Nexus vs Infosys: Platform vs IT Outsourcing
Infosys brings $19B+ in IT services scale and 335,000+ consultants. Nexus deploys agents in weeks with FDEs, not months. Orange generated $4M+. Full comparison.
Last updated: February 2026
Quick honest summary
Infosys is one of the world's largest IT services and consulting companies, with $19B+ in annual revenue, 335,000+ employees, and decades of experience delivering large-scale technology transformation for Global 2000 enterprises. Their Topaz AI platform, launched in 2023 and expanded significantly in 2025 with Topaz Fabric and the Agentic Foundry, represents a serious push into enterprise AI. It includes 200+ pre-built AI agents, 12,000+ AI assets, 150+ pre-trained models, and partnerships with Anthropic, Google Cloud, and other leading AI providers. Infosys has deep domain expertise across industries, competitive offshore delivery rates, and the ability to staff hundreds of consultants on a single engagement. For large-scale IT transformation programs that span multiple years and require massive delivery capacity, Infosys is a proven choice.
There is something structural worth naming, though. Infosys, like all large IT outsourcing firms, generates revenue primarily through billable hours and FTEs. The longer an engagement runs and the more people assigned, the higher the revenue. This is not a criticism of intent; it is how the business model works. The firm is structurally incentivized to scope large, staff heavily, and extend timelines. When you pay per person per month, there is no inherent pressure to reduce headcount or accelerate delivery.
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 and not just services. Nexus is built for enterprises that need autonomous agents completing real business workflows in production, with business teams owning the outcome, deployed in weeks rather than months. Critically, you do not pay for FTEs. Nexus is incentivized to deliver results quickly, because the pricing model is tied to outcomes, not effort.
The core question is about model, not capability. Infosys approaches enterprise AI primarily through services: consultants who scope, design, build, and deliver custom solutions on your behalf, increasingly supported by Topaz tooling. The business model profits when projects require more people for longer periods; the client pays for effort, not outcomes. Nexus approaches enterprise AI through a platform paired with embedded engineering: your teams deploy and own agents, supported by Forward Deployed Engineers who handle complexity and drive adoption. The incentive is to get you to production fast, because that is how Nexus demonstrates value.
Both can deliver enterprise AI agents. The question is: do you want a services firm whose revenue grows with project duration and team size, or a platform that puts production agents in your teams' hands in weeks, with embedded engineers bridging the gap between technology and organizational change?
Side-by-side comparison
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When Infosys is the better choice
Infosys is a formidable enterprise partner, and there are scenarios where their model is the right fit. The structural incentive dynamics described above do not disappear in these cases, but they matter less when the engagement genuinely requires scale and duration:
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You need large-scale IT transformation beyond just AI agents. If the initiative spans infrastructure modernization, cloud migration, ERP implementation, application development, and AI, all coordinated under a single program, Infosys has the breadth and depth to deliver. They are not just an AI company. They are a full-service IT partner with decades of experience managing complex, multi-workstream programs.
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Your organization operates at a scale that requires hundreds of consultants. Some transformation programs genuinely require massive delivery capacity across multiple geographies, time zones, and workstreams. Infosys can staff these programs in ways that smaller, specialized firms cannot. If you need 200 consultants across 5 countries for 3 years, Infosys has the bench.
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Cost-per-hour matters more than speed-to-value. Infosys's blended offshore/onshore model delivers competitive day rates. For organizations where the primary constraint is budget (not time), and where a 6-12 month timeline is acceptable, the Infosys model can be cost-effective on a per-hour basis. Just be aware that per-hour cost-effectiveness and total project cost are different things. A low hourly rate spread across a large team over many months can still result in a high total cost, and the FTE model provides no structural incentive to minimize either variable.
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You are already in the Infosys ecosystem. If Infosys already manages your IT infrastructure, application portfolio, or other technology services, adding AI capabilities through Topaz is a natural extension. Your teams already know how to work with Infosys. Procurement is streamlined. Governance frameworks are in place. The incremental cost of adding AI services to an existing master services agreement is often lower than engaging a new vendor.
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Your AI needs are tightly coupled with broader IT services. If the AI agent deployment requires deep integration with legacy systems that Infosys already manages, or if the AI initiative is part of a larger application modernization program, having one partner handle both layers can reduce coordination overhead.
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You prefer a services relationship over a platform relationship. Some enterprises prefer having an external team handle the build entirely. They want to define requirements, review deliverables, and accept the output, without their business teams needing to learn a new platform or take ownership of agent configuration. Infosys is built for this model. The trade-off is that this preference creates exactly the ongoing dependency that the FTE model is designed to sustain.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they have tried (or evaluated) the services approach, realized the timeline, dependency model, and ownership structure did not match what they needed for AI agent deployment, and chose a platform + embedded engineering approach instead. In many cases, the structural incentive misalignment of the outsourcing model is what pushed them to look for alternatives.
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You need production agents in weeks, not quarters. With a services engagement, the typical path is: scoping (2-4 weeks), requirements gathering (2-4 weeks), design (2-4 weeks), development (4-12 weeks), testing (2-4 weeks), deployment (2-4 weeks). That is 3-6 months for a single agent at best, often longer with change requests and competing priorities. Each of those phases is billable, so the delivery model has no structural pressure to compress them. A concrete example: at one Nexus enterprise client, the previous outsourcing firm spent a full year in "project management mode," only finalizing planning for a first knowledge assistant. Twelve months of billable effort before a single user touched a working product. Nexus came in, scraped the relevant knowledge bases, implemented the agent, and pushed to production in 4 weeks. Same goal; a fraction of the time and cost. With Nexus, most agents go live within 2-6 weeks. A Forward Deployed Engineer works alongside your team from day one. Orange deployed customer onboarding agents in 4 weeks. Lambda deployed sales intelligence agents in days.
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You want your business teams to own the agents, not file change requests with an external team. With a services model, every modification requires going back to the delivery team: updated logic, new integrations, changed business rules. That means change requests, approvals, scheduling, and additional billable hours. With Nexus, the business teams who understand the workflows own and iterate on the agents directly. When Lambda's Head of Sales Intelligence needed to adjust data sources, he did it himself. No tickets. No waiting.
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You have tried AI initiatives that took too long and delivered rigid outputs. This is the pattern Nexus sees most often. An enterprise engaged a services firm (Infosys, Accenture, Wipro, TCS, or a smaller agency) to build a custom AI solution. The project took 6-12 months. The deliverable worked for the original requirements. But requirements changed. The business evolved. And now the solution is rigid, hard to modify, and the services team has moved on to other clients. Any changes require re-engaging, waiting for availability, and paying for more billable hours. That rigidity is not accidental; it is downstream of the billing model. A solution that requires ongoing paid modification is more profitable than one the client can iterate on independently. Nexus agents adapt. When systems change, the agent adjusts. When business logic evolves, the business team updates it directly.
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You do not want to create a permanent consulting dependency for AI. The FTE-based outsourcing model is the purest form of incentive misalignment: you literally pay per person per month. The structural incentive to staff projects heavily and keep them running indefinitely is not incidental; it is how the business model generates revenue. The longer your AI initiative takes, the more the firm earns. The more people assigned, the higher the invoice. Firms are skilled at making problems require more headcount than they actually need, because that is what the model rewards. Nexus is structured around outcomes. The 3-month POC is tied to specific, measurable results. Forward Deployed Engineers are there to make your team self-sufficient, not to create a dependency. You do not pay for FTEs. You pay for agents in production. This is why Nexus converts 100% of POCs to annual contracts: the value is clear before you commit.
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Your AI initiative is focused on business workflows, not a broader IT overhaul. If the goal is autonomous agents for sales operations, customer support, HR, or marketing (not a multi-year IT transformation), a services engagement is often too heavy. You also do not need to build with developer frameworks for well-understood workflow patterns. You do not need 20 consultants for 9 months to deploy a customer onboarding agent, but an outsourcing firm has every structural reason to scope it that way. You need a platform that handles the infrastructure and an embedded engineer who understands your business.
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You need more than software delivery. You need organizational change. Most services firms deliver technology. Nexus delivers adoption. Deploying AI at scale is 10% technology and 90% organizational change. Forward Deployed Engineers do not just configure agents. They help identify the highest-impact use cases, design agents that fit your specific reality, frame the change for your teams, build confidence through small wins, and scale from there. This is why Orange achieved 100% adoption, not just 100% delivery.
What enterprises experienced
Orange: 4 weeks to production, $4M+ yearly impact
Orange Group is a multi-billion euro telecom operator with 120,000+ employees across Europe and Africa. They have significant internal engineering resources and the budget to build anything they want. They also have relationships with every major IT services provider, Infosys included.
They chose Nexus. Where IT services engagements typically start with weeks of scoping alone, Orange went from kickoff to live agents in 4 weeks. Their business team, not an external consulting team, built customer onboarding agents using the Nexus platform. Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. 100% adoption. 100% compliance.
The deployment timeline tells the story. Four weeks from start to production. Not 4 weeks of scoping followed by months of development. Four weeks to agents handling real customer interactions across multiple European markets. With a services engagement of comparable scope, the scoping alone would likely take that long, and every week of scoping is billable.
Before Nexus, this same client had engaged an outsourcing firm for a knowledge assistant initiative. That firm spent a full year in "project management mode": assembling teams, running workshops, producing documentation, refining requirements. Twelve months of billable FTEs, and they had only finalized the planning phase. No working product. No users. Nexus came in, scraped the relevant knowledge bases, built the agent, and pushed to production in 4 weeks. The contrast is not about competence; it is about incentive structure. The outsourcing firm was paid by the month, per person. There was no structural reason to move faster.
Lambda: a $4B+ AI company chose platform over services
Lambda is a $4B+ AI infrastructure company with world-class engineers. If any company could build custom AI agents internally or manage a services engagement effectively, it was Lambda. They could also afford to engage any IT outsourcing firm in the world. Lambda, a $4B+ AI company, chose a platform approach over building in-house or outsourcing, because they wanted results on a timeline that the FTE-billing model is not structured to deliver.
Lambda's Head of Sales Intelligence, Joaquin Paz, built an autonomous research agent that monitors 12,000+ enterprise accounts annually, identifies buying signals, and synthesizes competitive intelligence. The critical detail: Joaquin is not an engineer. He built this in days on the Nexus platform. No outsourcing team billing months of requirements gathering and development cycles.
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 services 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.
Multi-billion euro European telecom operator
A 13,000+ employee telecom operator deployed a multi-purpose agent suite for support, compliance, registration, data harmonization, and escalation handling. 40% of support capacity freed. 100% compliance assurance. 12-week deployment across multiple agent types. The business teams own the agents and iterate without external dependency. No ongoing FTE billing for maintenance. No change requests to modify business logic. The team that understands the workflows controls the agents directly.
Key differences explained
Platform + service vs. pure services: fundamentally different models and incentives
This is the core distinction, and it matters more for AI agent deployment than for traditional IT services.
Infosys is a services company. Their model is proven for large-scale IT delivery: assemble a team, scope the work, execute against requirements, deliver the output. Topaz and the Agentic Foundry add AI-specific tooling and pre-built components that accelerate parts of the engagement. But the fundamental model remains services-led: your enterprise defines what it needs, Infosys builds it, you receive the output. And the fundamental economics remain FTE-driven: the firm earns more when projects require more people for longer periods. This is not unique to Infosys; it is how the entire IT outsourcing industry works. But it creates a structural misalignment between what the client wants (fast results, minimal dependency) and what the business model rewards (large teams, extended timelines).
Nexus is a platform + embedded engineering solution. Your business teams deploy agents on the platform. Forward Deployed Engineers work alongside your team to handle complexity, integration, and organizational change. The business team owns and iterates on the agents directly. Nexus does not bill FTEs. The incentive is to prove value quickly so you convert from POC to annual contract.
Why this distinction matters for AI agents specifically: AI agents are not static deliverables. They improve with use. They need to be refined as business logic evolves. They require iteration based on real-world performance. In a services model, every iteration is a change request, and every change request is billable. The outsourcing firm has no structural incentive to build something the client can iterate on independently. In a platform model, the team that understands the business makes the change directly.
The timeline gap: weeks vs. months compounds quickly (and profitably)
A typical Infosys AI engagement follows the services delivery lifecycle: discovery and scoping (2-6 weeks), solution design (2-4 weeks), development and integration (6-16 weeks), testing and UAT (2-4 weeks), deployment and stabilization (2-4 weeks). For a well-run engagement with clear requirements and available stakeholders, that is 3-6 months for a single agent or agent cluster. In practice, with enterprise stakeholder alignment, procurement cycles, and requirement changes, it is often longer. Every one of those phases is billable. The longer each phase runs, the more the firm earns.
Consider the real-world example: an outsourcing firm at a Nexus client spent 12 months in project management mode for a knowledge assistant. Twelve months of staffed FTEs producing planning documentation, running alignment workshops, and refining requirements. Nexus delivered the same scope in 4 weeks. The outsourcing firm was not incompetent; they were responding to the incentives of their business model. When revenue comes from billing time, there is no structural pressure to compress timelines.
With Nexus, most enterprise agents go live within 2-6 weeks, including integration with existing systems. A Forward Deployed Engineer works alongside your team from the start. The incentive is to ship, because that is what converts POCs to contracts.
The gap compounds with each additional agent. In a services model, each new agent is a new workstream, or at minimum a new phase, each requiring its own scoping, staffing, and billing cycle. With Nexus, each new agent builds on the foundation already in place. Lambda went from one agent to an expanding fleet, with each new agent deploying in days.
Day rates and FTE billing vs. per-agent pricing: the core incentive misalignment
The Infosys pricing model is built around time and materials or fixed-price engagements, with FTE-based billing as the foundation. Offshore rates in the $25-50/hour range, onshore rates in the $75-150/hour range, with blended models depending on engagement structure. This is competitive on a per-hour basis compared to Western consulting firms. But per-hour cost is not the metric that matters. What matters is total cost to achieve the outcome, and the FTE model provides no structural incentive to minimize that number.
This is the purest form of incentive misalignment in enterprise services. You literally pay per person per month. The firm profits when projects require more people for longer periods. A 6-month engagement with a 10-person team at blended rates of $75/hour is approximately $900,000. If the same outcome can be achieved in 4 weeks with a platform and an embedded engineer, the economics shift fundamentally. But the outsourcing firm has no reason to tell you that. Their revenue depends on the engagement being large and long. They are skilled at making problems require more headcount than they actually need, because that is what the business model rewards.
Nexus uses per-agent pricing tied to value delivered. No FTE billing. No day rates. No incentive to extend timelines or inflate team sizes. Every engagement starts with a 3-month proof of concept with specific, measurable outcomes defined upfront. You see the results, measure the impact, and decide whether to continue. This is why Nexus converts 100% of POCs to annual contracts: the engagement is structured to deliver value before you commit, not to generate billable hours.
Forward Deployed Engineers vs. consulting teams: different relationships, different incentives
Infosys assigns consulting teams to your project: project managers, solution architects, developers, testers, change management consultants. Each role is a billable FTE. They work on your behalf, delivering against agreed requirements. Your team reviews and approves. The relationship is client-vendor, and the vendor's revenue is directly proportional to team size and engagement duration. There is no structural incentive for the consulting team to make your team self-sufficient; self-sufficiency means fewer billable hours.
Nexus embeds Forward Deployed Engineers with your team. FDEs are not building for you. They are building with you. They help identify the highest-impact use cases, design agents that fit your specific reality, handle integration complexity, manage organizational change, and optimize continuously. The goal is to make your team capable, not dependent. This is the opposite of the outsourcing incentive structure: Nexus succeeds when your team can operate independently, because that is what drives contract renewals.
This is particularly important for AI agent deployment, where adoption determines success. Orange achieved 100% adoption not because the technology was delivered to spec, but because the deployment was designed around how people actually work. FDEs ensure agents are integrated into existing tools (Slack, Teams, WhatsApp, email), framed as enhancements rather than replacements, and refined based on real-world feedback from the teams using them.
Ownership and iteration speed: who controls the agent?
In a services model, the delivery team controls the agent during development. After handover, your team receives the output. But who maintains it? Who iterates when business needs change? Typically, you re-engage the services team, or you build internal capability to maintain what was delivered. Both paths have costs, but only one of them generates revenue for the outsourcing firm. The services model is structurally incentivized toward the first path: ongoing dependency that produces ongoing billable hours. A solution your team can maintain independently is, from the firm's perspective, a solution that stops generating revenue.
With Nexus, business teams own the agents from day one. They understand what the agent does because they helped build it. They can modify workflows, add integrations, and iterate without filing tickets with an external team. When Lambda's team needed to change data sources or adjust account segmentation, they did it themselves. No change requests. No additional billing.
This ownership model is what drives long-term value. Agents are not projects that end. They are capabilities that evolve. The team that understands the business should be the team that controls the agent, not an external team whose revenue depends on remaining involved.
Frequently asked questions
Infosys has 335,000+ employees and decades of experience. Why would we choose a smaller company?
Scale matters for large IT transformation programs. For AI agent deployment, what matters is speed to production, business team ownership, and adoption, not how many consultants can be assigned to an engagement. In fact, the ability to staff 335,000+ people is part of the structural challenge: the business model is built around deploying large numbers of billable FTEs. Orange, a multi-billion euro telecom with access to every major IT services provider, chose Nexus because the speed, ownership model, and adoption approach were what they needed. Lambda, a $4B+ AI company, made the same choice. The question is not company size. It is whether the delivery model is incentivized to get agents into production quickly, or to generate billable hours over extended timelines.
Can we use Infosys for broader IT services and Nexus for AI agents?
Yes. Many enterprises work with large IT services firms for infrastructure, application management, and broad transformation programs while using Nexus specifically for AI agent deployment. The two solve different problems with different models. Infosys is strong for managed services, large-scale development, and multi-year IT programs. Nexus is built for getting autonomous agents into production quickly, with business teams owning the outcome.
Infosys has Topaz and 200+ pre-built agents. How is that different from Nexus?
Topaz is a strong platform, and the Agentic Foundry adds valuable pre-built components. The difference is the delivery model and the incentive structure behind it. Topaz is primarily delivered through Infosys consulting engagements. The pre-built agents accelerate parts of the engagement, but the overall timeline still follows the services delivery lifecycle: scoping, requirements, design, development, testing, deployment. Each phase is billable. The pre-built components reduce development time, but they do not change the fundamental incentive to scope large and staff heavily. Nexus is a platform your business teams use directly, supported by Forward Deployed Engineers. The pre-built components are part of the platform itself, not part of a consulting engagement. The result is a fundamentally different timeline, ownership structure, and incentive alignment.
Our procurement team prefers working with large, established vendors. How do we make the case for Nexus?
Nexus is Y Combinator F25 batch, backed by General Catalyst and Y Combinator ($4M seed), with $1M+ ARR from enterprise customers including Orange Group (multi-billion euro telecom) and Lambda ($4B+ valuation). SOC 2 Type II, ISO 27001, ISO 42001, GDPR certified. Offices in Brussels and San Francisco. The 3-month POC structure means your procurement team can evaluate results before committing to an annual contract. 100% of POCs convert because the value is demonstrated before the commitment.
Is Infosys more cost-effective than Nexus?
On a per-hour basis, Infosys offshore rates are competitive. On a total-cost-of-ownership basis, the comparison shifts dramatically. A 6-month services engagement with a blended team at $75/hour average costs approximately $900,000 for a 10-person team. That does not include ongoing maintenance, change requests, or the opportunity cost of a 6-month timeline. And the FTE-based model creates no incentive to reduce team size or shorten timelines; both actions would reduce the firm's revenue. The real-world example is instructive: an outsourcing firm billed 12 months of FTEs for planning alone before Nexus delivered the working product in 4 weeks. Nexus delivers production agents in 2-6 weeks at per-agent pricing tied to measurable outcomes. The right comparison is not hourly rate vs. hourly rate. It is total cost to achieve the outcome, including time, and whether the vendor is structurally incentivized to minimize or maximize that total.
What about Infosys's recent partnership with Anthropic?
Infosys announced a collaboration with Anthropic in February 2026 to integrate Claude models into Topaz for enterprise workflows across telecommunications, financial services, manufacturing, and software development. This is a meaningful development. It signals that Infosys is investing in the AI agent space. The partnership strengthens their AI capabilities. But better AI models delivered through an FTE-billing model does not change the structural incentive dynamics. The underlying delivery model remains services-led: you still pay per person per month, and the firm still profits when projects require more people for longer. Nexus is model-agnostic (you choose any AI model) and delivers through a platform + embedded engineering model. The question is not which AI model powers the agent. It is how the agent gets into production, who owns it, and whether the vendor is incentivized to deliver quickly or bill indefinitely.
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 why our POC-to-contract conversion rate is 100%: we do not move forward unless the value is clear.
We already have an Infosys managed services agreement. Can Nexus work alongside it?
Absolutely. Nexus agents integrate with your existing tech stack through 4,000+ native integrations. If Infosys manages your CRM, ERP, or support systems, Nexus agents connect to those systems without requiring changes to your Infosys engagement. The two operate in parallel: Infosys manages your IT infrastructure, Nexus puts autonomous agents into your business workflows. This approach also lets you compare delivery models side by side. You will see what the FTE-billing model delivers over months versus what a platform approach delivers in weeks, and draw your own conclusions about incentive alignment.
Worth exploring?
If your team has been evaluating whether to engage an IT outsourcing firm for AI agent deployment, or if you have already started a services engagement and the timeline is not matching expectations, it might be worth examining whether the delivery model's incentive structure is part of the problem. When you pay per person per month, there is no structural pressure to deliver faster or with fewer people.
One Nexus client experienced this firsthand: their outsourcing partner spent 12 months in project management mode before delivering a working product. Nexus delivered the same scope in 4 weeks. Orange, a multi-billion euro telecom, deployed customer onboarding agents in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. 100% adoption. Lambda, a $4B+ AI company, chose to buy instead of build or outsource. $4B+ pipeline identified. Anticipated value: more than $7M by 2026.
Every Nexus engagement starts with a 3-month proof of concept tied to specific outcomes. No FTE billing. No day rates. Forward Deployed Engineers work alongside your team from day one. You see results before committing. You can exit anytime.
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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.