Nexus vs Endava: Platform vs Nearshore Engineering

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Quick honest summary

Endava is a publicly traded (NYSE: DAVA), London-headquartered technology services company with approximately 11,500 employees across 29 countries. They generated roughly £772M (approximately $980M) in revenue in fiscal 2025. Founded in 2000, Endava built its reputation on high-quality custom software engineering delivered through a nearshore model, with deep delivery centers across Eastern Europe (Romania, Moldova, Bulgaria, Serbia, and others) and expanding operations in Latin America and Asia-Pacific. Their engineering culture is strong, their technical talent is well-regarded, and their nearshore model offers European clients good timezone overlap at competitive rates. Endava has been investing significantly in AI capabilities, launching Programme Keystone to embed AI across delivery and operations, and developing Dava.Flow, their AI-native engagement methodology. They recently expanded their partnership with Cognition to scale agentic coding capabilities.

Nexus is an enterprise AI agent platform paired with white-glove service: Forward Deployed Engineers embedded with your team, change management support, and ongoing optimization. It is not just software you buy and figure out on your own. Nexus is built for enterprises that need agents completing business workflows in production, with business teams owning the outcome, not waiting months for a custom development engagement to deliver.

This comparison is not about whether Endava has good engineers. They do. Their talent in Eastern Europe is genuinely strong, and their nearshore model works well for custom software projects. The question is about the model, and specifically, about structural incentives. Nearshore firms bill by the day. The longer a project takes, the more the provider earns. Even firms with strong engineering cultures face this tension, because their revenue is tied to time, not to outcomes. For deploying AI agents on specific business workflows, do you need a team of nearshore engineers building something bespoke over months, with every additional month adding to the provider's top line? Or do you need a platform that goes live in weeks, where the vendor is incentivized to deliver results quickly because you are not paying for days?

Endava is the right choice when you need dedicated engineering teams to build complex, bespoke software applications. Nexus is the right choice when you need AI agents completing business workflows in production, fast, with business ownership and without creating a permanent dependency on a team whose revenue model rewards longer engagements.

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Side-by-side comparison

Dimension	Endava	Nexus
What you get	Custom software engineering by nearshore teams Digital transformation consulting System integration, AI/ML development Managed services Dava.Flow AI-native engagement methodology	Enterprise AI agent platform + embedded FDEs Business teams build and own agents Ongoing optimization included
Who builds it	Endava's nearshore engineers (Eastern Europe, LatAm, APAC) They design, build, and maintain the solution Your team participates but Endava leads	Business teams build and deploy agents with FDE support Your people own the outcome from day one No permanent external dependency
Timeline to production	Typically 3-12 months depending on complexity Requirements, architecture, sprints, QA, deployment Agile methodology, but still custom dev timelines Structural incentive: longer timelines mean more billable days	2-6 weeks for most enterprise agents FDEs work alongside your team from day one POC in production before custom projects finish requirements At one Nexus client, an outsourcing firm spent 1 year in project management mode before Nexus delivered in 4 weeks
Pricing model	Day rates for nearshore teams Lower than onshore, but costs scale linearly Time-and-materials or dedicated team models Average contracts $350K to $1.2M+ Provider earns more when projects take longer	Per-agent pricing tied to value delivered 3-month POC with measurable outcomes first Costs do not scale linearly as you add agents You never pay for FDEs by the day; Nexus is incentivized to deliver fast
What you own after	Endava delivers custom code and documentation Your team must maintain it (or retain Endava) Knowledge often resides with the dev team Custom codebase requires developers to evolve Retention creates ongoing billable dependency	Your business teams own agents, workflows, and logic No dependency on external developers Teams iterate directly on what they built
Ongoing support	Managed services or team retention (additional cost) Or your internal team maintains the codebase Requires hiring developers who understand it Ongoing support is a separate revenue stream for the provider	Continuous optimization included FDEs analyze performance, refine escalation logic Scale agents to new teams and processes
Governance and compliance	Compliance built into custom implementations Security designed per project Governance requires custom engineering each time	SOC 2 Type II, ISO 27001, ISO 42001, GDPR from day one Full audit trails, decision traceability, RBAC Enterprise governance built into the platform
Scalability	New use cases mean extending the team or timelines Or starting new engagements Each project is largely a new build More use cases means more billable hours for the provider	Each new agent builds on the existing foundation Business teams deploy additional agents in days 4,000+ native integrations
AI specialization	Growing AI/ML practice with Dava.Flow and Cognition Strong general engineering AI agent deployment not their core for 25 years	Purpose-built for AI agent deployment Agent-first architecture, 4,000+ integrations Every engagement focused on production AI agents

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When Endava is the better choice

Endava is a strong partner for specific types of technology engagements, and there are situations where the time-based billing model is appropriate and the structural incentive question is less relevant:

• You need dedicated engineering teams to build complex, bespoke software applications. If the project is a custom software product, a complex platform build, or a deeply bespoke application that does not map to agent-based workflows, Endava's nearshore engineering teams can deliver. They have been doing this well for over two decades. Custom software development is their core competency. For these kinds of projects, the time-based model makes sense because the work genuinely requires sustained engineering effort.

• You want nearshore delivery with strong timezone overlap for European operations. Endava's delivery centers in Romania, Moldova, Bulgaria, Serbia, and other Eastern European locations provide European clients with timezone-aligned engineering teams at rates that are competitive compared to onshore alternatives. Their CEO has described this as "close time zone delivery" with daily scrums between teams and clients. For organizations headquartered in Western Europe that want a dedicated, embedded development team without the cost of fully onshore resources, this model works well.

• You need dedicated development teams as an extension of your engineering organization. If you need 5, 10, or 50 engineers working as a long-term extension of your internal engineering team (on your product, your codebase, your roadmap), Endava's dedicated team model is designed for exactly this. They integrate their engineers into your workflows and processes. In this model, the incentive alignment is clearer: you are effectively augmenting your own team, and the work is governed by your roadmap and priorities.

• The project requires deep, bespoke engineering that does not fit agent-based automation. Not every technology initiative maps to AI agents. Complex data platform builds, custom application development, infrastructure engineering, legacy system modernization: these require traditional software engineering, and Endava has strong capabilities here. The incentive question matters less when the work is genuinely complex engineering rather than deploying a well-understood capability.

• You are already an Endava client and want to extend the relationship to include AI capabilities. If you already work with Endava on custom engineering and want to add AI elements to your existing projects, their Dava.Flow methodology and growing AI practice can build on the existing relationship and codebase.

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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 custom development engagements), and they concluded that the cost, timeline, or dependency model did not make sense for deploying agents on business workflows. Often, the turning point is recognizing the structural incentive misalignment: paying a firm by the day to deliver something that could be deployed in weeks creates a situation where the provider has no financial reason to move faster.

• You need AI agents in production in weeks, not months. A typical custom development engagement with Endava (or any nearshore engineering firm) involves requirements gathering, architecture design, sprint planning, development cycles, QA, and deployment. That is 3-12 months before agents reach production, and every additional month is additional revenue for the provider. There is no structural incentive to compress that timeline. Nexus agents go live in 2-6 weeks. Orange deployed customer onboarding agents in 4 weeks. Lambda went from zero to production in days. At another Nexus client, an outsourcing firm spent a full year in "project management mode," only finalizing planning for a first knowledge assistant. Nexus came in and delivered in 4 weeks: scraping, implementation, and push to production. For enterprises under pressure to show AI results this quarter, the difference is not just speed; it is alignment.

• You want your business teams to own the agents, not create a development dependency. When a custom engineering firm builds your AI solution, the knowledge of how it works lives with their development team. Changes require going back to the firm, requesting developer time, and paying for more hours. This dependency is not accidental; it is how the business model works. The provider benefits from being needed for every change. 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 development tickets. No backlog. No dependency.

• The math on day rates does not work for deploying AI agents. Endava's nearshore rates are competitive compared to onshore consulting, but costs still scale linearly with team size and duration. Cheaper hours are still hours, and the fundamental misalignment remains: longer projects mean more revenue for the provider. A 6-month engagement with a nearshore team of 5-8 engineers can easily reach $500K-1.5M+ before production. And that covers one set of use cases. Scaling to additional workflows means extending the team or starting new projects, each one another billable engagement. Nexus per-agent pricing does not scale linearly. You do not pay for FDEs by the day. The second, third, and fourth agents build on the platform foundation already in place.

• You need AI agent expertise specifically, not general software engineering. Even teams considering developer frameworks find the gap between prototype and production is larger than expected. Endava's engineers are strong at custom software development. But building production-grade AI agents that handle enterprise workflows, escalate intelligently, and integrate across 4,000+ enterprise systems is a specific discipline. Nexus and its Forward Deployed Engineers do this every day. It is the only thing Nexus does. That specialization shows in the speed and quality of deployment.

• You have already tried a custom build and ended up with something rigid. This is the dynamic explored in depth in the build vs buy comparison. A pattern we see repeatedly: an enterprise hired engineers (nearshore, onshore, or internal) to build a custom AI solution. It took months. It worked for the original requirements. But when the business changed, the custom code could not adapt without another engineering cycle, which of course means another set of billable hours for the provider. The rigidity is not just a technical limitation; it is a business model feature. Nexus agents adapt to changing requirements. Business teams iterate directly, without filing development requests.

• You want embedded expertise without the ongoing development dependency. Forward Deployed Engineers provide specialized AI agent expertise, embedded with your team, focused on getting agents into production and making your team self-sufficient. FDEs identify the highest-impact use cases, design agents for your specific workflows, handle integration complexity, and manage organizational change. The difference is structural: FDEs work to transfer capability to your team, not to create an ongoing billable relationship. Nexus succeeds when you become independent. A nearshore firm succeeds when you keep paying for their engineers.

• You want enterprise governance out of the box, not built from scratch. When a custom engineering team implements governance (audit trails, compliance, access controls, decision traceability), it is designed and coded from scratch for each project. That adds weeks or months of engineering time. 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 code.

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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 custom development firm in the world.

Where a nearshore engineering engagement would spend months in requirements and development, Orange deployed in 4 weeks. Their business team (not engineering, not a nearshore development firm) built customer onboarding agents using the Nexus platform, deployed across multiple European markets. The agents collect customer information, validate data, check system compatibility, and route complex cases with full context.

Results:

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

A comparable custom engineering engagement would have involved months of requirements, architecture, development sprints, and QA, with the engineering team owning the codebase knowledge and every additional month generating revenue for the provider. Orange's business team owns everything.

Enterprise client: 1 year of outsourced "project management," then 4 weeks with Nexus

This story illustrates the incentive misalignment clearly. A Nexus enterprise client had previously engaged an outsourcing firm to build a knowledge assistant. That firm spent a full year in "project management mode," cycling through requirements documentation, architecture reviews, stakeholder alignment sessions, and planning sprints. After 12 months, the project had not moved past planning. No production deployment. No working agent.

Nexus was brought in. Within 4 weeks, the team scraped the relevant knowledge sources, implemented the assistant, and pushed it to production. The same outcome the outsourcing firm had been planning for a year.

The outsourcing firm almost certainly had competent engineers. The problem was not talent. The problem was that every month of planning was another month of billable work. There was no structural incentive to compress the timeline. The provider earned more the longer the project lasted. This is the core tension of the time-based model applied to AI agent deployment: the work that delivers value (building and deploying the agent) takes weeks, but the engagement model rewards months.

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 custom development engagement with top-tier engineers, it was Lambda.

Lambda, a $4B+ AI company, chose a platform approach over custom builds. Their CTO evaluated the options, including nearshore firms and IT outsourcing, and concluded: the opportunity cost was too high. Every hour spent on internal build (or managing a nearshore development engagement) was an hour not spent on their core product. And paying a nearshore firm by the day to do it would have meant months of billable engineering time for something a non-engineer could accomplish with the right platform. 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 custom engineering project 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

European telecom: tried building for 6 months, zero production results

A multi-billion euro telecom operator spent 6 months trying to build AI use cases through custom development approaches. The result: zero production use cases deployed. Six months of billable engineering time, consumed entirely by requirements gathering, architecture debates, and sprint ceremonies. 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 development firm has talented engineers, but whether the model incentivizes delivery or billable activity. When the provider earns revenue by the day, there is no structural pressure to compress timelines. The 6 months of planning were 6 months of revenue for the provider, regardless of whether anything reached production.

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Key differences explained

Platform vs. custom engineering: the fundamental model difference

This is the core distinction, and it applies whether the custom engineering comes from Endava, another nearshore firm, or an internal team. But the distinction goes deeper than just "platform vs. services." It is about structural incentives: who profits when a project takes longer, and who profits when it ships faster.

Endava operates a custom engineering model. They assign development teams to your project. Those teams gather requirements, design architecture, write code, test it, and deploy it. The work is custom for each engagement. The engineering quality is often strong. But the model has structural characteristics that create incentive misalignment, even when the engineers themselves want to deliver quickly:

• Each project is a custom build. It has a start date, estimated timeline, scope, and budget. New requirements mean new development cycles, new sprints, and often new budget conversations. Every extension is additional revenue for the provider.
• Knowledge concentrates in the development team. The engineers who designed and built the solution understand the codebase best. When they rotate to other projects (or leave), that knowledge goes with them. This creates a dependency that generates ongoing billable work.
• Scaling means more engineers. Adding more use cases means more developer hours, more project management, more coordination overhead. Costs scale roughly linearly with scope, and every incremental use case is another engagement for the provider.
• You own code, but you depend on developers. The custom codebase is yours. But maintaining and evolving it requires developers who understand it, which often means retaining the team that built it. The provider benefits from this retention.
• The provider earns more when projects take longer. This is the structural reality. Even with strong engineering cultures and good intentions, the revenue model rewards duration. The 1-year knowledge assistant planning engagement that Nexus replaced in 4 weeks is the extreme case, but the incentive operates on every project.

Nexus operates a platform + service model. The platform handles infrastructure, integrations, security, compliance, and agent deployment. Forward Deployed Engineers provide specialized AI agent expertise, embedded with your team to make them self-sufficient. Critically, the incentive structure is inverted. Nexus earns per-agent pricing tied to value, not day rates tied to duration. The model has different structural characteristics:

• 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 dependency that generates ongoing revenue for Nexus.
• Scaling means more agents, not more engineers. Adding use cases does not require proportionally more external resources. The platform handles the complexity. 4,000+ integrations are already built. Each new agent is not a new billable engagement.

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 than engaging a nearshore engineering team to build it from scratch. And the incentive alignment is fundamentally different: Nexus earns by delivering working agents, not by billing engineering days.

Nearshore engineering vs. Forward Deployed Engineers: different expertise, different incentives

Endava's nearshore model gives you skilled software engineers at competitive rates with good timezone overlap. These engineers are excellent at writing code, building applications, and delivering software projects. Their strength is in general-purpose software engineering, and increasingly in AI/ML development. But the nearshore model, despite offering lower rates, carries the same structural incentive as any time-based service: cheaper hours are still hours, and the provider earns more when more hours are consumed.

Forward Deployed Engineers are a different kind of resource entirely, with a fundamentally different incentive structure. You do not pay for FDEs by the day. FDEs are AI agent specialists who:

• Identify the highest-impact use cases first. Not guessing based on templates, but analyzing your specific operations to find where agents deliver the most value.
• Design agents that fit your reality. Not building custom code from a requirements document, but configuring production-ready 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 product work. 4,000+ enterprise integrations are already built.
• Manage organizational change. Because 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.
• Transfer capability to your team. FDEs work to make your business teams self-sufficient, not to create an ongoing billable dependency.

The distinction matters on two levels. First, expertise: nearshore engineers build custom solutions for you, while FDEs build production agents with you and leave your team fully capable of owning the result. Second, incentives: nearshore engineers are billed by the day, which means the provider earns more the longer the engagement lasts. FDEs are part of a platform engagement where Nexus earns by delivering agents that work, not by extending timelines.

Time to value: the compounding advantage (and the incentive to delay it)

The timeline difference between custom engineering and a platform approach compounds over time. But the timeline difference is not just about methodology; it reflects incentive structures. A time-based provider has no financial reason to compress timelines. Every month of requirements gathering, architecture review, and sprint planning is another month of revenue. The longer the "time to value," the more the provider earns.

Consider a 12-month window. With a typical custom engineering engagement:

• Months 1-2: Requirements gathering, architecture design, environment setup
• Months 3-6: Development sprints, integration work, iteration
• Months 7-8: QA, user acceptance testing, stabilization
• Months 9-10: Deployment and production readiness
• Months 11-12: First agents in production, beginning to generate value

With Nexus:

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

By the time a custom engineering project 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 custom build would still have been in the development phase. The outsourcing firm at a Nexus client that spent 12 months planning a knowledge assistant was earning revenue the entire time, while the client received zero production value. With Nexus, the same outcome took 4 weeks.

Total cost: day rates vs. per-agent pricing (and who benefits from higher costs)

Endava's nearshore rates are more competitive than onshore consulting firms like Accenture or McKinsey. That is a genuine advantage of the nearshore model for custom engineering projects. But cheaper hours are still hours. The structural economics still apply: costs scale linearly with team size and project duration, and the provider earns more when both are larger. The nearshore model offers lower rates, but the fundamental incentive misalignment is the same as any time-based service.

A mid-sized engagement (6 nearshore engineers, 6 months) represents a significant investment before any agent reaches production. Scaling to additional departments or workflows means extending the team or launching new projects with similar cost profiles. Each extension is additional revenue for the provider, regardless of whether the extended timeline was necessary.

Nexus pricing is per-agent, tied to value delivered. You do not pay for FDEs by the day. 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 custom engineering project would cost for the same scope. And the incentive alignment is clear: Nexus earns when agents deliver value, not when projects take longer.

This does not mean Endava is overpriced. Their day rates reflect the quality of their engineering talent and the operational overhead of running delivery centers across multiple countries. But if the primary need is AI agents on business workflows, the per-agent cost model delivers more value per dollar than paying engineers by the day to build something custom. More importantly, the per-agent model aligns the vendor's incentive with yours: faster delivery, not longer engagements.

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Frequently asked questions

Can we use Endava for custom engineering and Nexus for agent deployment?

Yes. Some enterprises use nearshore engineering firms for custom software development (product builds, platform engineering, legacy modernization) and Nexus specifically for deploying AI agents on business workflows. The two solve different problems, and the incentive question is less relevant for genuine custom software work where sustained engineering effort is appropriate. Endava can build your custom applications. Nexus can handle the agent layer that automates your business workflows. The agents integrate with whatever systems are in place, through 4,000+ native integrations.

We already work with Endava. Can Nexus complement that relationship?

Absolutely. If Endava is handling custom software development, platform engineering, or system integration, Nexus can run in parallel to deploy AI agents on specific business workflows. This is common: enterprises keep their engineering partners for product and infrastructure work while using Nexus for the operational AI agent layer. The two are complementary, not competitive.

Endava has Dava.Flow and is investing in AI. Does that close the gap?

Endava's AI investments are real. Programme Keystone, Dava.Flow, and the Cognition partnership show they are serious about embedding AI into their delivery model. But the fundamental model has not changed: Endava assigns engineering teams to build custom solutions for your specific requirements, billed by the day. That process takes months, creates a codebase your team needs to maintain, and requires ongoing developer involvement to evolve. Dava.Flow improves how Endava delivers custom engineering. It does not change the underlying revenue model: time-based billing where longer engagements mean more revenue. Nexus is a platform purpose-built for AI agent deployment, where business teams own and iterate on agents directly without engineering dependency, and where the vendor is incentivized to deliver results quickly rather than extend timelines.

Endava's nearshore rates are lower than big consulting firms. Does Nexus still win on cost?

Endava's rates are genuinely competitive compared to onshore consulting firms. For custom software development projects, their nearshore model offers strong value. But cheaper hours are still hours. The nearshore model reduces the rate per day while preserving the same structural incentive: the provider earns more when projects take longer. For AI agent deployment specifically, the comparison is not Endava's day rates versus Nexus's day rates. It is the total cost of a custom engineering engagement (team of engineers over months, with the provider earning more the longer it takes) versus per-agent pricing (agents in production in weeks, with the vendor incentivized to deliver fast). Even at nearshore rates, a 6-month custom build typically costs more than deploying multiple agents through Nexus, and takes significantly longer to deliver value.

Is Nexus too small compared to Endava's 11,000+ 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.

Endava has strong engineering talent. Why would we choose Nexus instead?

Having strong engineering talent available does not mean it should be applied to every problem. Lambda has world-class AI engineers and chose to buy instead of build, because the opportunity cost of engineering time was too high. The question is whether deploying AI agents on business workflows is a good use of custom engineering capacity (at any rate), or whether a purpose-built platform with embedded expertise delivers the same outcome faster, at lower cost, and with greater business ownership. There is also the incentive question: strong engineers working under a time-based billing model still operate within a structure where the provider profits from longer engagements. The engineers may want to deliver quickly, but the business model does not reward it. For most enterprise AI agent use cases, custom engineering is more capability than you need, more dependency than you want, and a misaligned incentive structure for the problem you are solving.

What does the 3-month POC look like?

Every Nexus engagement starts with a 3-month proof of concept tied to specific, measurable outcomes defined upfront. Most agents are in production within the first 2-6 weeks. A Forward Deployed Engineer is embedded with your team for the entire period. You see the results, measure the impact, and decide whether to continue. You can exit anytime. This is why our POC-to-contract conversion rate is 100%: we do not move forward unless the value is clear.

What if we need both custom software and AI agents?

This is common. Custom software development and AI agent deployment are different workstreams. You can keep Endava (or any engineering partner) for custom product builds, platform engineering, and infrastructure work, while Nexus handles the AI agent layer on business workflows. Lambda runs exactly this way: their engineering team focuses on their core AI infrastructure product, while business teams own operational agents through Nexus.

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Worth exploring?

If your team has been evaluating custom engineering firms for AI agent deployment and weighing the timeline, cost, and dependency implications, it might be worth asking a different question: is the provider structurally incentivized to deliver results quickly, or to bill days?

Orange, a 120,000+ employee telecom operator with the budget for any custom engineering engagement, chose a platform approach. Business teams deployed in 4 weeks. $4M+ in incremental yearly revenue. 100% adoption. No day rates, no billable hours, no incentive misalignment.

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.

At another enterprise client, an outsourcing firm spent an entire year planning a knowledge assistant. Nexus came in and delivered it in 4 weeks. Same problem. Same complexity. Different incentive structure.

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 day. You see results before committing. You can exit anytime.

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

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