinction, and it goes deeper than technology.
TCS is an IT services company. Their model is to staff teams of consultants and engineers who build custom solutions for enterprise clients. Revenue is generated by billing for those people's time. This model works well for many technology challenges: infrastructure migrations, ERP implementations, application development, systems integration. It has served enterprises effectively for decades. But the model creates a structural incentive: the longer a project runs and the more people it requires, the more revenue TCS generates. The client pays for effort. The provider profits from effort. Nobody is structurally incentivized to minimize either.
AI agent deployment is a different kind of problem, and the misalignment becomes especially visible here. Agents need to be deployed quickly (weeks, not months) because business requirements change faster than custom builds can keep up. Agents need to be iterable by business teams, because the people closest to the workflow understand it best. Agents need to handle exceptions intelligently, not through custom-coded logic that breaks when reality deviates from the spec. None of these requirements align with a model that generates revenue from extended timelines and large teams.
A platform approach solves this differently than a services approach. The platform handles infrastructure, integrations, security, and compliance. Forward Deployed Engineers handle the complexity of identifying use cases, designing agents, and managing organizational change. Business teams own what they build and iterate without filing change requests. Nexus does not bill FTEs. It is incentivized to deliver results fast, because that is what converts POCs to annual contracts.
TCS has started building AI platforms (TCS AI WisdomNext, TCS MasterCraft with agentic AI), which signals they recognize the shift. But their core delivery model remains services-centric: TCS teams build it, TCS teams maintain it, and the enterprise depends on TCS for changes. The platforms are delivered through the same FTE-based engagement model.
The speed gap compounds over time, and so does the billing
With TCS or any large IT services firm, the typical path to production AI agents follows a structured methodology: discovery and assessment (4-8 weeks), strategy and architecture (8-12 weeks), development and pilot (12-16 weeks), production rollout (12-24 months for enterprise scale). This is a well-established approach for managing risk in large technology programs. It is also, not coincidentally, a structure that maximizes billable hours. Each phase requires staffing. Each transition creates new scoping work. The methodology is real, but it also serves the business model.
Consider the 1-year planning story. An outsourcing firm spent 12 months in project management mode for a single knowledge assistant. That is 12 months of FTE billing for zero production output. Under the outsourcing model, this was not a failure; the firm was being paid the entire time.
With Nexus, most agents are in production within 2-6 weeks. But the real gap is not just the first agent. It is what happens next. Each additional agent with a services model requires another project: scoping, staffing, development, testing, each one a new billing event. Each additional agent with Nexus builds on the foundation already in place and deploys in days. Lambda went from one agent to an expanding fleet, with each new agent deploying rapidly because the infrastructure and integrations were already established.
Over 12 months, a services approach might deliver 2-4 production agents after significant investment. A platform approach can have a fleet of agents across multiple departments. The outsourcing firm has billed for 12 months of FTEs. Nexus has delivered results.
Forward Deployed Engineers: expertise without the FTE billing model
Most enterprise AI platforms sell software and leave you to figure out the rest. Most IT services firms sell people, and the more people they sell for the longer they can, the better their revenue. Nexus sits between these two models, but with a critical difference: FDEs are not billed as FTEs. There is no incentive to overstaff. There is no incentive to extend the engagement. The incentive is to deliver results that convert a POC into an annual contract.
Forward Deployed Engineers (FDEs) are real engineers embedded with your team who:
- Identify the highest-impact use cases first. Not a 12-week assessment phase that generates billable hours. FDEs work with your team to find where agents deliver the most value and start there.
- Design agents that fit your specific reality. Not generic templates, but agents tailored to your workflows, systems, edge cases, and business logic.
- Handle integration complexity. So your team does not need to learn a new platform, and you do not need to hire a consulting firm to connect systems. No separate integration project with its own staffing and timeline.
- Manage organizational change. Because deploying AI at scale is 10% technology and 90% organizational change. FDEs help frame the change, train teams, build confidence through small wins, and address concerns.
- Optimize continuously. Agents improve with use. FDEs help analyze performance, refine logic, and scale to new teams and processes.
This is why Nexus converts 100% of POCs to annual contracts. The engagement is structured to deliver measurable value before you commit. There is no trailing team billing quietly in the background.
Dependency and ownership: who controls the outcome?
With a services model, the consulting firm builds the solution. They understand how it works. When they leave or rotate to another project, the enterprise is left maintaining something it did not build. Changes require going back to the services partner, waiting for availability, scoping the work, and paying for more time. This dependency is not a side effect; it is the engine of recurring revenue. Every change request, every update, every new requirement flows through the outsourcing firm and generates more billing. The enterprise does not truly own the outcome. It rents access to expertise, and the rental never ends.
With Nexus, business teams build and own the agents. FDEs accelerate the process and handle complexity, but the goal is business team ownership from the start. When Lambda's team needed to change data sources, update account segmentation, or adjust priorities, they did it themselves. No tickets. No project scoping. No waiting. No new invoice.
This is not a theoretical distinction. It is the difference between an organization that can iterate on its own AI capabilities weekly and one that submits change requests and waits weeks for a response while the meter runs.
Pricing: the FTE model is the purest form of incentive misalignment
IT outsourcing pricing is the clearest example of structural incentive misalignment in enterprise technology. TCS charges day rates (offshore rates typically $25-80/hour, onshore $100-200+/hour) multiplied by team size and project duration. The FTE-based model means you pay per person per month. A typical enterprise AI project with a 10-person blended team running for 12 months can easily reach $1-3 million or more, before ongoing maintenance costs.
The structural problem is not that these rates are unreasonable in isolation. It is that the model creates incentives that work against the client's interests. The provider earns more when teams are larger. The provider earns more when projects run longer. The provider earns more when problems turn out to require more headcount than initially estimated. There is no structural incentive to be efficient, to automate away work, or to finish early. IT outsourcing firms are skilled at making problems require more people than they actually need, not because they are dishonest, but because the business model rewards that outcome.
Nexus uses per-agent pricing tied to value delivered. You never pay for FTEs. The 3-month POC is structured around specific, measurable outcomes defined upfront. You see results before committing to an annual contract. Orange's 4-week deployment generated $4M+ in yearly impact. Lambda's agent fleet is projecting $7M+ in value by 2026. The pricing conversation starts with "what outcome do you need?" not "how many people do you need?"
Frequently asked questions
We already work with TCS across our IT landscape. Should we use them for AI agents too?
It depends on what you are trying to achieve. If AI agents are one component of a larger systems integration or infrastructure project that TCS is already managing, keeping it within the same engagement can reduce coordination complexity. But if the goal is specifically deploying AI agents that complete business workflows, be aware of what the existing relationship incentivizes. TCS benefits from expanding scope, adding headcount, and extending timelines. That is the nature of the FTE model. The platform + FDE model typically delivers faster, with business team ownership, and without creating another long-term consulting dependency. Some enterprises use both: TCS for broad IT services and Nexus specifically for AI agent deployment, precisely because the incentives are different.
TCS has over 5,500 AI projects and $1.8B in AI revenue. How does Nexus compete with that scale?
TCS's AI practice is large and covers everything from AI strategy consulting to machine learning model development to data analytics to AI-powered legacy modernization. That breadth is valuable for certain needs. But scale in an FTE-based model also means scale in billing. 5,500 projects delivered through time-and-materials engagements is $1.8B in revenue generated from clients paying for effort, not outcomes. Nexus is not trying to compete across all of AI. Nexus does one thing: deploy AI agents that complete business workflows in production. When the specific goal is AI agents in production quickly, with business team ownership, the question is not who has more AI projects. It is who gets agents into production faster with better outcomes, and whose incentives align with yours. Orange deployed in 4 weeks. Lambda deployed in days. Nexus converts 100% of POCs to annual contracts.
TCS offers competitive offshore rates. Is Nexus more expensive?
It depends on how you measure cost, and this is where the FTE model obscures reality. If you compare a TCS offshore developer's hourly rate to Nexus's per-agent pricing, the hourly rate looks lower. But that comparison misses the point. The question is: what is the total cost to get a production AI agent delivering business value? With an FTE model, you are paying for a team of people for 12 to 18 months. The hourly rate is low, but the total spend is high because the model incentivizes longer timelines and larger teams. One Nexus client watched an outsourcing firm bill for an entire year of planning before producing a single working agent. The "low hourly rate" added up to 12 months of FTE costs with zero production output. Nexus delivered the same agent in 4 weeks. Lambda calculated that the opportunity cost of engineering time alone made building through a services partner more expensive. The right comparison is total cost to production outcome, not hourly rate.
Can TCS build exactly the same thing Nexus delivers?
Given enough time, budget, and the right team composition, a services firm can build anything. The question is whether it makes sense, and whose interests the model serves. A custom build for AI agents means 6-18 months of development, ongoing maintenance, dependency on the services partner for changes, and business teams that do not own what was built. Every month of that timeline is revenue for the outsourcing firm. There is no structural incentive to compress it. Nexus delivers production agents in weeks, with business teams owning the outcome, and with 4,000+ native integrations already built. Lambda, a $4B+ AI company, ran exactly this calculation and concluded: the opportunity cost is too high. Build your core product. Use a platform for AI agents.
What does the 3-month POC look like?
Every Nexus engagement starts with a 3-month proof of concept tied to specific, measurable outcomes defined upfront. Most agents are in production within the first 2-6 weeks. A Forward Deployed Engineer is embedded with your team for the entire period. You see the results, measure the impact, and decide whether to continue. You can exit anytime. This is structurally different from a traditional IT services pilot, which typically involves scoping, staffing, development, and a longer path to first results. With an outsourcing firm, a "pilot" often becomes a multi-month engagement that generates significant FTE billing before any production output. The Nexus POC is designed to prove value fast, because that is what converts to an annual contract. The incentives are aligned with yours.
We need AI agents that integrate with our existing enterprise systems (SAP, Salesforce, ServiceNow). Can Nexus handle that?
Nexus connects to 4,000+ enterprise systems natively: CRMs (Salesforce, HubSpot), ERPs (SAP, NetSuite), communication tools (Slack, Teams, Gmail), productivity suites (Google Workspace, Microsoft 365), and custom APIs. Agents deploy across any channel: Slack, Teams, WhatsApp, email, phone, web. Integration complexity is handled by Forward Deployed Engineers, not by your team and not through a months-long custom integration project.
Is this an either/or decision?
Not necessarily. Some enterprises maintain their TCS relationship for broad IT services (infrastructure, application development, managed operations) and use Nexus specifically for deploying AI agents. The two serve different purposes and, crucially, operate under different incentive structures. TCS is a technology services partner paid for time and headcount. Nexus is an AI agent platform with embedded engineering support, paid for outcomes. Using each where their model fits best is a pragmatic approach.
Worth exploring?
If your team has been evaluating whether to engage a large IT services firm for AI agent deployment, or if you have already started down that path and the timeline is stretching longer than expected, it is worth asking a simple question: are you paying for results, or are you paying for people's time?
One Nexus client asked that question after an outsourcing firm spent a year in planning mode for a single knowledge assistant. Twelve months of FTE billing. Zero production output. Nexus delivered the same agent in 4 weeks. The difference was not talent. It was incentives.
Orange, a multi-billion euro telecom operator with 120,000+ employees, deployed customer onboarding agents in 4 weeks. $4M+ yearly impact. 100% adoption. Business teams own the agents. No FTE billing. No trailing consulting team.
Lambda, a $4B+ AI company with world-class engineers, concluded the opportunity cost of custom builds was too high. They deployed in days. Anticipated value: more than $7M by 2026.
Every Nexus engagement starts with a 3-month proof of concept tied to specific outcomes. Forward Deployed Engineers work alongside your team from day one. You see results before committing. You can exit anytime. You never pay for headcount.
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Every engagement starts with a 3-month proof of concept tied to specific, measurable business outcomes. Forward Deployed Engineers embed with your team from day one.