Outsourcing and AI Consulting: 14 Firms Compared

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Why enterprises consider outsourcing AI agent development

The logic is straightforward: deploying AI agents for enterprise workflows requires expertise most organizations do not have in-house. You need people who understand large language models, agent architectures, integration with enterprise systems, data security, compliance, and organizational change management. Consulting and outsourcing firms have that expertise. They have delivered thousands of AI projects. Their track records are real. Many of the people working at these firms are genuinely talented.

The question is not whether these firms are capable. They are. The question is whether the business model they operate under is structurally aligned with your goal of getting AI agents into production quickly.

Here is the core issue: consulting and outsourcing firms bill by the day, by the hour, or by the team. The longer a project takes, the more they earn. The more consultants staffed, the higher the revenue. The more phases in the roadmap, the bigger the contract. This is not about bad people or bad intentions. It is a structural incentive problem. The firm profits when projects take longer, require more consultants, and involve more "phases." The client pays for effort, not outcomes.

Most consulting and outsourcing engagements follow a familiar pattern. A scoping phase (2-6 weeks). Requirements gathering and design (2-8 weeks). Custom development and integration (8-24 weeks). Testing, UAT, and deployment (4-8 weeks). Change management and handoff. Total timeline: 6-18 months for a single set of use cases. Each of these phases is billable. Each transition between phases creates natural opportunities to expand scope. The cost scales linearly with team size, seniority, and duration. And when the business changes (it always does), modifications require re-engaging the same firm, or finding someone else who can maintain what was built.

A real example of what this looks like in practice. An outsourcing firm was engaged at one of Nexus's current clients. They were in "project management mode," running governance meetings, consolidating requirements, building planning documentation. After a full year, they had only finalized the planning of a first knowledge assistant and had only begun to consolidate the knowledge base. No production deployment. When Nexus came in, within 4 weeks the team had scraped the data, implemented the agent, and pushed it to production. This is an extreme case, but it illustrates the structural dynamic clearly: when a firm is paid for time spent, there is no structural urgency to deliver.

This delivery model works well for certain types of enterprise technology: ERP implementations, cloud migrations, multi-year digital transformation programs where complexity genuinely warrants extended timelines. But AI agents are not static deliverables. They are living systems that improve with use, adapt when business logic changes, and need to scale from one use case to many across departments. In a consulting model, every iteration is a change request. Every new use case is a new project with new timelines and budgets. Knowledge concentrates in the consulting team and leaves when they move to the next client. And the incentive to keep that knowledge concentrated, rather than transfer it, is baked into the business model.

A platform approach is fundamentally different, starting with how incentives are structured. With Nexus, you do not pay for the service. Forward Deployed Engineers are included. Nexus earns when agents deliver value, not when projects drag on. This means Nexus is structurally incentivized to get you to production as fast as possible, because that is when the platform starts delivering measurable results. The platform handles infrastructure, integrations, security, and compliance. FDEs provide the expertise that consulting firms sell, but embedded with your team to transfer ownership rather than create dependency. Business teams build and own agents directly. The first agent goes live in weeks. The fifth goes live in days. Costs do not scale linearly because each new agent builds on the foundation already in place. And when the business changes, your team iterates directly, without waiting for consultant availability or approving additional hours.

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How outsourcing and consulting firms compare to Nexus

Big 4 and Global Management Consulting

These firms bring board-level credibility, cross-industry expertise, and the ability to run multi-year, cross-functional transformation programs. Their AI practices are growing rapidly, backed by partnerships with every major technology vendor. When you need AI strategy, regulatory credibility, or a single vendor for transformation at scale, these are proven choices. The trade-off to understand clearly: their revenue model is built on day rates and team size. A faster delivery is, structurally, a smaller contract. This does not mean they intentionally slow down, but there is no structural incentive to accelerate.

For strategy firms specifically (McKinsey, BCG, and the strategy practices within Deloitte and PwC), there is a second structural problem beyond incentive misalignment: these are advisory organizations, not builder organizations. The partners who control engagements are advisors. The culture rewards synthesizing insights, presenting recommendations, and managing executive relationships. It does not reward shipping production software. When these firms claim they "implemented" AI, what typically happened is that consultants project-managed developers: they ran standups, wrote status decks, and escalated blockers, but they did not write code, challenge architectural decisions, or debug production issues. The people in control of the engagement cannot evaluate whether the technical team is making the right calls, because they have never built anything themselves. This is not a criticism of individual talent. It is a structural reality of how these firms are organized. Strategy firms have tried for decades to build genuine technology capabilities through acquisitions and dedicated units: QuantumBlack at McKinsey, BCG X (formerly Gamma) at BCG, Monitor Deloitte's analytics practice, Strategy&'s digital team at PwC. These efforts brought in real technical talent, but the advisory culture always reasserts control. The builders remain subordinate to the advisors, because advisory partners own the client relationships and control how work is staffed and sold. The result: strategy firms can advise brilliantly on what AI to build, but they struggle to build it at the speed and quality that production demands. Note: this advisory mindset problem applies most acutely to strategy and management consulting firms. IT outsourcing firms like Infosys, TCS, or Cognizant have different limitations (scale over speed, headcount over outcomes), but their teams are at least composed of engineers who build for a living.

Dimension	Accenture	Deloitte	BCG X	McKinsey / QuantumBlack	PwC	Capgemini	Nexus
Company size	$69.7B revenue, 779,000 employees	$70.5B revenue, 470,000+ employees	Part of BCG ($13.5B), 3,000+ technologists	Part of McKinsey, ~1,700 in QuantumBlack	$56.9B revenue, 364,000 employees	EUR 22.5B revenue, 355,000+ employees	Startup. Brussels HQ, San Francisco office. Y Combinator F25, backed by General Catalyst
Typical day rate	$300-500/hour. Revenue scales with hours billed, not outcomes delivered	$250-500+/hour. Same structural model: longer projects mean more revenue	$400-600/hour. Premium rates amplify the incentive misalignment	$400-700+/hour. Highest rates in the industry; every additional week is significant revenue	$250-500+/hour. Time-based billing across all engagement types	$200-400/hour (blended onshore/offshore). Lower rates, but revenue still tied to duration	Per-agent pricing tied to value delivered. No day rates. Nexus earns when agents deliver results, not when projects extend
Engagement length	3-18 months. Each phase is billable; scope expansion is structurally rewarded	3-18 months. Phases, governance layers, and discovery extend timelines	6-18 months. Strategy-led approach means months before any deployment	6-18 months. Strategy-first model means production is always "later"	3-18 months. Governance frameworks add phases that extend the engagement	6-18 months. Managed services model incentivizes long-term dependency	3-month POC tied to measurable outcomes. Most agents live in 2-6 weeks. Nexus is incentivized to prove value fast
What you own after	Custom solution. Maintenance requires retaining the firm or building internal capability. Knowledge stays with the consultants	Custom solution. IP often tied to Deloitte frameworks. Dependency is the default outcome	Custom solution and strategy docs. Modifications require re-engagement, generating new billable scope	Strategy and custom analytics. QuantumBlack retains platform IP. Ongoing dependency is structural	Governance frameworks and custom builds. Ongoing dependency common; the frameworks themselves create recurring advisory needs	Custom solution. Maintenance via managed services contracts, which is recurring revenue for the firm	Your agents, your workflows, your logic. Business teams own and iterate directly. No vendor lock-in. FDEs transfer knowledge; they do not hoard it
AI specialization depth	Broad. AI Refinery platform, 77,000 AI professionals. Partners with NVIDIA, OpenAI, Google, Anthropic	Deep in regulated industries. Deloitte AI Institute. Strong in governance and audit-adjacent AI	Strategy-led. 10-20-70 framework. 200+ PhDs. AI Science Institute	Deepest strategy. QuantumBlack Labs with 20+ AI products, 140+ accelerators. ~40% of McKinsey revenue	Strongest in AI governance, responsible AI, regulatory compliance	Broad delivery. Strong European presence. 10%+ of bookings from GenAI/agentic AI in Q4 2025	Purpose-built for enterprise AI agents. 4,000+ integrations. SOC 2 Type II, ISO 27001, ISO 42001, GDPR
Best for	Multi-year transformation, systems integration, massive scale	Regulated industries, audit-adjacent credibility, board trust	Board-level AI strategy, transformation roadmaps, R&D-heavy AI	AI strategy, executive alignment, analytics at scale	AI governance, regulatory frameworks, responsible AI	Large-scale delivery, European enterprises, cost-competitive blended models	Business teams that need production agents in weeks, with ownership and no consulting dependency

European AI Boutiques

Smaller, more specialized firms with strong technical talent and focused expertise. They bring hands-on engineering, local presence, and deep knowledge in specific technology ecosystems. For custom ML model development, specialized data science challenges, or bespoke technical builds, these firms deliver genuine quality. The same structural incentive issue applies at a smaller scale: these firms bill by the day, and their revenue depends on engagement duration. Talented engineers, same misaligned model.

Dimension	ML6	Artefact	Xebia	Thoughtworks	Nexus
Company size	120+ AI experts. Ghent, Amsterdam, Berlin, Munich, Eindhoven	1,700+ employees, 31 offices in 25 countries	5,500+ professionals, 28 offices worldwide	10,500+ consultants, 48 offices in 19 countries	Startup. Brussels HQ, San Francisco office
Typical day rate	Senior AI engineer consultancy rates (European market). Revenue tied to days billed	$1,000-2,500/day per consultant. Each additional day of "optimization" is more revenue	European consultancy rates for AI/cloud engineers. Time-based billing across all engagements	Premium rates reflecting engineering culture. High day rates make scope expansion very profitable	Per-agent pricing. No day rates. Nexus revenue is tied to agent value, not time spent
Engagement length	3-12 months for custom builds. No structural incentive to compress the timeline	3-12 months for custom AI solutions, retainers for ongoing work. Retainers are recurring revenue by design	Project-based, typically 3-12 months. Each project scoped and billed independently	Months to multi-year for large programs. Longer programs mean more revenue	3-month POC tied to measurable outcomes. Most agents live in 2-6 weeks. Speed is the incentive
What you own after	Custom solution. Maintenance is your team's burden (or re-engage ML6, generating new billable work)	Custom models and pipelines. Ongoing optimization requires re-engagement; the firm benefits from your continued need	Custom solution. Handoff to your team at project end, but modifications often require bringing Xebia back	Well-architected custom build. Your team inherits maintenance, but deep knowledge stays with the Thoughtworks team	Your agents, your workflows. Business teams iterate directly without external dependency. FDEs build with you so knowledge transfers naturally
AI specialization depth	Deep. Google Cloud partner, 450+ AI use cases delivered. Building Unum platform	Data and AI focused. Google Cloud EMEA AI Partner of the Year. Strong in marketing analytics	Broad digital consultancy with strong AI/ML practice. Google and Microsoft partner	Engineering excellence. Technology Radar. AI/works platform for legacy modernization	Purpose-built for enterprise AI agents. Platform + FDEs. 4,000+ integrations
Best for	Custom ML models, Google Cloud-native architectures, Benelux enterprises wanting a local partner	Data strategy, custom analytics, marketing attribution, organizations early in data maturity	Full-stack digital transformation with AI, enterprises wanting breadth across cloud and data	Engineering-led transformation, legacy modernization, organizations that value disciplined methodology	Business teams that need production agents fast, with ownership and embedded engineering support

Global IT Outsourcing

Massive delivery capacity at competitive rates. These firms manage technology operations for the world's largest enterprises. When you need hundreds of consultants across geographies for a multi-year IT program, or when cost-per-hour is the primary constraint, their scale and delivery models are hard to match. The structural incentive dynamic here is worth noting: lower hourly rates can actually amplify the misalignment, because revenue growth depends on staffing more people for longer periods. The business model rewards headcount and duration, not speed of delivery.

Dimension	Infosys	TCS	Cognizant	Endava	Nexus
Company size	$19B+ revenue, 320,000+ employees	$30B+ revenue, ~600,000 employees	$21.1B revenue, 350,000+ employees	~$970M revenue, 11,400 employees	Startup. Brussels HQ, San Francisco office
Typical day rate	$25-50/hour offshore, $75-150/hour onshore. Low rates, but revenue depends on volume of hours and headcount	Competitive offshore rates, blended models. Revenue scales with team size and contract duration	Blended onshore/offshore models. Same incentive structure: more people, more hours, more revenue	Nearshore (Eastern Europe) at competitive European rates. Time-based billing throughout	Per-agent pricing. No day rates. Nexus earns when agents work, not when consultants bill hours
Engagement length	3-6 months initial, 6-18 months full-scale. Multi-year managed services is the goal, structurally	Multi-month to multi-year. TCS is optimized for long-term managed relationships, not fast delivery	3-6 months initial, longer for transformation. Expansion into managed services is the revenue model	Months to multi-year for custom development. Longer engagements mean more billable engineering hours	3-month POC tied to measurable outcomes. Most agents live in 2-6 weeks. Nexus is incentivized to deliver fast
What you own after	Varies by contract. Shared IP clauses common. Topaz components remain Infosys IP. Dependency is often contractual	Custom deliverable. Ongoing maintenance via managed services, which is recurring revenue for TCS	Custom solution. Neuro AI platform components remain Cognizant IP. Switching costs are structural	Custom software. Your team inherits codebase and maintenance, but deep knowledge leaves with the Endava team	Your agents, your workflows. Zero vendor lock-in. Platform handles infrastructure. Your team owns everything because they built it alongside FDEs
AI specialization depth	Growing. Topaz AI with 200+ pre-built agents, 12,000+ AI assets. Partnership with Anthropic	Strong. $1.8B AI services revenue. TCS AI WisdomNext. 5,500+ AI projects	Growing. Neuro AI platform with Multi-Agent Accelerator. Leader in Everest Group AI rankings	Emerging. Dava.Flow methodology. Partnership with Cognition for agentic coding	Purpose-built for enterprise AI agents. Platform + FDEs. 4,000+ integrations
Best for	Large-scale IT transformation, enterprises already in the Infosys ecosystem, budget-sensitive programs	Massive delivery capacity, multi-year managed relationships, enterprises needing scale above all	Healthcare, banking, retail specialization. Cost-competitive offshore delivery with growing AI capability	Bespoke software engineering, European enterprises wanting nearshore delivery, custom application development	Business teams that need production agents in weeks, without creating an IT services dependency

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The Nexus alternative: platform + Forward Deployed Engineers

The pattern enterprises encounter with outsourcing is consistent across all three sub-segments. A firm scopes the work. A team builds a custom solution. The solution is handed off. Your team inherits something they did not build and may not fully understand. When business needs change, you re-engage the firm or struggle to modify the solution internally. At every step, the firm's revenue model rewards extending the timeline, adding phases, and maintaining your dependency on their team.

This is the "complexity inflation" problem. Outsourcing firms often add layers of complexity: governance frameworks, multi-phase rollout plans, extensive discovery phases, capability assessments, change management workstreams. Some of this is genuinely valuable. But much of it serves to increase billable scope. The fastest way to know if an AI agent works is to build it and measure the results, not to plan it for 12 months. Yet planning for 12 months generates 12 months of billable revenue.

For strategy firms, there is an additional layer. Even when the intent is genuine and the timeline is reasonable, the people running the engagement are advisors, not builders. They can diagnose problems, design frameworks, and present recommendations. But when it comes to building production AI agents, they depend on developers they often cannot technically evaluate or challenge. The advisory layer between the client and the actual builder adds coordination cost, slows decision-making, and dilutes accountability. The question is not whether the strategy firm's recommendation is smart. It usually is. The question is whether an advisory-led organization can execute at the speed AI deployment demands.

Nexus addresses this differently, not by dismissing the value of expertise, but by delivering it through a model where the incentives are structurally aligned with your goals.

Builders in control, not advisors. At Nexus, the people who sit with your team are Forward Deployed Engineers: real engineers who implement directly. They write code, debug production issues, make architectural decisions, and deploy agents on a full-stack platform that Nexus develops itself. There is no coordination layer between an advisory partner and a developer team. There is no IT dependency to route requests through. The builders are in control of the engagement, from scoping through production. This is the structural inverse of how strategy firms operate, where advisory partners control client relationships and builders are subordinate. Nexus's CEO, Assem Chelli, is a former McKinsey consultant who saw this dynamic firsthand: brilliant strategy work that stalled at execution because the people in control had never built production software. Nexus was designed from the ground up so that builders lead, and the platform gives them everything they need to deliver without intermediaries.

Expert guidance, like consultants provide, but with aligned incentives. FDEs identify the highest-impact use cases by analyzing your specific operations. They design agents tailored to your workflows, systems, and edge cases. They handle integration complexity so your team does not need to pull engineers off product work. They manage organizational change, because deploying AI at scale is 10% technology and 90% getting people to adopt it. This is the same caliber of expertise you get from a top consulting firm. The critical difference: FDEs are included in the platform. You do not pay for their time separately. There is no day rate. No incentive to stretch the engagement. Their success is measured by how quickly they get your agents to production, not by how many hours they log.

Platform ownership, unlike custom builds. Your business teams build and own the agents on the Nexus platform. When requirements change, your team iterates directly. No change requests. No waiting for consultant availability. No additional billable hours. In a consulting model, every modification is a new project with a new budget; the firm benefits each time you need something changed. With Nexus, iteration is self-service. The platform handles infrastructure, security (SOC 2 Type II, ISO 27001, ISO 42001, GDPR), and 4,000+ integrations. Each new agent builds on the foundation already in place. Lambda went from one agent to a fleet across sales and marketing, with each new agent deploying in days.

Transfer of ownership, not creation of dependency. The consulting business model structurally rewards keeping knowledge within the consulting team, because that knowledge is what justifies the next engagement. FDEs are structured to do the opposite: make your team self-sufficient. They build with you, not for you. Your team understands what was built because they were building alongside the FDE the entire time. There is no incentive to withhold knowledge, because Nexus does not profit from your team's inability to operate independently.

Nexus starts with a 3-month POC tied to measurable outcomes, not a 6-month discovery phase. Because the fastest way to know if something works is to build it and measure it. Consulting firms often propose months of planning before any agent reaches production. Nexus proposes building the agent and proving the value. This is why Nexus converts 100% of POCs to annual contracts. The value is demonstrated before commitment, and the customer owns the result.

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Quick decision guide

The most important question to ask: who profits when the project takes longer? If the answer is "the vendor," the incentives are misaligned with your goals. If the answer is "nobody" or "we both lose," the incentives are aligned.

Choose outsourcing or consulting if...

• You need a multi-year, cross-functional transformation program that goes beyond AI agents to include strategy, operating model redesign, cloud migration, and organizational change at scale. The time-based billing model is acceptable because the scope genuinely warrants it.
• You need board-level or regulatory credibility where the name on the engagement letter matters as much as the technology delivered.
• You have not yet determined your AI strategy and need help defining where AI fits, what the roadmap should be, and how to align stakeholders. This is where advisory-led firms genuinely excel; strategy, alignment, and roadmap design are what they were built for. Just be aware that the firm advising you on strategy may recommend an approach that generates more billable work for the same firm, and that the transition from "advising on what to build" to "actually building it" is where advisory organizations historically struggle.
• The AI project requires deep R&D or novel research that does not map to established enterprise workflow patterns.
• You need hundreds of consultants across geographies for a large-scale, multi-workstream IT program.
• Cost-per-hour is your primary constraint and a 6-12 month timeline is acceptable for initial delivery. Consider, though, whether 6-12 months of low hourly rates ultimately costs more than 4 weeks at a different price point.
• Your challenge is custom ML model development (computer vision, predictive analytics, recommendation engines) rather than agents completing business workflows.
• You are comfortable with the structural trade-off: the firm earns more when the project is larger and longer, which means the incentive to simplify, accelerate, or reduce scope works against their revenue model.

Choose Nexus if...

• You need AI agents in production in weeks, not quarters. Orange deployed in 4 weeks. Lambda deployed in days. At one client, an outsourcing firm spent a year just planning; Nexus delivered production agents in 4 weeks.
• You want your business teams to own the agents and iterate without filing change requests with an external team.
• You have already done the strategy work (sometimes with a consulting firm) and now need execution, not more planning phases.
• You want per-agent economics, not day-rate economics. Costs tied to value delivered, not consultant hours consumed. Nexus earns when agents deliver results; nobody profits when projects drag on.
• You do not want to create a permanent consulting dependency for AI. FDEs transfer ownership to your team; they do not extend the engagement. There is no structural incentive to keep you dependent.
• You want builders in control of your AI deployment, not advisors. At Nexus, the people leading your engagement are engineers who implement directly on a platform Nexus builds itself. No advisory layer between you and the people doing the work. No project managers relaying requirements to developers they cannot technically challenge. If your past experience with strategy firms involved months of excellent slides followed by slow, dependency-laden execution, this is the structural alternative.
• Your use cases are business workflows (sales operations, customer support, HR, marketing) rather than multi-year IT transformation.
• You need enterprise governance from day one, not custom-built per engagement (which adds billable scope). SOC 2 Type II, ISO 27001, ISO 42001, GDPR certified.
• You want to scale from one agent to many without each new use case requiring a new project, new timeline, and new budget. In a consulting model, every new agent is a new revenue opportunity for the firm. With Nexus, each new agent builds on the existing foundation.
• You want incentive alignment. Nexus starts with a 3-month POC tied to measurable outcomes. If it does not work, you walk away. There is no 12-month planning phase generating billable hours before you see a single result.

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What enterprises experienced

These stories share a common thread: enterprises that had access to consulting firms, outsourcing partners, and internal engineering teams chose a platform approach. In each case, the deciding factor was not capability but structural incentives and speed to production.

Lambda: $4B+ AI company chose platform over build or outsource

Lambda builds supercomputers for AI training. They employ world-class engineers. They had every option: build internally, hire an agency, engage a consulting firm. Their CTO concluded the opportunity cost was too high. A consulting engagement would have meant months of scoping, design, and custom development, all billable, before a single agent reached production.

Joaquin Paz, Head of Sales Intelligence and not an engineer, built an autonomous research agent monitoring 12,000+ enterprise accounts in days. Lambda has since expanded to a fleet across sales and marketing. No consulting firm. No day rates. No phases.

Results:

• $4B+ in cumulative pipeline identified
• 24,000+ research hours added annually (equivalent to 12 full-time analysts)
• Deployed in weeks, not the months a consulting engagement would require
• Anticipated value: more than $7M by 2026

"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

Orange: multi-billion euro telecom deployed in 4 weeks

Orange Group has 120,000+ employees, significant internal engineering resources, and the budget to engage any consulting firm. Their business team (not engineering, not an external consultancy) built customer onboarding agents on Nexus.

Results:

• 50% conversion improvement
• $4M+ incremental yearly revenue
• 4-week deployment timeline
• 100% adoption by sales teams
• 100% compliance with full audit trails

A comparable consulting engagement would have required weeks of scoping alone, all billable at $300-700/hour. Orange had agents in production before most consulting projects finish the discovery phase. No governance framework needed to be custom-built; the platform provided it from day one.

The 1-year plan vs. 4 weeks to production

At one of Nexus's current clients, an outsourcing firm had been engaged to deploy a knowledge assistant. They were in "project management mode": running governance meetings, consolidating requirements, building planning documentation, coordinating across workstreams. After a full year, they had only finalized the planning of the first knowledge assistant and had only begun to consolidate the knowledge base. No agent in production. Twelve months of billable work had produced a plan.

When Nexus came in, the team scraped the data, implemented the agent, and pushed it to production within 4 weeks.

This is an extreme case. Not every outsourcing engagement takes a year to produce a plan. But it illustrates the structural dynamic clearly: when a firm is paid for time and effort, there is no structural urgency to ship. Every additional meeting, every additional planning document, every additional governance review is revenue. With Nexus, the incentive runs the other way. Nexus earns when agents are in production delivering value, not when projects are in planning.

European telecom operator: tried other approaches, succeeded with Nexus

A multi-billion euro telecom with 13,000+ employees spent 6 months trying to build AI use cases with Microsoft Copilot Studio. Zero production use cases deployed. They then deployed a multi-agent suite with Nexus across support, compliance, registration, and escalation handling. 40% of support capacity freed. 100% compliance assurance. Millions of customer interactions handled. No multi-phase consulting roadmap required.

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All outsourcing and AI consulting comparisons

Big 4 and Global Management Consulting

Comparison	Summary
Nexus vs Accenture AI	World's largest consulting firm ($69.7B, 779,000 employees) with AI Refinery platform. Platform + service vs. global consulting at scale
Nexus vs Deloitte AI	Big 4 leader with deep regulated-industry credibility and Deloitte AI Institute. Board-level trust vs. production speed
Nexus vs BCG X	BCG's tech build arm with 3,000+ technologists and the 10-20-70 framework. Strategy-led AI vs. deployment-led AI
Nexus vs McKinsey / QuantumBlack	The most respected strategy firm with QuantumBlack driving ~40% of revenue. AI strategy vs. AI agents in production
Nexus vs PwC AI	Big 4 firm with strongest AI governance and regulatory compliance expertise. Agent OS platform vs. Nexus platform + FDEs
Nexus vs Capgemini AI	EUR 22.5B European technology and consulting firm with massive delivery capacity. Headcount scaling vs. platform scaling

European AI Boutiques

Comparison	Summary
Nexus vs ML6	Strong Belgian AI engineering consultancy with 450+ use cases and Google Cloud expertise. Custom ML builds vs. platform-deployed agents
Nexus vs Artefact	Global data and AI consultancy (1,700+ employees, valued at EUR 1B+). Data strategy and custom models vs. production agents owned by business teams
Nexus vs Xebia	Dutch-origin digital consultancy with 5,500+ professionals across AI, cloud, and software. Full-stack consulting vs. focused agent deployment
Nexus vs Thoughtworks	Premium engineering consultancy (10,500+ consultants) known for the Technology Radar and Agile Manifesto. Engineering excellence vs. speed to production

Global IT Outsourcing

Comparison	Summary
Nexus vs Infosys AI	$19B+ IT services giant with Topaz AI platform and 200+ pre-built agents. Services-led delivery vs. platform + embedded engineering
Nexus vs TCS AI	World's largest IT services company by headcount ($30B+, ~600K employees). Massive scale and delivery capacity vs. speed and business ownership
Nexus vs Cognizant AI	$21B IT services firm with Neuro AI platform and deep healthcare/banking expertise. Offshore delivery model vs. embedded FDE model
Nexus vs Endava	Nearshore custom engineering firm (~$970M, 11,400 employees) with strong Eastern European talent. Bespoke software builds vs. platform-deployed agents

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Worth exploring?

If your team has been evaluating consulting firms or IT services partners for AI agent deployment, it is worth asking one structural question before signing: does this vendor profit when the project takes longer?

With day-rate and time-based billing models, the answer is yes. The firm earns more when timelines extend, when scope expands, when additional phases are added. This is not about intentions; it is about how the business model works.

Orange, a multi-billion euro telecom with access to every consulting firm on the planet, chose a platform approach. Business teams deployed in 4 weeks. $4M+ incremental yearly revenue. 100% adoption. Lambda, a $4B+ AI company with world-class engineers, chose to buy instead of build or outsource. $4B+ pipeline identified. Projected value: more than $7M by 2026. At another enterprise, an outsourcing firm spent a year planning what Nexus delivered in 4 weeks.

Every Nexus engagement starts with a 3-month proof of concept tied to specific, measurable outcomes. Not a 6-month discovery phase. Not a capability assessment. A POC where agents go to production and results are measured. Forward Deployed Engineers are included, not billed by the hour. You see results before committing. You can exit anytime. Nobody profits from dragging this out.

Read how Orange deployed in 4 weeks --> (case study)

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