Nexus vs Xebia: Platform vs Full-Stack Digital Consultancy

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Quick honest summary

Xebia is a respected, global digital consultancy founded in the Netherlands in 2001, with 5,500+ professionals across 28 offices worldwide. They bring deep technical talent across AI/ML, cloud, data engineering, software development, and agile transformation. Their client roster includes names like Philips, Ahold Delhaize, Tesco, and ING. As an AI-first consultancy, Xebia has invested heavily in their practice, building capabilities in agentic AI, GenAI platforms, MLOps, and managed AI services. They have earned recognition as a Google Cloud Premier Partner, a Microsoft Solutions Partner, and were named a Disruptor in Avasant's Generative AI Services RadarView. When you engage Xebia, you get experienced engineers building custom solutions tailored to your needs.

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 configure alone. Nexus is built for enterprises that need agents completing business workflows in production, with business teams owning the outcome, not waiting on engineering backlogs or external consultants.

The core question comes down to incentive structure, not competence. Xebia has talented engineers. But like every consultancy, their revenue model is time-based: the longer a project takes, the more they bill. This is not a criticism of intent; it is a structural reality of the consulting business model. Firms that bill by the day or by the sprint are not financially rewarded for finishing faster. Even firms with strong engineering cultures face this dynamic, because the underlying economics pull in the opposite direction from the client's interest in speed.

Nexus is a platform your business teams own, supported by Forward Deployed Engineers who bridge the gap between technology and organizational change. The incentive structure is different: Nexus is paid per agent in production, not per hour of effort. That means Nexus earns nothing during a long discovery phase and everything when agents are live and delivering value. Agents deploy in weeks, not months. Your teams iterate without filing tickets. The platform scales with you.

Both approaches solve real problems. The question is whether your enterprise wants to pay for effort (days, sprints, phases) or for outcomes (agents in production delivering measurable value), and which incentive structure you want your AI partner to operate under.

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Side-by-side comparison

Dimension	Xebia	Nexus
What it is	Global digital consultancy, 5,500+ professionals Services span AI/ML, cloud, data, software engineering Custom solutions built by their engineers Revenue model: time-based (day rates, sprints, phases)	Enterprise AI agent platform + embedded service Includes Forward Deployed Engineers, change management Ongoing optimization included Revenue model: per-agent pricing tied to production outcomes
Who builds and owns it	Xebia's engineers design, build, and deliver Your team inherits the solution post-engagement Changes often require re-engaging Xebia (more billable days) Ongoing dependency creates recurring revenue for the consultancy	Business teams build and deploy agents with FDE support They own the outcome from day one No permanent dependency on external engineers Nexus has no incentive to create ongoing dependency
Engagement model	Consulting engagement: discovery, scoping, staffing, implementation Phased delivery model incentivizes longer timelines Day-rate or project-based pricing; the longer it takes, the more the firm earns Typical investments range from $360K to $2M+	3-month POC tied to measurable business outcomes Per-agent pricing: Nexus earns when agents are in production, not during planning Incentive alignment: speed benefits both parties See results before committing annually
Time to production	Typically 8-16 weeks for AI/GenAI projects Discovery and scoping phases add lead time (and billable hours) At one Nexus client, an outsourcing firm spent 1 year in "project management mode" before finalizing plans for a first knowledge assistant	2-6 weeks for most enterprise agents Nexus came into that same client: 4 weeks to scrape, implement, and push to production FDEs handle configuration, integration, deployment from day one
AI approach	Custom AI solutions for each client GenAI platforms, agentic AI builds MLOps pipelines and data infrastructure Tailored engineering per engagement	Agent-first platform with autonomous agents Agents complete entire workflows end to end Handle exceptions intelligently, escalate with context 4,000+ native integrations
Handles exceptions?	Depends on how thoroughly edge cases were anticipated Custom code handles only what was scoped Unscoped scenarios may cause silent failures	Agents adapt intelligently to unexpected inputs Escalate with full context when needed No silent failures Exception handling is native to the architecture
After go-live	Xebia offers AI Managed Services for ongoing support (additional revenue stream) Core logic changes may require new engagements (more billable days) Or your team takes over maintenance The consulting model structurally benefits from post-launch dependency	Platform-managed infrastructure and updates Agents adapt to system changes without rebuilds Business teams iterate directly, no re-engagement needed FDEs provide ongoing optimization as part of partnership, not as upsell
Scaling to additional use cases	Each new use case requires a new engagement (new revenue for the firm) Scoping, staffing, building each time The model structurally incentivizes treating every use case as a fresh project	Each new agent builds on existing foundation Business teams deploy additional agents in days Compounding value, not compounding cost; Nexus benefits when you scale faster
Security and compliance	Xebia builds compliant solutions per project Compliance posture depends on what was engineered ISO certifications vary by engagement	SOC 2 Type II, ISO 27001, ISO 42001, GDPR certified All certifications from day one Full audit trails and decision traceability Role-based access built into the platform
Knowledge transfer	Xebia provides documentation and training Custom-built solutions require technical expertise Maintaining, modifying, extending needs skilled engineers	Business teams own agents natively No specialized technical knowledge needed to iterate The platform is the knowledge transfer
Best for	Enterprises needing deeply custom AI solutions Complex data infrastructure projects Full-stack digital transformation Problems requiring bespoke engineering where time-based billing is acceptable	Enterprises needing production agents for business workflows Business teams owning and operating agents Organizations that want their AI partner incentivized to deliver fast Engineering-grade support without permanent consulting dependency

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When Xebia is the better choice

Xebia is a strong consultancy with genuine technical depth, and there are scenarios where their model is the right fit. The structural incentive issue (time-based billing rewarding effort over speed) matters less in these cases, because the problems genuinely require sustained, deep engineering work:

• You need full-stack digital transformation, not just AI agents. If the project scope includes cloud migration, data platform modernization, software engineering, and agile transformation alongside AI, Xebia's breadth across disciplines is a real advantage. They can staff a complete program across multiple workstreams in a way that a platform cannot.

• The AI problem is deeply novel and requires custom research. If you need custom ML models trained on proprietary data, specialized data pipelines, or AI capabilities that do not map to established workflow patterns (think: demand forecasting models, fraud detection algorithms, or recommendation engines built from scratch), Xebia's data science and ML engineering teams can build what a platform cannot.

• You already work with Xebia and trust the relationship. If Xebia is already your strategic partner across cloud, software, or data, extending that relationship into AI can be efficient. The context they already have about your systems, your culture, and your constraints has value. Adding another vendor introduces coordination overhead.

• You need nearshore or offshore development capacity. Xebia has 5,500+ professionals across 16 countries, including significant engineering capacity in India, Poland, and Vietnam. If your primary need is development capacity at competitive rates, their global delivery model serves that well.

• The project is truly one-time. If you need a single, well-scoped AI solution built once and maintained by your internal team going forward (and you have the internal engineering capacity to maintain it), a consulting engagement can work. The incentive misalignment matters less for a bounded, one-time project than for an ongoing program. The key test: does your team have the skills and bandwidth to own what gets built?

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When Nexus is the better choice

Enterprises that partner with Nexus tend to share a specific pattern: they evaluated consulting firms, watched timelines stretch as billable hours accumulated, and chose a platform + service approach where the provider is incentivized to deliver fast.

• You need agents in production in weeks, not quarters (or years). Custom AI builds through consultancies typically take 8-16 weeks for initial delivery, with discovery and scoping adding more time on top. But the structural incentive issue means timelines often stretch well beyond estimates. At one Nexus client, an outsourcing firm spent a full year in "project management mode," only finalizing the planning for a first knowledge assistant. Twelve months of billable project management, zero production output. Nexus came in: 4 weeks to scrape, implement, and push to production. That gap is not explained by competence alone; it is explained by incentive structure. A firm billing by the day has no financial reason to move faster. With Nexus, most enterprise agents go live within 2-6 weeks. Orange deployed customer onboarding agents across multiple European markets in 4 weeks. A Forward Deployed Engineer works alongside your team from day one.

• Your business teams need to own the agents, not depend on consultants for every change. With a consulting-built solution, modifying workflows, adding integrations, or adjusting business logic often means re-engaging the consultancy or relying on internal engineers who understand the custom codebase. With Nexus, business teams iterate on agents directly. When Lambda's Head of Sales Intelligence needed to adjust data sources or account segmentation, he did it himself. No consulting engagement. No engineering tickets.

• You do not want to pay for effort when you should be paying for outcomes. Consulting day rates compound, and the structural incentive is clear: the longer the engagement runs, the more the firm earns. A 12-week engagement with a senior team can easily reach $500K or more, and that is for one solution. Each additional use case requires another engagement, another set of billable phases. With Nexus, per-agent pricing ties cost to value delivered. You do not pay for FDEs by the hour. Nexus earns when agents are in production, which means the incentive is to get you there as fast as possible. Additional agents build on the existing foundation, not a new budget line.

• You want enterprise governance built in, not engineered from scratch. When consultants build custom AI solutions, compliance (SOC 2, ISO 27001, GDPR, audit trails, decision traceability) must be engineered into each project. That adds scope, cost, and time. Nexus ships with enterprise governance from day one. At Orange, every agent decision is traceable, every step logged, every escalation visible. Result: 100% adoption, 100% compliance.

• You are planning for a fleet of agents, not a single project. This is the central argument in the build vs buy analysis. The difference between consulting and platform becomes dramatic at scale, and so does the incentive misalignment. Each consulting project starts from scratch: new scope, new team, new timeline, new revenue for the firm. The consulting model structurally benefits from treating every use case as a fresh engagement. With Nexus, each new agent builds on the foundation already in place, and Nexus benefits when you scale faster. Lambda went from one agent to an expanding fleet across sales and marketing, with each new agent deploying in days. Anticipated value: more than $7M by 2026.

• You need more than software delivery. You need organizational change. Deploying AI at scale is 10% technology and 90% organizational change, whether you use workflow automation tools or consulting-built solutions. Consultancies deliver solutions and move to the next billable project. Nexus embeds Forward Deployed Engineers who help identify the highest-impact use cases, design agents that fit your specific reality, handle integration complexity, manage change across teams, and optimize continuously. FDEs are not billed by the hour; they are part of the partnership. Their incentive is your success, not extending the engagement. This is why Nexus converts 100% of POCs to annual contracts.

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What enterprises experienced

Orange: $4M+ yearly revenue from agents 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 and the budget to build anything they want, or to engage any consultancy in the world.

They chose Nexus. While digital consultancies typically scope for weeks before writing a single line of code, Orange went from kickoff to production in 4 weeks. Their business team (not engineering, not an external consultancy) built customer onboarding agents using the Nexus platform. Deployed across multiple European markets in 4 weeks.

The results:

• 50% conversion rate improvement
• $4M+ incremental yearly revenue
• 100% team adoption
• 100% compliance with full audit trails
• Business teams own the agents, no external dependency

Consider the incentive contrast: a consulting firm billing day rates would have spent weeks in discovery and scoping before writing a single line of code, each phase generating revenue for the firm. Orange was in production before that process would have started, because Nexus only earns when agents are live.

Lambda: a $4B+ AI company chose platform over custom build

Lambda is a $4B+ AI infrastructure company with world-class engineers who build supercomputers for a living. If any company had the internal capability to build custom AI solutions (or the budget to hire the best consultancy available), it was Lambda.

Lambda, a $4B+ AI company, concluded that neither in-house engineering nor consulting firms could match the speed they needed. Their Head of Sales Intelligence, Joaquin Paz, who is not an engineer, built an autonomous research agent that monitors 12,000+ enterprise accounts annually.

The results:

• $4B+ in cumulative pipeline identified
• 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 have required

"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

Note what Joaquin described: "spec everything out and wait months for development." That is the consulting model. The speccing, the waiting, the development phases; each stage generates billable work for the firm. Lambda chose a model where the provider is incentivized to get to production, not to extend the timeline.

Lambda has since expanded from one agent to a fleet across sales and marketing. Each new agent deploys in days, building on the existing foundation. Anticipated value: more than $7M by 2026.

European telecom operator: 40% support capacity freed

A multi-billion euro European telecom operator (13,000+ employees) deployed a suite of agents for customer support, compliance, and registration. 40% of support capacity freed. 100% compliance assurance. Full audit trails across millions of customer interactions.

A consulting engagement of this scope, spanning customer support, compliance, and registration, would typically require multiple workstreams, multiple teams, and months of phased delivery. Each phase billable. Each workstream staffed. The consulting model rewards that complexity. With Nexus, the agents were deployed on a platform where speed to production is the shared incentive.

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Key differences explained

Consulting model vs. platform + service: fundamentally different incentives

This is the core distinction, and it matters more than any feature comparison.

Xebia operates on a consulting model. You engage them for a project. They scope the work, staff a team of engineers, and build a custom solution. The work is often technically strong. But the economics follow consulting economics: revenue is generated from billable time (day rates, sprints, phases), knowledge is concentrated in the consulting team, and each new project starts a new engagement cycle. Here is the structural issue: the firm's revenue increases when projects take longer, require more phases, or need ongoing support. This is not about intent or ethics. Xebia has talented, well-meaning engineers. It is about incentive structure. A business model built on billing time will never be naturally aligned with a client's interest in speed.

Nexus operates on a platform + service model. The platform provides the infrastructure, integrations, security, and compliance. Forward Deployed Engineers provide the expertise, change management, and optimization. Business teams own the outcome. The economics are structurally different: per-agent pricing tied to value, compounding returns as you add agents, and no dependency on external engineering for day-to-day iteration. Nexus earns nothing during a long planning phase and everything when agents are in production. The incentives are aligned: Nexus benefits when you go live fast.

The gap widens with scale. Your first consulting engagement might cost $360K-$500K and take 3-4 months. Your second costs roughly the same. Your fifth costs roughly the same. Each one is a new revenue event for the consulting firm. With Nexus, each additional agent builds on what exists. Lambda's expanding fleet deploys new agents in days, not months, at marginal cost.

Forward Deployed Engineers: why Nexus is a solution, not just software

Nexus does not sell software and disappear. And critically, it does not sell engineering hours. You do not pay for FDEs by the day or by the sprint. FDEs represent a different model entirely, one where the provider's incentive is to get agents into production, not to extend the engagement.

Every engagement includes Forward Deployed Engineers (FDEs), real engineers embedded with your team who:

• Identify the highest-impact use cases first. Not generic workshop outputs, but analysis of your specific operations to find where agents deliver the most value.
• Design agents that fit your reality. Not reusable templates from previous clients, but agents tailored to your workflows, systems, edge cases, and business logic.
• Handle integration complexity. So your team does not have to learn a new platform or pull engineers off core work.
• Manage 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 about transparency and control.
• Optimize continuously. Agents improve with use. FDEs help analyze performance, refine escalation logic, and scale to new teams and processes.

The critical difference: when the POC ends, your business team owns the agents. With a consultancy, when the engagement ends, you inherit a codebase, and any changes require re-engaging the firm (generating more billable work). With Nexus, you inherit a capability. The business team iterates independently, and Nexus has no structural incentive to make you dependent.

The ownership question: who maintains this in 12 months?

This is the question that often decides the comparison, and it reveals the incentive structure most clearly.

With a consulting-built solution, someone needs to maintain the custom code, update integrations when APIs change, debug issues, and iterate when business needs evolve. If Xebia's team built it, options are: (1) re-engage Xebia for changes, (2) hire engineers who can understand and modify the custom codebase, or (3) hope the documentation is good enough. Notice that option 1 is the most common outcome, and it is also the most profitable one for the consulting firm. The consulting model structurally benefits from creating solutions that require ongoing expert support. This is not a conspiracy; it is simply what the incentive structure produces.

With Nexus, business teams modify agents directly. The platform handles infrastructure, integrations, security, and updates. When requirements change (a new data source, a different escalation path, an additional channel), the team that owns the workflow makes the change. No engineering tickets. No consulting re-engagement. No dependency. Nexus has no financial incentive to make agents harder to maintain independently.

Lambda proved this model. When they changed data sources, updated account segmentation, and adjusted priorities, the agents adapted. No consulting engagement. No custom development cycle. The business team iterated directly.

Time to value: the compounding advantage (and the compounding misalignment)

The difference in deployment speed is significant for a single agent. It becomes transformative for an agent fleet. And the structural incentive gap compounds at exactly the same rate.

A typical Xebia AI engagement: 2-4 weeks of discovery and scoping, 8-16 weeks of implementation, plus time for testing, deployment, and knowledge transfer. Total: 3-5 months for a single solution, with investments typically ranging from $360K to $2M. Every week of that timeline is revenue for the consulting firm. At one Nexus client, an outsourcing firm demonstrated this dynamic in stark terms: a full year spent in "project management mode," only finalizing the planning for a first knowledge assistant. Nexus delivered in 4 weeks.

A typical Nexus engagement: 2-6 weeks from kickoff to production agent, with a Forward Deployed Engineer alongside your team. 3-month POC tied to measurable outcomes. Nexus earns when agents are live, not during planning.

Now multiply by five agents, ten agents, twenty. The consulting model scales linearly: each project is a new engagement, a new revenue event for the firm. The incentive to start fresh each time is structural. The platform model compounds: each new agent builds on the foundation. Lambda went from one agent to a fleet, with each new agent deploying in days, not starting over. Nexus benefits when you scale faster, not when you re-scope.

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Frequently asked questions

Xebia has 5,500+ engineers. How can a platform replace that kind of capacity?

It does not replace it, and that is the point. Xebia's engineering capacity is real and valuable for problems that require custom engineering: cloud infrastructure, data platforms, bespoke ML models, full-stack application development. Nexus solves a different problem. For business workflow automation (sales operations, customer support, HR, marketing), the question is not whether you can staff enough engineers. It is whether paying for engineering hours is the right model for something business teams should own. More engineers billing more hours does not necessarily mean faster delivery; the incentive structure actually works against speed. Lambda has world-class AI engineers and chose a platform because the opportunity cost of custom building was too high, and because they wanted a provider incentivized to deliver fast.

Can we use Xebia for some things and Nexus for others?

Yes. Some enterprises use consultancies for infrastructure and data platform work while using Nexus for business workflow automation. These are complementary, not competing. Xebia can build your data infrastructure; Nexus agents can operate on top of it. The key is matching the incentive model to the problem: time-based billing is acceptable for deep infrastructure work where sustained engineering effort is genuinely needed. For business workflows that need speed and ownership, you want a partner whose revenue model rewards fast delivery, not extended timelines.

Xebia offers their own agentic AI solutions. How is Nexus different?

Xebia has invested in agentic AI capabilities, including their GenAI Platform and Agentic OS. The difference is the delivery model and the incentive structure behind it. Xebia's agentic solutions are custom-built by their engineers for each client engagement. The quality depends on the team staffed, the scope defined, and the implementation timeline, and every phase of that process generates billable revenue for the firm. Nexus is a production-grade platform with 4,000+ native integrations, enterprise governance built in, and Forward Deployed Engineers embedded with your team. The question: do you want agentic AI built for you by consultants billing by the day, or a platform your business teams own and operate, delivered by a partner that earns when agents are in production?

Xebia claims outcome-based pricing. How does that compare to Nexus?

Xebia does offer outcome-based pricing alongside traditional project-based models. But even outcome-based consulting pricing requires scoping, staffing, and delivery phases that follow the same time-based pattern. The underlying incentive structure remains: more phases, more complexity, more staffing all increase the firm's revenue. Nexus uses per-agent pricing, where cost is directly tied to agents in production. You do not pay for FDEs by the hour. Nexus earns when agents are live and delivering value, not during planning or implementation phases. Every engagement starts with a 3-month POC with measurable outcomes defined upfront. You see results before committing to an annual contract. This is why Nexus has a 100% POC-to-contract conversion rate.

What if we have already started a project with Xebia?

The investment is not wasted, especially for data infrastructure, cloud work, or specialized ML capabilities. For business workflow automation specifically, though, it is worth asking some honest questions about incentive alignment: will your team maintain what was built, or will every change require another billable engagement? Will each new use case require re-scoping and re-staffing? Is the timeline tracking to what was promised, or has it expanded (as the consulting incentive structure tends to produce)? If the answer creates tension with speed or budget, Nexus can handle the business workflow layer while Xebia (or another partner) handles what requires custom engineering.

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 the POC-to-contract conversion rate is 100%: Nexus does not move forward unless the value is clear.

We are concerned about vendor lock-in with a platform.

Reasonable concern. Nexus agents operate within your existing enterprise systems (Slack, Teams, CRM, ERP). The platform connects to 4,000+ tools natively. Your data stays in your systems. Your agents are yours. With a consulting-built solution, the lock-in risk is different but arguably more insidious: custom code that only the original team (or someone with their expertise) can modify, creating a structural dependency that generates ongoing revenue for the consulting firm. The consulting model is financially rewarded for building solutions that are hard to maintain independently. Neither model is lock-in free. The question is which form of dependency is easier for your team to manage, and which provider is incentivized to minimize it.

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Worth exploring?

If your team has been evaluating consulting firms for AI initiatives and noticing the pattern (timelines stretching, phases multiplying, budgets growing, while production deployment stays perpetually "a few weeks away"), it might be worth asking whether the incentive structure of your current approach is working for you or against you.

Orange is a multi-billion euro telecom operator that deployed agents in 4 weeks. Lambda is a $4B+ AI company whose engineers build supercomputers for a living, and they chose a platform over custom builds. At another client, an outsourcing firm spent a year in project management before Nexus delivered in 4 weeks. The common thread: these enterprises chose a partner whose revenue model rewards delivery speed, not effort duration.

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 do not pay for FDEs by the hour. You see results before committing. You can exit anytime.

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