Nexus vs Accenture: Platform vs Global IT Services

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Quick honest summary

Accenture is one of the world's largest professional services firms. $69.7B in annual revenue. 779,000 employees. Partnerships with every major technology vendor (NVIDIA, OpenAI, Google, Microsoft, Anthropic, Salesforce). Their AI practice generated $2.7B in generative and agentic AI revenue in fiscal 2025, tripling year-over-year. They have 77,000 AI and data professionals. They launched AI Refinery (built on NVIDIA AI Foundry) with plans for 100+ industry-specific agent solutions. When an enterprise needs a multi-year, cross-functional transformation program that touches strategy, technology, operations, and change management simultaneously, Accenture is one of the few firms on the planet that can deliver it.

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 6-18 months for a consulting engagement to deliver.

This comparison is not about whether Accenture is good at what they do. They are. Unlike pure strategy firms like McKinsey or BCG, Accenture has genuine technology delivery capability: real engineers, real implementation teams, real production deployments. But at the partner and account leadership level, the firm is still advisory-led. The people who control client relationships, scope engagements, and set timelines are advisors and account managers, not builders. This is a structural difference worth understanding, and it shapes how engagements actually run. It is about a fundamental structural difference in how the two models are incentivized. Consulting firms bill for time: hours, days, phases. The longer an engagement runs and the more consultants it requires, the more revenue the firm generates. This is not a criticism of the people involved; it is a description of the business model. Accenture has talented consultants. But the incentive structure rewards effort and duration, not speed of delivery.

Nexus operates under the opposite incentive: you pay for the platform and for agents in production, not for hours of consulting time. Forward Deployed Engineers are included, not billed separately. Nexus has no structural reason to stretch timelines or inflate complexity. The faster agents reach production and deliver measurable results, the stronger the relationship.

The question worth asking: for deploying AI agents on specific business workflows, do you need a $300-500/hour consulting engagement where the provider profits from longer timelines? Or do you need a platform that goes live in weeks, where the provider is structurally incentivized to deliver results quickly, and your business teams own and iterate on the agents directly?

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Side-by-side comparison

Dimension	Accenture AI	Nexus
What you get	Custom-built AI solutions by consulting teams AI Refinery platform for building and orchestrating agents Strategy consulting, system integration, and managed services	Enterprise AI agent platform + embedded FDEs Business teams build and own agents Ongoing optimization included
Who builds it	Accenture consultants and engineers design, build, and implement Your internal teams participate Accenture leads the engagement On AI projects, consultants often sit between the client and the technical team as coordinators rather than builders	Business teams build and deploy agents with FDE support FDEs are builders who implement directly on a full-stack platform built in-house, no IT dependency Your people own the outcome from day one No permanent external dependency
Timeline to production	Typically 3-18 months depending on scope Discovery, design, build, testing, deployment, change management 8-18 week modular programs for specific use cases Structural incentive: longer timelines mean more billable hours	2-6 weeks for most enterprise agents FDEs work alongside your team from day one POC in production before consulting finishes discovery Structural incentive: faster delivery strengthens the partnership
Pricing model	Day rates: $300-500/hour depending on seniority and geography Project-based fees or managed service contracts Costs scale linearly with scope and duration Revenue model rewards more consultants and longer phases	Per-agent pricing tied to value delivered FDEs included, not billed by the hour 3-month POC with measurable outcomes before annual commitment Costs do not scale linearly as you add agents
What you own after	Accenture delivers the solution Your team maintains it or retains Accenture for ongoing support Knowledge often resides with the consulting team Codebase may require Accenture-specific tooling	Your business teams own agents, workflows, and logic No dependency on external consultants for modifications Teams iterate directly on what they built
Ongoing support	Managed services contracts (additional cost) Or your internal team maintains what was built Requires understanding the custom implementation Ongoing dependency generates recurring revenue for the firm	Continuous optimization included in the platform FDEs help analyze performance and refine escalation logic Scale agents to new teams and processes No separate managed services contract required
Governance and compliance	Governance built into custom implementations Strong compliance, especially in regulated industries 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 the platform, not per project
Scalability	New use cases mean new engagements, timelines, and budgets Each project is largely independent Scaling generates proportionally more consulting revenue	Each new agent builds on the existing foundation Business teams deploy additional agents in days, not months 4,000+ native integrations Scaling does not require proportionally more spend
Vendor relationship	Large account team, multiple practice leads Complex governance Relationship-driven, long-term partnership model Firm profits from expanding scope and extending timelines	Dedicated FDEs embedded with your team Direct partnership focused on measurable outcomes Provider profits when you succeed, not when engagements extend

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When Accenture is the better choice

Accenture is genuinely the right partner for certain enterprise needs, and there are situations where their scale, depth, and breadth are exactly what is required:

• You need a multi-year, cross-functional transformation program. If the initiative involves rethinking your entire operating model, touching strategy, technology, operations, talent, and change management across the organization simultaneously, Accenture has the scale and experience to run that kind of program. They do this for the world's largest companies across every industry, and few firms can match their ability to deploy hundreds of consultants on a single transformation.

• You need strategy consulting alongside implementation. If you are still defining your AI strategy, identifying which processes to transform, and need a partner to help think through the "what" before the "how," Accenture combines strategy consulting (through Accenture Strategy) with implementation capability. That end-to-end advisory is valuable when you are early in the journey.

• Regulatory credibility matters as much as the solution itself. In certain regulated industries and government contexts, working with a Big 4-adjacent firm like Accenture carries credibility that matters for board reporting, regulatory submissions, and stakeholder confidence. The name on the engagement letter can matter as much as the technology.

• You need deep, industry-specific domain expertise baked into the build. Accenture's industry practices (financial services, health, communications, energy, etc.) bring decades of domain knowledge. If the AI solution requires deep industry-specific logic that only comes from years of operating in that sector, their industry agent solutions and vertical expertise are hard to replicate.

• The project requires integrating AI into a massive, complex legacy landscape. If the challenge is not just deploying agents but rewiring the systems architecture underneath (SAP migrations, cloud transformations, legacy system modernization), Accenture's systems integration capabilities are relevant. They have done this at scale for decades.

• You want a single vendor for everything. Accenture can handle strategy, technology, operations, talent, and managed services under one umbrella. If your organization prefers (or requires) a single-vendor approach to reduce procurement complexity, that consolidation has value.

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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 (or evaluated consulting engagements), and they concluded that the cost, timeline, or dependency model did not make sense for deploying agents on business workflows. Often, they recognized that the consulting model's structural incentives were misaligned with what they actually needed: fast results, business ownership, and predictable costs.

• You need AI agents in production in weeks, not months. A typical Accenture AI engagement involves discovery, design, build, testing, and deployment phases that take 3-18 months. Each phase generates billable hours, which means the structure itself rewards longer timelines. Nexus agents go live in 2-6 weeks. For enterprises under pressure to show AI results this quarter (not next year), that timeline difference is decisive. This is the core tension in the build vs buy decision for AI agents. 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 create a consulting dependency. When a consulting firm builds your AI solution, the knowledge of how it works lives with the consulting team. Changes require going back to the firm, waiting for availability, and paying for more hours. 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 consulting engagement. No backlog.

• The math on consultant day rates does not work for your use case. At $300-500/hour, a 6-month engagement with a team of 4-8 consultants can cost $1M-3M+ before you see production results. And that is for one use case. The billing model creates a structural incentive to staff generously and scope broadly: more consultants and more phases mean more revenue for the firm, even when a leaner approach would deliver the same outcome. Nexus per-agent pricing does not scale linearly. The second, third, and fourth agents build on the foundation already in place.

• You have already tried a consulting engagement and ended up with something rigid. This is a pattern we see repeatedly: an enterprise hired a consulting firm to build a custom AI solution. It took months. It worked for the original requirements. But when the business changed, the solution could not adapt without another engagement, and the firm was happy to take that call, because every modification is a new revenue opportunity. With Nexus, agents adapt to changing requirements. Business teams iterate directly, without external dependency and without generating a new statement of work.

• You want embedded expertise without the consulting overhead. Forward Deployed Engineers provide the same caliber of expertise you would get from a top consulting firm, but embedded in your team with a focus on getting agents into production. FDEs identify the highest-impact use cases, design agents for your specific reality, handle integration complexity, and manage change. This is also what separates the platform approach from building with developer frameworks in-house: you get expertise without the months of custom engineering. The difference is structural: even at Accenture, which has more genuine technical depth than most consulting firms, the consultants on AI projects often sit between the client and the engineering team as coordinators. They manage, they advise, they present. FDEs build. They implement directly on the Nexus platform, writing agent logic, configuring integrations, and deploying to production alongside your team. The incentive model is different too: consultants work within a model that rewards longer, larger engagements. FDEs work within a model that rewards making your team self-sufficient and delivering measurable outcomes.

• You want enterprise governance out of the box, not custom-built per project. When a consulting firm implements governance, it is designed and built for each engagement. That adds time, cost, and complexity. 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.

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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 and the budget to engage any consulting firm in the world.

Where a typical consulting engagement takes 6-18 months to deliver, Orange deployed 120 AI agents with Nexus. Their business team (not engineering, not a consulting firm) built customer onboarding agents using the Nexus platform. Deployed in 4 weeks 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 consulting engagement would have involved months of discovery, design, and build, with each phase generating billable hours and the consulting team owning the implementation knowledge. Orange's business team owns everything. No ongoing consulting dependency. No managed services contract extending the revenue stream.

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 consulting engagement, it was Lambda.

Their CTO evaluated the options and concluded: the opportunity cost was too high. Every hour spent on internal build (or managing a consulting engagement) 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
• 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 consulting engagement would have taken
• Non-technical team member built and owns the agent
• Projected 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

Enterprise client: outsourcing firm spent 1 year, Nexus delivered in 4 weeks

An outsourcing firm was embedded at one of Nexus's clients in "project management mode" to build a knowledge assistant. After 12 months, they had only finalized planning for the first assistant and had just begun to consolidate the knowledge base. No production deployment. No measurable results. Twelve months of billable hours.

Nexus came in. Within 4 weeks, the team scraped the data, implemented the agent, and pushed it to production. A working knowledge assistant, live and serving real users, in less time than the previous firm had spent on one planning phase.

This is an extreme case, but it illustrates the structural issue clearly. The outsourcing firm was not incompetent. Their consultants were capable. But the incentive model rewarded thorough planning, governance layers, and phased rollouts, each of which generated revenue. There was no structural pressure to ship. Nexus, paid for outcomes and not hours, had every reason to deliver as fast as possible.

European telecom: tried Copilot Studio for 6 months, zero production results

A multi-billion euro telecom operator spent 6 months trying to build AI use cases with Microsoft Copilot Studio. The result: zero production use cases deployed. In a comparable timeframe with Nexus, they deployed a dozen agents across support, compliance, and customer operations.

This is the pattern that matters: not whether a particular tool or consulting firm can theoretically build the solution, but how long it actually takes to get agents into production delivering value.

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Key differences explained

Platform vs. services: the fundamental model difference

This is the core distinction, and it is worth understanding clearly.

Accenture operates a services model. They assign consulting teams to your engagement. Those teams analyze your requirements, design a solution, build it, test it, and hand it over. The work is custom for each engagement. The quality is often excellent, and Accenture has more genuine technology delivery capability than pure strategy firms. They employ real engineers who build real systems. But at the partner and leadership level, the people who control engagements, set scope, and manage client relationships are still advisory-led. Even on AI projects, the consultants involved often function as coordinators between the client and technical teams rather than as hands-on builders. The model has structural characteristics, including an incentive reality that is worth understanding honestly:

• Each engagement is a project. It has a start date, end date, scope, and budget. New requirements mean new scope, new timelines, and often new budget approvals. The firm benefits each time scope expands.
• Knowledge concentrates in the consulting team. The people who designed and built the solution understand it best. When they move to other engagements, that knowledge goes with them. This creates a dependency that generates ongoing managed services revenue.
• Scaling means more consultants. Adding more use cases means more consulting hours, more project management, more engagement governance. Costs scale roughly linearly with scope, and so does the firm's revenue.
• The incentive structure rewards duration, not speed. This is systemic, not personal. The firm's revenue is a function of billable hours multiplied by headcount. Delivering faster with fewer people directly reduces revenue. This does not mean consultants deliberately slow down; it means the system has no structural pressure to optimize for speed.

Nexus operates a platform + service model. The platform handles infrastructure, integrations, security, compliance, and agent deployment. Forward Deployed Engineers provide the expertise that consulting firms sell, but embedded with your team to make them self-sufficient. The model has different structural characteristics, and critically, different incentives:

• The platform compounds. Each agent builds on the foundation already in place. The second agent is faster than the first. The fifth is faster still. Lambda went from one agent to an expanding fleet, with each new agent deploying in days.
• Knowledge stays with your team. Business teams build, own, and iterate on agents directly. When an FDE engagement evolves, your team has full capability to operate independently. There is no knowledge asymmetry that generates dependency revenue.
• Scaling means more agents, not more consultants. Adding use cases does not require proportionally more external resources. The platform handles the complexity. 4,000+ integrations are already built.
• The incentive structure rewards results, not duration. Nexus earns from agents in production delivering value. FDEs are included, not billed by the hour. There is no structural benefit to stretching timelines, adding unnecessary phases, or inflating complexity. The faster your agents deliver results, the faster the partnership grows.

Neither model is universally better. But for deploying AI agents on specific business workflows (sales operations, customer support, marketing, HR), the platform model tends to deliver faster, at lower total cost, with greater business ownership.

Ownership: who controls the AI after it is deployed

This is where the model difference becomes most tangible.

After a consulting engagement ends, the enterprise is left with what was built. Maintaining it, modifying it, and scaling it requires either (a) retaining the consulting firm on a managed services contract, or (b) having internal teams learn the custom implementation well enough to operate it independently. In practice, most enterprises choose option (a), which creates an ongoing dependency. This is not accidental. The consulting model is structurally designed so that delivery creates follow-on revenue: the more complex the custom build, the harder it is to maintain without the firm that built it.

With Nexus, business teams own the agents from day one. They build them (with FDE support), they understand them, and they iterate on them directly. When Lambda's team needed to change data sources, update account segmentation, or adjust priorities, they did it themselves. No consulting engagement. No managed services contract. No waiting for external availability.

"We've changed data sources, updated our account segmentation, adjusted priorities. The agent adapts. With the workflow tools we tried before, every change meant starting over."

Joaquin Paz, Head of Sales Intelligence, Lambda

Time to value: weeks vs. months compounds over a year

The timeline difference is not just about speed. It compounds.

Consider a 12-month window. With a typical consulting engagement, where every month of activity generates revenue for the firm:

• Months 1-3: Discovery, requirements, design
• Months 4-8: Build and testing
• Months 9-10: Deployment and stabilization
• Months 11-12: First agents in production, beginning to generate value

That is 10 months of billable work before production value begins. There is no structural incentive to compress this timeline. In fact, the opposite is true: the firm earns more when each phase runs longer or when additional "capability assessments" and "governance frameworks" are layered in.

With Nexus:

• Weeks 1-4: First agents in production
• Months 2-12: Iterating, optimizing, and scaling to additional use cases

By the time a consulting engagement delivers its first production agent, a Nexus deployment can have multiple agents operating, optimized, and generating measurable results. Orange generated $4M+ in incremental yearly revenue starting from a 4-week deployment. Lambda identified $4B+ in pipeline. That value was accruing while a consulting engagement would still have been in the design phase.

Total cost: day rates vs. per-agent pricing

Accenture's consulting rates vary by seniority, geography, and engagement type, but enterprise AI engagements typically involve blended rates of $300-500/hour per consultant. A 6-month engagement with a team of 4-8 people represents a significant investment before any agent reaches production.

For context: a mid-sized engagement (6 consultants at $400/hour average, 6 months) can easily reach $2M-4M. That covers one set of use cases. Scaling to additional departments or workflows means additional engagements with similar cost structures.

Nexus pricing is per-agent, tied to value delivered. The 3-month proof of concept is structured so you see measurable results before committing to an annual contract. As you add agents, costs do not scale linearly because each new agent builds on the platform foundation. The total cost of deploying 5-10 agents with Nexus is typically a fraction of what a comparable consulting engagement would cost for the same scope.

This does not mean Accenture is overpriced for what they deliver. Their engagements often include strategy work, organizational design, change management, and system integration that go well beyond agent deployment. But it is worth noting how consulting firms can make problems feel more complex than they need to be. Governance frameworks, multi-phase rollouts, discovery phases, capability assessments: these layers may sometimes be justified, but they also expand billable scope. A consulting firm has a structural incentive to add complexity. Nexus has a structural incentive to reduce it. We start with a 3-month POC because the fastest way to know if something works is to build it and measure it, not to spend months planning it.

Forward Deployed Engineers: the expertise of consultants, the model of a platform

FDEs are Nexus's answer to the "we need expertise, not just software" reality.

Nexus's CEO is a former McKinsey consultant, which gives the company an insider understanding of how consulting firms operate: the incentive structures, the staffing models, the way engagements expand. That perspective shaped Nexus's model deliberately. FDEs are not advisors who coordinate between your team and a separate technical team. They are builders who implement directly, on a full-stack platform built in-house, with no dependency on external IT teams or third-party integrators.

Deploying AI at scale is 10% technology and 90% organizational change. Enterprises know this, which is one reason they hire consulting firms. The expertise matters: identifying the right use cases, designing agents that fit specific workflows, handling integration complexity, managing change.

FDEs provide this same expertise, but within a fundamentally different incentive structure:

• Consultants are incentivized to extend engagements. The business model rewards billable hours and long-term managed services contracts. Every additional phase, every governance review, every capability assessment generates revenue. FDEs are incentivized to make your team self-sufficient, because Nexus earns from agents in production, not from hours billed.
• Consultants build for you. FDEs build with you. Your team is hands-on from day one, which means they understand what was built and can iterate independently. A consultant building for you creates a dependency that generates future revenue. An FDE building with you eliminates that dependency by design.
• Consultant knowledge leaves when the engagement ends. FDE knowledge transfers to your team because they were building alongside you the entire time. This is structurally intentional: Nexus benefits when your team is capable and autonomous, not when they need to call for help.

This is why Nexus converts 100% of POCs to annual contracts. The engagement is structured to deliver measurable value quickly, with your team fully capable of owning the result. There is no incentive to delay, overcomplicate, or create dependency.

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Frequently asked questions

Can we use Accenture for strategy and Nexus for agent deployment?

Yes. Some enterprises use consulting firms for broader transformation strategy (operating model design, organizational change, technology roadmap) and Nexus specifically for deploying AI agents on business workflows. The two are not mutually exclusive. Accenture can define the "what" and "where" of your AI strategy. Nexus can handle the "how" of getting agents into production quickly, with your business teams owning the result. In fact, separating strategy from execution can be healthy: the firm defining the strategy does not also profit from stretching the execution timeline.

We already have an Accenture engagement. Can Nexus complement it?

Absolutely. If Accenture is handling broader digital transformation (cloud migration, system integration, operating model redesign), Nexus can run in parallel to deploy AI agents on specific workflows. The agents integrate with whatever systems Accenture is implementing, through 4,000+ native integrations. Several enterprises work with consulting firms on strategic programs while using Nexus for the agent deployment layer.

Accenture has AI Refinery. How is Nexus different?

AI Refinery is Accenture's platform for building and orchestrating AI agents, built on NVIDIA AI Foundry. It is a capable platform, especially for enterprises already in the Accenture ecosystem. The key differences are in the delivery and incentive model: AI Refinery is typically deployed through Accenture consulting engagements (with consulting teams leading the implementation and billing for hours), while Nexus is deployed with Forward Deployed Engineers who work alongside your business teams so they own the result. Even with a platform layer like AI Refinery, the underlying business model remains consulting: you still pay for consultant time, and the firm still profits from longer, larger engagements. The timeline, cost structure, and ownership model remain fundamentally different.

Is Nexus too small for our enterprise? Accenture has 779,000 employees.

Company size and delivery capability are different things. Orange Group (120,000+ employees, multi-billion euro revenue) and Lambda ($4B+ valuation, 500M+ ARR) both chose Nexus. The question is not how many employees the vendor has, but whether the solution delivers measurable results. Nexus is Y Combinator-backed, SOC 2 Type II, ISO 27001, and ISO 42001 certified, with $4M seed funding from General Catalyst and Y Combinator. The POC model means you validate results before committing. Every POC has converted to an annual contract.

How does Nexus handle change management compared to a consulting firm?

Change management is built into the Nexus engagement model, not sold as a separate workstream. FDEs help frame the change (agents make teams more powerful, not replace them), train teams on new workflows hands-on, build confidence through small wins before scaling, and address concerns about transparency and control. At Orange, this approach delivered 100% adoption. The difference: consulting firms often treat change management as a separate workstream with additional consultants and cost, because a separate workstream means separate billing. Nexus treats it as inseparable from agent deployment, because it is included in the platform, not an upsell opportunity.

What if we need Accenture's industry expertise?

Accenture's industry depth is real. If the AI use case requires deep, industry-specific domain knowledge that only comes from decades of operating in that sector (regulatory frameworks, industry-specific data models, sector-specific compliance requirements), that expertise is valuable. However, most AI agent deployments on business workflows (sales, support, marketing, HR) rely on process knowledge, not industry-specific expertise. Your business teams already have the domain knowledge. Nexus and FDEs help translate that knowledge into production agents.

What does the 3-month POC look like?

Every Nexus engagement starts with a 3-month proof of concept tied to specific, measurable outcomes defined upfront. Most agents are in production within the first 2-6 weeks. A Forward Deployed Engineer is embedded with your team for the entire period. You see the results, measure the impact, and decide whether to continue. You can exit anytime. Compare this to a consulting model where the first months are typically spent on discovery and planning, generating billable hours before any production value exists. Nexus starts with a POC because the fastest way to know if something works is to build it and measure it. This is why our POC-to-contract conversion rate is 100%: we do not move forward unless the value is clear.

Is Accenture pricing really $300-500/hour?

Consulting rates vary significantly by engagement type, seniority mix, geography, and relationship. Accenture (like other large consulting and systems integration firms) does not publicly disclose client billing rates. The $300-500/hour range reflects industry estimates for enterprise technology consulting engagements, with rates going higher for senior partners and strategy work. The precise rate matters less than the structural model: when you pay by the hour, the provider earns more when things take longer. When you pay per agent in production, the provider earns more when things ship faster. That incentive difference shapes everything: staffing decisions, timeline estimates, how many "phases" the engagement includes, and how quickly you see production results.

Why does the incentive structure matter so much?

Because it shapes behavior at every level, even when the people involved have the best intentions. A consulting firm billing $300-500/hour has no structural incentive to tell you that your problem is simpler than you think. In fact, the more complex a problem feels, the larger the engagement, the more phases it requires, and the more revenue it generates. This is not about bad people. It is about a business model where revenue is a function of time and headcount. Nexus operates under the opposite structure: FDEs are included, not billed separately. The platform earns from agents delivering value in production. Every incentive points toward shipping fast, reducing complexity, and making your team self-sufficient.

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Worth exploring?

If your team has been evaluating consulting firms for AI agent deployment, it is worth asking a direct question: is the provider you are considering structurally incentivized to deliver results quickly, or to bill for the time it takes to get there?

Orange, a 120,000+ employee telecom operator with the budget for any consulting engagement, chose a platform approach. Business teams deployed in 4 weeks. $4M+ in incremental yearly revenue. 100% adoption. No ongoing consulting dependency.

Lambda, a $4B+ AI company whose CTO considered building internally, concluded the opportunity cost was too high. Non-technical team members built and own the agents. $4B+ pipeline identified. Projected value: more than $7M by 2026. No billable hours. No phased rollout. Production in days.

At another enterprise, an outsourcing firm spent 12 months in project management mode on a knowledge assistant; they had only finished planning. Nexus delivered the working agent in 4 weeks. Same problem, fundamentally different incentives.

Every Nexus engagement starts with a 3-month proof of concept tied to specific outcomes. Forward Deployed Engineers are included, not billed separately. You see results before committing. You can exit anytime. The structure is designed so that Nexus only succeeds when you do.

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

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• Nexus vs TCS AI. IT services and outsourcing: similar platform vs. services dynamics at different price points
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• Back to all comparisons -->