Nexus vs Cognizant: Platform vs IT Services & AI Consulting

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Quick honest summary

Cognizant is one of the world's largest IT services and outsourcing companies. $21.1B in annual revenue (FY 2025). Over 350,000 employees globally, with the majority operating through offshore delivery centers in India. They are a recognized leader in AI and generative AI services (named a Leader and Star Performer by Everest Group in 2025). Their Neuro AI platform includes a Multi-Agent Accelerator and Agent Foundry for building and orchestrating enterprise AI agents. They have deep, industry-specific expertise in healthcare (embedded in 350 major healthcare systems), banking and financial services, and retail. They partner with NVIDIA, Microsoft, Google Cloud, and Salesforce on AI initiatives. When an enterprise needs a large-scale IT partner that combines industry domain knowledge with cost-competitive offshore delivery across a broad digital transformation program, Cognizant is a strong choice. That said, their revenue model is built on billing days, hours, and FTEs. The longer an engagement runs and the more people staffed on it, the more Cognizant earns. This is not a critique of their intent; it is the structural reality of how IT outsourcing firms operate and generate margin.

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 buy and figure out on your own. Nexus is built for enterprises that need agents completing business workflows in production, with business teams owning the outcome, not waiting months for an IT services engagement to deliver. Nexus is incentivized to deliver results quickly; you pay for agents that produce outcomes, not for people who bill hours.

This comparison is not about whether Cognizant is capable. They clearly are, at enormous scale. It is about a structural incentive question. IT outsourcing firms bill by the day, the hour, or the FTE. The more people assigned and the longer the project runs, the higher the revenue. Cognizant has strong industry expertise, but the business model creates a misalignment: the firm profits when projects require more people for longer periods, while the client pays for effort, not outcomes. Nexus operates on the opposite model: per-agent pricing tied to results, where speed to production is a feature, not a cost center.

The question worth asking: for deploying AI agents on specific business workflows, do you want a billing model where the provider earns more the longer the project takes? Or do you want a platform that goes live in weeks, with Forward Deployed Engineers alongside your team, where the provider only succeeds when you see measurable results?

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Side-by-side comparison

Dimension	Cognizant AI	Nexus
What you get	Custom AI solutions by Cognizant consulting and delivery teams Neuro AI platform (Multi-Agent Accelerator, Agent Foundry) Industry-specific solutions, system integration, managed services	Enterprise AI agent platform + embedded FDEs Business teams build and own agents Ongoing optimization included
Who builds it	Cognizant teams design, build, and implement Blended model: onshore architects, offshore delivery Your teams participate, but Cognizant leads delivery More people staffed = more revenue for Cognizant	Business teams build and deploy agents with FDE support Your people own the outcome from day one No permanent external dependency
Timeline to production	Typically 3-12 months depending on scope Discovery, design, build, testing, deployment phases Neuro AI aims to accelerate, but still follows services cycle Longer timelines generate more billable days; the incentive to compress is limited	2-6 weeks for most enterprise agents FDEs work alongside your team from day one POC in production before services engagement finishes discovery Nexus earns when agents deliver results, not when timelines extend
Pricing model	Blended day rates (onshore $150-300/hr, offshore lower) FTE-based billing: per person, per month; the purest form of incentive misalignment Costs scale with team size and engagement duration; provider earns more when projects grow	Per-agent pricing tied to value delivered You do not pay for FTEs; Nexus is incentivized to deliver results quickly 3-month POC with measurable outcomes before commitment Costs do not scale linearly as you add agents
What you own after	Cognizant delivers the solution Maintenance requires managed services contract or internal capacity (additional revenue for Cognizant) Knowledge often resides with the delivery team, creating ongoing dependency	Your teams own agents, workflows, and logic No dependency on external teams for modifications Teams iterate directly on what they built
Ongoing support	Managed services contracts (additional cost) Typically delivered through offshore teams Or your team maintains it (requires understanding custom build)	Continuous optimization included FDEs analyze performance and refine escalation logic Scale agents to new teams and processes
Governance and compliance	Governance built into custom implementations Strong in regulated industries (healthcare, banking) Requires custom design per engagement	SOC 2 Type II, ISO 27001, ISO 42001, GDPR from day one Full audit trails, decision traceability, role-based access Enterprise governance built into platform, not per project
Scalability	New use cases mean new project phases, teams, timelines (and new billing cycles) Neuro AI templates can accelerate somewhat Each new implementation still follows a services model; scaling means scaling the team	Each new agent builds on existing foundation Business teams deploy additional agents in days 4,000+ native integrations
Industry depth	Deep expertise in healthcare, banking, retail, manufacturing Decades of vertical knowledge in delivery teams 350 healthcare integrations, dedicated financial services practice	Proven in telecom, SaaS, professional services, automotive, fintech Agent-first architecture is industry-agnostic FDEs tailor solutions to your specific vertical and workflows
AI platform	Neuro AI Multi-Agent Accelerator (no-code agent design) Agent Foundry (build and orchestrate agents at scale) Neuro SAN (open-source multi-agent framework) Partnerships with NVIDIA, Microsoft, Google Cloud	Agent-first architecture with visual workflow builder Knowledge integration (real-time and stored RAG) 4,000+ integrations One-click deployment: Slack, Teams, WhatsApp, email, phone, web

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When Cognizant is the better choice

Cognizant is a strong partner for certain enterprise needs, and there are situations where their scale, industry depth, and delivery model are the right fit. In these cases, the structural incentive dynamics of the outsourcing model (billing by the day, the FTE, or the engagement phase) are an acceptable trade-off for the value delivered:

• You need deep, industry-specific AI in healthcare, banking, or retail. Cognizant manages 4.4 billion transactions between payers and providers annually in healthcare alone. Their banking practice implements AI assistants for financial advisors, payment delinquency agents, and sales coaching tools. Their retail AI work has delivered results like 52% reduction in case cycle times for clients. If your AI initiative requires deep vertical domain knowledge that comes from decades of operating in a specific regulated industry, Cognizant's industry practices bring that expertise.

• You need a broad IT outsourcing partner, not just AI agents. If the initiative extends beyond deploying AI agents to include cloud migration, application modernization, legacy system integration, or managed IT services, Cognizant's full service portfolio covers all of this. Their recent acquisition of 3Cloud strengthens their Microsoft Azure capabilities. If you need one partner for broad digital transformation, Cognizant can serve that role.

• Cost-competitive offshore delivery matters for your budget. Cognizant's model blends onshore consultants with large offshore delivery centers. For enterprises where the total cost of a blended team over 12+ months is still more budget-friendly than other consulting firms charging premium onshore rates, Cognizant's pricing model works. They operate at scale that enables competitive pricing, particularly for ongoing managed services. Just be clear-eyed about the incentive structure: the model is still FTE-based, meaning the provider earns more when more people are staffed for longer periods. Cheaper per-hour does not necessarily mean cheaper overall.

• You are already in the Cognizant ecosystem. If Cognizant already manages parts of your technology landscape, if your teams are familiar with their delivery model, and if you have established governance and vendor management for Cognizant engagements, extending the relationship to include AI agent deployment reduces procurement and onboarding friction. Be aware that this convenience also deepens dependency; an incumbent outsourcing partner has structural reasons to expand scope rather than deliver quickly and step back.

• You need a partner with massive scale for global rollouts. With 350,000+ employees across geographies, Cognizant can staff large programs across multiple regions simultaneously. If the initiative requires deploying solutions across dozens of countries with local language, compliance, and infrastructure requirements, Cognizant's global delivery network is built for that.

• Regulatory credibility of a publicly listed IT services firm matters. In certain contexts, particularly in healthcare and financial services where Cognizant has deep incumbency, working with a $21B publicly traded firm carries board-level credibility that matters for regulatory submissions and stakeholder reporting.

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When Nexus is the better choice

Enterprises that partner with Nexus tend to share a specific pattern: they know which workflows they want to automate with AI agents, they have tried other approaches (including evaluating or hiring IT services firms), and they concluded that the cost, timeline, or dependency model did not make sense for getting agents into production on business workflows. Often, the breaking point is recognizing that the outsourcing firm was structurally incentivized to extend, not to finish.

• You need AI agents in production in weeks, not months. A typical Cognizant implementation follows a services lifecycle: discovery, design, build, testing, deployment. Even with their Neuro AI platform accelerating parts of the process, the services model involves onshore/offshore team coordination, project governance, and phased delivery that typically takes 3-12 months. There is no structural incentive to compress this timeline; longer projects mean more billable days. Nexus agents go live in 2-6 weeks. At one Nexus client, an IT outsourcing firm spent a full year in "project management mode," only finalizing planning for a first knowledge assistant. Nexus came in: 4 weeks to scrape, implement, and push to production. That is not an outlier. Orange deployed customer onboarding agents in 4 weeks. Lambda went from zero to production in days.

• You want your business teams to own the agents, not depend on an external delivery team. When an IT services firm builds your solution, the implementation knowledge lives with their team. Changes require engaging the services partner, going through change request processes, and paying for additional delivery hours. This dependency is not accidental; it is the revenue model. The outsourcing firm profits from every change request, every additional sprint, every new team member staffed. With Nexus, business teams own what they built. When Lambda's Head of Sales Intelligence needed to adjust data sources or account segmentation, he did it himself. No change requests. No waiting for offshore teams to schedule the work.

• You do not want to manage a blended onshore/offshore delivery engagement. Unlike TCS or other large IT outsourcers, Nexus does not require a parallel internal team to manage. IT services engagements come with project management overhead: sprint ceremonies, status calls, onshore-offshore coordination, resource rotation, knowledge transfer when team members change. This management burden is real and often underestimated. Nexus provides a dedicated Forward Deployed Engineer embedded with your team. One relationship, one point of accountability, focused on getting your agents into production.

• The math on FTE-based billing does not work for deploying agents. FTE-based billing is the purest form of incentive misalignment: you pay per person per month, and the provider's incentive to staff heavily and extend timelines is structural. A 6-month Cognizant engagement with a blended team of architects, developers, and QA can cost $500K-2M+ before production, depending on scope and team size. And that is for one implementation. Scaling to additional agent use cases means additional project phases with similar overhead, each generating new billing. Nexus per-agent pricing does not scale linearly. You do not pay for FTEs. The second, third, and fourth agents build on the foundation already in place.

• You want embedded expertise that makes your team self-sufficient. Forward Deployed Engineers provide the kind of hands-on partnership you would expect from a dedicated services team, but with a fundamentally different incentive. FDEs work to make your team capable of building, iterating, and scaling agents independently. They identify the highest-impact use cases, design agents for your specific workflows, handle integration complexity, and manage organizational change. The structural difference: FDEs succeed when your team no longer needs them. Outsourcing firms succeed when the engagement extends, because that is how they generate revenue.

• You want enterprise governance out of the box, not designed per project. When an IT services firm implements governance, it is scoped, designed, and built as part of each engagement. That adds time, cost, and variability. Nexus ships SOC 2 Type II, ISO 27001, ISO 42001, and GDPR compliance from day one. Every agent decision is traceable, every action logged, every escalation visible. At Orange, this meant 100% compliance with zero custom governance development.

• You tried an IT services engagement and ended up with something rigid. This is the build vs buy dynamic at work. An enterprise hired an IT services firm to build a custom AI solution. It took months. It worked for the original scope. But when the business evolved, the solution could not adapt without another project phase, which of course meant another billing cycle. Rigidity is a structural feature of the model, not a flaw; the provider benefits from every iteration requiring paid engagement. Nexus agents adapt to changing requirements. Business teams iterate directly, without external dependency and without waiting for a change request to be prioritized.

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What enterprises experienced

Orange Group: 120,000+ employees, business team deployed in 4 weeks

Orange Group is a multi-billion euro telecom operator with 120,000+ employees across Europe and Africa. They have significant internal engineering resources, established vendor relationships with major IT services firms, and the budget to engage any delivery partner in the world.

Where IT services firms like Cognizant or Infosys typically spend months in phased delivery, Orange went from kickoff to production in 4 weeks. Their business team (not engineering, not an external services firm) built customer onboarding agents using the Nexus platform. Deployed across multiple European markets. The agents collect customer information, validate data, check system compatibility, and route complex cases with full context.

Results:

• 50% conversion improvement
• $4M+ incremental yearly revenue
• 100% adoption by sales teams
• 100% compliance with full audit trails
• Business teams own the agents and iterate independently

A comparable IT services engagement would have involved months of discovery, design, and phased delivery, with the services team owning the implementation knowledge and billing for every phase. At another client, an outsourcing firm spent a full year in "project management mode" before anything reached production; that is the structural incentive at work. Orange's business team owns everything. They modify agents when requirements change. No change requests. No additional project phases. No one profits from extending the timeline.

Lambda: $4B+ AI company, CTO chose to buy not build

Lambda is a $4B+ AI infrastructure company with world-class engineers who build supercomputers for a living. If any company could build AI agents internally (or justify a premium services engagement to have someone else build them), it was Lambda.

Their CTO evaluated the options and concluded: the opportunity cost was too high, whether building with developer frameworks or managing an external services engagement where the provider is incentivized to extend rather than finish. Every hour was an hour not spent on their core product. Joaquin Paz, Head of Sales Intelligence and not an engineer, built the agent himself in days.

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 it would have taken through internal build or external delivery

Lambda has since expanded from a single agent to a fleet across sales and marketing, building what they call an "agentic layer" across their go-to-market organization. Anticipated value: more than $7M by 2026.

"We looked at open-ended AI agents; they were smart but inconsistent. We looked at traditional automation; it was reliable but felt heavy, lots of hard coding. With Nexus, we got both: intelligent and consistent."

Joaquin Paz, Head of Sales Intelligence, Lambda

Multi-billion euro European telecom operator: 40% support capacity freed

A multi-billion euro European telecom operator with 13,000+ employees deployed a multi-agent suite for customer support, compliance, and registration. Agents handle millions of customer interactions with full regulatory compliance and audit trails.

Results:

• 40% of support work automated
• 12-week deployment to production
• 100% audit trail, zero compliance gaps
• Support team freed for complex issues that require human judgment

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Key differences explained

IT services model vs. platform + service: fundamentally different incentives

This is the core distinction, and it applies to Cognizant specifically because of how their delivery model and revenue model operate.

Cognizant's model is services-led. They bring deep industry knowledge, technology partnerships, and a large delivery workforce. They design custom solutions, build them using a blend of onshore architects and offshore developers, test them, and deploy them. Their Neuro AI platform (Multi-Agent Accelerator, Agent Foundry) provides accelerators and templates, but the delivery model is still services: Cognizant teams do the work, following a project lifecycle with phases, milestones, and deliverables. Critically, Cognizant's revenue is tied to the size of the team and the duration of the engagement. The longer the project runs and the more people assigned, the more Cognizant earns. This is the structural reality of all IT outsourcing, not unique to Cognizant.

Nexus is a platform + service. Business teams (sales operations, customer support, marketing, HR) build and deploy agents that complete their workflows. The platform handles infrastructure, integrations, security, and compliance. Forward Deployed Engineers work alongside your team to identify use cases, design agents, handle complexity, and optimize over time. The business team focuses on outcomes, not managing a delivery engagement. Nexus earns when agents deliver measurable results; the incentive is to get you to production as quickly as possible.

These are fundamentally different incentive structures. Cognizant profits from effort. Nexus profits from outcomes. Cognizant assumes their teams will build and deliver the solution. Nexus assumes deploying AI agents at scale requires both a platform and embedded expertise, and that business teams should own what they build.

The blended delivery model: what it means in practice

Cognizant's strength is their ability to blend onshore consulting with massive offshore delivery capacity. For large, long-running IT programs, this model offers cost efficiency at scale. India accounts for the majority of their 350,000+ employees, and the margin between onshore billing and offshore operating costs is where the economics work. It is also where the incentive misalignment lives: more people staffed (especially offshore, where margins are higher) means more profit for Cognizant.

For deploying AI agents on specific business workflows, this model introduces complexity that may not serve you well. Onshore architects design the solution. Offshore teams build it. Knowledge transfers happen across time zones. Team members rotate as projects phase. When you need a change, it goes through a change request process, gets prioritized against other work, and gets scheduled for the next sprint. Every one of these steps generates billable hours.

With Nexus, a Forward Deployed Engineer sits with your team. They understand your workflows because they work in them. When something needs to change, your business team changes it. The feedback loop is hours, not weeks. No one bills for the change request.

Forward Deployed Engineers vs. IT services delivery teams

Cognizant's delivery model puts their people in charge of building the solution. They bring deep expertise, but the expertise walks out the door when the engagement ends (or transitions to a managed services contract with different team members, generating a new revenue stream). The incentive structure rewards dependency: the more your organization needs Cognizant's team to maintain, modify, and extend the solution, the more Cognizant earns.

Nexus Forward Deployed Engineers operate under the opposite incentive:

• They make your team capable, not dependent. FDEs work to ensure your business teams can build, iterate, and scale agents independently. The goal is self-sufficiency. An outsourcing firm has no structural reason to make you self-sufficient; Nexus does.
• They are embedded, not external. FDEs work alongside your team, not from an offshore delivery center coordinating through project managers.
• They focus on outcomes, not deliverables. FDEs are measured by whether your agents deliver measurable business results. Not by whether deliverables were completed on time and on budget. Not by how many hours were billed.
• They handle organizational change. 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. This is not a separate change management workstream billed as an add-on.

This is why Nexus converts 100% of POCs to annual contracts. The engagement is structured to deliver measurable value before you commit.

Cognizant's Neuro AI platform vs. Nexus platform

Cognizant has invested significantly in their own AI platform capabilities. The Neuro AI Multi-Agent Accelerator is a no-code framework for designing and deploying agent networks, with pre-built templates for domains like loan origination, customer service, retail optimization, and intranet automation. Agent Foundry (launched July 2025) helps enterprises design, deploy, and orchestrate autonomous AI agents at scale. They also open-sourced Neuro SAN (initially under an Academic Source License, now Apache 2.0 since November 2025).

These are real capabilities. The distinction is how they are delivered and who profits from the delivery. Cognizant's platforms are accelerators within their services model. You still engage Cognizant to implement, configure, and deploy. The platform makes the services engagement faster in theory, but does not change the fundamental incentive: Cognizant still earns from the hours and FTEs required to implement. A tool that could cut a project from 12 months to 2 months would cut Cognizant's revenue by 80%. The structural incentive to fully leverage that acceleration is limited.

Nexus is the platform. Business teams build directly on it, with FDE support. There is no separate services layer between you and the technology. This means faster iteration, direct ownership, and no handoff between "the people who built it" and "the people who use it." And no one billing you for the hours in between.

Timeline and cost: the compound effect of misaligned incentives

A single Cognizant AI implementation might take 3-12 months and cost $500K-2M+ depending on team size and scope. That is competitive within the IT services world, especially with Cognizant's offshore delivery pricing. But ask yourself: who benefits from the 12-month end of that range? Every additional month is additional revenue for the outsourcing firm.

The compound effect makes the incentive misalignment worse over time. When you want a second agent, you need another project phase. A third agent, another phase. Each one involves scoping, staffing, delivery, and testing. Each one generates a new billing cycle. The timeline is additive, and so is the cost. This is not a coincidence; it is the economic logic of FTE-based billing.

With Nexus, each additional agent builds on existing integrations, existing knowledge, and the team's existing familiarity with the platform. Lambda expanded from a single agent to a fleet across sales and marketing. Each new agent deployed in days, not months, because the foundation was already in place. No new staffing. No new project phase. No one profiting from the expansion taking longer.

Over 12 months, this compounds dramatically. An enterprise might deploy 1-2 agents through a services model (generating 1-2 billing cycles for the outsourcing firm), or 5-10+ through a platform model, with business teams iterating and improving continuously.

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Frequently asked questions

Cognizant has deep healthcare and banking expertise. Can Nexus match that?

Cognizant's vertical depth in healthcare (350 healthcare system integrations, 4.4 billion annual transactions) and banking is genuine and hard to replicate. For AI initiatives that require deep, industry-specific regulatory knowledge built over decades, that domain expertise matters. Nexus has deployed agents across telecom, SaaS, professional services, automotive, and financial technology. The platform is industry-agnostic; FDEs tailor the implementation to your specific workflows and compliance requirements. If your primary need is industry-specialized consulting across a broad transformation program, Cognizant's vertical practices are a genuine advantage. If your primary need is getting AI agents into production on specific business workflows, Nexus's speed, ownership model, and embedded support are the relevant differentiators.

We already work with Cognizant on other IT services. Should we use them for AI agents too?

Having an existing Cognizant relationship simplifies vendor management, and there is value in that. The question is whether a partner structurally incentivized to bill more hours and staff more people is the right approach for deploying AI agents, where speed to production and business team ownership are what matter. IT outsourcing and system integration can work well through Cognizant's model when the scope justifies a large team over an extended period. AI agent deployment, where the goal is production in weeks and self-sufficiency for your teams, may be better served by a partner whose revenue model rewards exactly that. Some enterprises use Cognizant for broad IT services and Nexus for AI agent deployment. The two serve different purposes, with different incentive structures.

Cognizant's rates are competitive compared to other consulting firms. How does Nexus pricing compare?

Cognizant's blended rates are generally more competitive than firms like Accenture or Deloitte, thanks to their offshore delivery model. But competitive hourly rates can be misleading when the incentive structure rewards maximizing hours. The pricing comparison is not about the rate per hour; it is about total cost to get agents into production and the ongoing cost of ownership. A 6-month Cognizant engagement, even at competitive blended rates, includes project management overhead, onshore/offshore coordination, testing phases, and change management. Every one of those elements generates billable time. Nexus per-agent pricing is tied to value delivered, with a 3-month POC that proves results before annual commitment. You do not pay for FTEs. The total cost depends on what you are deploying and at what scale, but the incentive is fundamentally different: Nexus earns when agents deliver outcomes, not when more people bill more hours.

Cognizant launched the Neuro AI Multi-Agent Accelerator and Agent Foundry. Does that close the gap?

These are meaningful capabilities. The Multi-Agent Accelerator provides pre-built templates and a no-code design interface. Agent Foundry helps with agent orchestration at scale. But these tools accelerate Cognizant's services delivery model; they do not replace it. You still engage Cognizant teams to implement, configure, integrate, and deploy. The platform is a tool used within a services engagement, not a platform your business teams build on directly. And the underlying incentive has not changed: Cognizant still earns from the FTEs and hours required to deliver. A platform that truly eliminated the need for long services engagements would undermine the outsourcing revenue model. For enterprises that want their own teams to build, own, and iterate on agents with embedded engineering support, the gap remains.

How do Cognizant's AI partnerships (NVIDIA, Microsoft, Google Cloud) compare?

Cognizant's partnerships with NVIDIA, Microsoft, and Google Cloud are genuine and give them access to the latest AI infrastructure and models. Their March 2025 NVIDIA collaboration covers enterprise AI agents, industry-specific LLMs, digital twins, and AI platform capabilities. Their Microsoft partnership focuses on co-building industry AI solutions. Nexus is model-agnostic; you choose any AI model. Nexus's 4,000+ integrations connect to enterprise systems (CRMs, ERPs, communication tools) where agents need to operate. The difference is not about access to AI models. It is about how agents get deployed and who owns them in production.

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.

Can Nexus handle the same scale as Cognizant's global delivery?

Different kind of scale, and different incentive behind the scaling. Cognizant can staff hundreds of people on a single engagement across multiple geographies. That scale matters for massive, multi-year transformation programs. It is also the core of their revenue model: more people, more geographies, more months, more billing. Nexus scales differently: each agent deployment builds on existing infrastructure, integrations, and team knowledge. Lambda scaled from one agent to an entire fleet across sales and marketing, with each new agent deploying in days. Orange deployed across multiple European markets in 4 weeks. The platform scales; you do not need to scale a delivery team. And no one profits from the scaling taking longer than it should.

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Worth exploring?

If your team has been evaluating IT services firms for AI agent deployment, and wrestling with the timeline, cost, and ownership trade-offs (how long until production, what happens when the engagement ends, who owns the solution, what does scaling look like), it is worth asking one structural question first: is your provider incentivized to deliver results quickly, or to bill more hours? FTE-based billing means the provider earns more when projects take longer and require more people. That is not a critique of any firm's talent or intent; it is the economic reality of the outsourcing model.

Orange Group, with 120,000+ employees and the resources to engage any IT services firm in the world, chose a platform approach. Their business team deployed customer onboarding agents in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. 100% adoption.

Lambda, a $4B+ AI company with world-class engineers, concluded the opportunity cost of building (or managing an external engagement) was too high. They deployed in days. $4B+ pipeline visibility. 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.

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