Nexus vs ML6: Enterprise Platform vs AI Boutique
ML6 has talented AI engineers and a strong Benelux track record. But boutique consultancies bill for time, not outcomes. Nexus pays for results. Full comparison.
Last updated: February 2026
Quick honest summary
ML6 is one of the strongest AI engineering consultancies in Europe. Founded in Ghent in 2013, they have grown to 100+ AI experts across Ghent, Amsterdam, Berlin, and Munich. They are a Google Cloud Services Partner of the Year for Benelux (2024), an OpenAI Services Partner, and trusted by enterprises like Randstad, ASML, Pfizer, and P&G. They have delivered 400+ AI projects across 150+ organizations, earned four consecutive Deloitte Fast 50 awards, and landed on the FT1000 list of fastest-growing European companies. ML6 is not a generalist IT shop. They are specialists, and they have earned their reputation.
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 on engineering or external consultants.
The core question is not about talent. ML6 has excellent talent. The question is about the business model and the incentives it creates.
ML6 operates as a services company. They scope a project, assign a team, build a custom solution, and hand it off. Their revenue comes from day rates and project fees; the longer an engagement runs, the more they earn. This is not a criticism of ML6 specifically. It is how every services firm works, from Big 4 to boutiques. The structural incentive is to bill hours, not to minimize time-to-value. ML6 is smaller and more agile than a Big 4 firm, which typically makes them faster. But the underlying incentive is the same: the firm profits from effort, not outcomes.
For well-defined, specialized ML challenges (computer vision, predictive models, MLOps pipelines) where the output is a model or system your team can maintain, this model works. ML6 is a genuinely strong choice for those projects, especially if you want a local partner in the Benelux region with deep Google Cloud expertise.
Where the model breaks down is in enterprise AI agents for business workflows. These are not one-time builds. They are living systems that need to evolve with the business, be owned by business teams (not engineers), and scale from one agent to many across departments. A time-based billing model rewards longer engagements, not faster delivery. For agents, you need a platform your teams can own and operate, with embedded engineering support that stays with you. That is what Nexus provides.
Side-by-side comparison
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When ML6 is the better choice
ML6 has genuine strengths, and there are scenarios where the time-based services model is appropriate. When the problem is well-defined, the scope is bounded, and the deliverable is a discrete model or system, the structural incentive question matters less because the engagement has a natural endpoint.
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You need a custom ML model for a specialized problem. Computer vision for manufacturing quality control. Predictive maintenance models. Custom NLP for industry-specific language. These are ML engineering challenges where off-the-shelf solutions do not work and deep model expertise matters. ML6 has delivered 400+ AI projects across these domains.
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Your project is deeply Google Cloud-native. ML6 has been a Google Cloud partner for over a decade. They won Benelux Services Partner of the Year in 2024. If your AI initiative is tightly coupled with Google Cloud infrastructure (Vertex AI, BigQuery, GKE), their depth of experience in that ecosystem is hard to match.
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You are a Benelux enterprise that values a local partner. ML6 has offices in Ghent and Amsterdam. They understand the regulatory landscape, speak the language, and can be on-site. For organizations that prioritize geographic proximity and cultural alignment with their technology partners, this matters.
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You have a unique data science challenge that requires bespoke model development. ML6 helped ASML analyze calibration data from photolithography machines, shortening release cycles from monthly to biweekly. They helped Randstad build predictive sales tools that raised hit rates from 25% to 70%. These are specialized, data-intensive problems where custom model development is the right approach.
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You need team augmentation for a specific AI initiative. If your team has the architecture but needs additional senior ML engineers for a defined period, ML6 can embed their people to accelerate delivery. Just define the scope tightly upfront; open-ended augmentation engagements are where the time-based billing model tends to expand.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they need AI agents for business workflows, they need speed, and they need their own teams to own the outcome long-term. They also recognize that paying for time spent is not the same as paying for results delivered.
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You need business teams to own and operate the agents, not file change requests with an external consultancy. With ML6, modifications to what was built typically require re-engaging their team, scoping the change, and waiting for availability. Each re-engagement is a new set of billable days. With Nexus, the business teams who understand the workflows own and iterate on the agents directly. When Lambda's Head of Sales Intelligence needed to adjust data sources or account segmentation, he did it himself. No external dependency. No waiting. No new invoice.
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You need production agents in weeks, not months. Custom builds from consultancies follow a predictable timeline: scoping (2-4 weeks), data preparation (2-4 weeks), development (4-12 weeks), testing and integration (2-4 weeks), deployment (1-2 weeks). For a straightforward project, that is 3-6 months. For complex ones, longer. There is no structural incentive to compress these timelines; longer projects mean more billable days. One enterprise had an outsourcing firm spend a full year in "project management mode," only finalizing planning for a first knowledge assistant. Nexus came in: 4 weeks to scrape, implement, and push to production. Orange deployed customer onboarding agents across multiple European markets in the same timeframe. ML6 is typically faster than Big 4 firms, but the billing model still rewards duration over speed.
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You want to scale from one agent to many without starting from scratch each time. With a services model, each new use case is a new project. New scoping, new timeline, new budget. The cost and timeline scale linearly, and each new project generates more billable days for the firm. With Nexus, each new agent builds on the platform foundation. Lambda went from one agent to a fleet across sales and marketing, with each new agent deploying in days. The platform compounds; custom builds do not.
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You do not want to maintain what was built after the consultancy leaves. This is the hidden cost of custom builds. ML6 delivers the solution and moves on. Your team inherits the maintenance: model drift, integration updates, infrastructure changes, framework version updates. Unless you re-engage ML6 (at additional cost, billed by the day), your internal team needs the expertise to keep it running. The maintenance phase is also where "complexity inflation" can emerge: solutions built with more architectural complexity than necessary require more ongoing support, which generates more billable work. With Nexus, the platform handles infrastructure, integrations, and ongoing optimization. Agents adapt to system changes without requiring rebuilds.
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You want enterprise governance without building it yourself. ML6 can implement security measures into custom solutions, but enterprise compliance frameworks (SOC 2, ISO 27001, GDPR audit trails, decision traceability) require significant additional engineering, all billed at day rates. Nexus ships enterprise governance from day one: SOC 2 Type II, ISO 27001, ISO 42001, GDPR, full audit trails, and role-based access controls. No additional billable work required.
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You need more than software or more than a consultancy. You need both, without the misaligned incentives of either. This is the realization at the center of the build vs buy decision. Most consultancies build and leave. Most software vendors sell and disappear. Nexus embeds Forward Deployed Engineers with your team. Unlike consultants whose firms profit from longer engagements, FDEs are incentivized to deliver results quickly. They help identify the highest-impact use cases, design agents that fit your specific reality, handle integration complexity, manage organizational change, and optimize continuously. Deploying AI at scale is 10% technology and 90% organizational change. That is what FDEs are built for.
What enterprises experienced
The 1-year vs 4-weeks story
Before discussing specific case studies, one story illustrates the structural incentive problem clearly.
An outsourcing firm at a Nexus client spent a full year in "project management mode" on a knowledge assistant project. Twelve months of scoping, stakeholder alignment, architecture reviews, and planning documents. After one year, they had finalized planning for the first assistant. Not built it. Planned it.
Nexus came in. Four weeks to scrape the knowledge base, implement the agent, and push it to production. Live, working, delivering value.
The outsourcing firm had talented people. But the business model created no incentive to move faster. Every week of planning was a week of billable work. ML6 is smaller and more agile than a large outsourcing firm, which typically means faster delivery. But the structural incentive is the same: time-based billing rewards longer engagements.
Orange: 4 weeks to production, $4M+ incremental revenue
Orange Group is a multi-billion euro telecom operator with 120,000+ employees. They have internal engineering resources. They have the budget to hire any consultancy. Rather than spending months on a custom build, Orange deployed 120 AI agents with Nexus.
Their business team (not engineering) built customer onboarding agents and deployed them across multiple European markets in 4 weeks. The result: 50% conversion improvement, $4M+ incremental yearly revenue, 100% adoption, and 100% compliance. Business teams own the agents. No external dependency.
The timeline matters. A custom build from a consultancy, billed by the day, would have taken months of scoping, development, testing, and integration. There is no structural incentive to compress that timeline. Orange had agents in production before most consultancy projects finish the scoping phase.
Lambda: a $4B+ AI company chose to buy instead of build
Lambda is a $4B+ AI infrastructure company with world-class engineers. If any company could build custom AI solutions internally or manage a consultancy engagement, it was Lambda.
Lambda, a $4B+ AI company, chose a platform approach over building with boutique consultants or in-house engineering. Not because they lacked engineering talent, but because they understood the difference between paying for time and paying for outcomes. Joaquin Paz, Lambda's Head of Sales Intelligence (not an engineer), built an autonomous research agent that monitors 12,000+ enterprise accounts, identifies buying signals, and synthesizes competitive intelligence. He built it in days.
The results: $4B+ in cumulative pipeline identified, 24,000+ research hours added annually (equivalent to 12 full-time analysts), deployed in weeks. Lambda has since expanded to an agent fleet across sales and marketing. 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
European telecom operator: 40% support capacity freed
A multi-billion euro European telecom operator with 13,000+ employees deployed multi-purpose agents for customer support, compliance, and registration. Results: 40% of support capacity freed, 100% compliance assurance, 12-week deployment, millions of customer interactions handled. A consultancy engagement scoped for the same outcome would have been billed by the day across a much longer timeline, with no structural incentive to deliver in 12 weeks.
Key differences explained
Platform vs. services: different models, different incentives
This is the fundamental distinction, and it goes beyond delivery methodology. It is about who profits from what.
ML6 operates a services model. Their revenue comes from billable days and project fees. They bring talented engineers to your problem, build a custom solution, and deliver it. The quality of what they build depends on their talent (which is strong). But the business model itself creates a structural misalignment: the firm earns more when projects take longer, and the client pays for effort rather than outcomes. This is not unique to ML6. It is the economics of every services firm, from Big 4 to boutiques. ML6, being smaller and more specialized, is typically faster than large consultancies. But faster is relative when the incentive structure still rewards duration.
Ongoing dependency. When the business changes (and it always does), you need ML6 back. New data sources, new workflows, changing business logic, expanding to new departments. Each change is a new engagement, a new timeline, a new set of billable days. The firm is structurally incentivized to be needed again.
Ownership gap. The people who built it (ML6 engineers) leave. The people who need to use it (your business teams) did not build it and may not understand it deeply enough to modify it. Knowledge transfers help, but they rarely capture everything. And when the gap becomes a problem, the solution is another billable engagement.
Linear scaling. Your fifth agent takes roughly as long and costs roughly as much as your first. There is no compounding advantage. Each new use case starts from scratch, generating a new stream of billable work.
Nexus operates a platform + service model with a fundamentally different incentive structure. You pay per agent, tied to value delivered, not per day of engineering time. Nexus is incentivized to get agents into production quickly, because that is when value (and revenue) begins. Forward Deployed Engineers provide the expertise, use case identification, and organizational change management. Business teams build and own the agents. The FDEs make sure they succeed.
The result: your first agent goes live in weeks. Your fifth agent goes live in days. Business teams iterate without external dependencies. The platform compounds. The service ensures you capture value.
The real cost of custom builds (and why it keeps growing)
ML6's day rates reflect the quality of their talent. Senior AI engineers at European consultancy rates are not inexpensive. But the visible cost (the invoice) is only part of the picture. And the structure of time-based billing means the total cost has a tendency to grow.
The hidden costs of custom builds include:
- Scoping and alignment (weeks before any building starts, all billable)
- Data preparation and pipeline development (often the longest phase, and the least visible to the client)
- Model development, testing, and iteration (the core build)
- Integration with enterprise systems (where most delays happen, and where "complexity inflation" can emerge: architectural decisions that increase the technical footprint and, consequently, future billable work)
- Knowledge transfer and documentation (often rushed at the end)
- Ongoing maintenance after the consultancy disengages (your team's burden, or a new billable engagement)
- Re-engagement costs when the business changes and the solution needs updating (each re-engagement billed at day rates)
"Complexity inflation" deserves a closer look. It is the tendency for custom solutions to be built with more architectural complexity than the business problem requires. Larger firms are more prone to this, but even boutiques face the structural incentive: more complex solutions require more billable work to maintain and evolve. ML6 is less likely to inflate complexity than a Big 4 firm, but the incentive exists in any time-based model.
For a single, well-defined ML model, these costs may be justified. For enterprise AI agents that need to evolve continuously, span multiple use cases, and be owned by business teams, the math often does not work. The total cost of a custom-built agent fleet (scoping, building, maintaining, and updating each one individually) frequently exceeds the cost of a platform approach, while taking 3-5x longer. And with time-based billing, every additional week is revenue for the firm, not value for the client.
Forward Deployed Engineers: aligned incentives, not billable hours
This is the central difference between Nexus and both consultancies and pure software vendors. And it is fundamentally about incentive alignment.
Most consultancies (including ML6) send engineers who build for you and then leave. Their firm earns revenue from billable days; the longer they are engaged, the more the firm earns. Most software vendors sell a product and expect you to figure it out. Nexus does neither.
Forward Deployed Engineers are not consultants billing by the day. They are real engineers embedded with your team, and Nexus does not charge for their time. You pay for agents delivering value. FDEs are incentivized to get agents into production as quickly as possible, because that is when results begin. They:
- Identify the highest-impact use cases first. Not guessing based on templates. Analyzing your specific operations to find where agents deliver the most value.
- Design agents that fit your reality. Not generic configurations. 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.
- Manage organizational change. Deploying AI at scale is 10% technology and 90% organizational change. FDEs help frame the change, train teams on new workflows, build confidence through small wins, and address concerns about transparency and control.
- Stay with you. Unlike consultants who deliver and leave, FDEs remain embedded throughout the engagement. They optimize continuously, help scale agents to new teams, and ensure the value compounds over time.
This is why Nexus converts 100% of POCs to annual contracts. The engagement is structured to deliver measurable value before you commit.
Maintenance and evolution: what happens after go-live
With ML6, go-live is the beginning of a new phase: your team's phase. They inherit the solution and become responsible for keeping it running, adapting it to business changes, monitoring for model drift, updating integrations, and troubleshooting issues. If the solution was well-built and well-documented, this can work. But it requires internal ML expertise that many business teams do not have. And when it does not work, the solution is to re-engage ML6, generating a new stream of billable days. The post-delivery phase is where the structural incentive misalignment becomes most visible: the firm profits from solutions that require ongoing re-engagement.
With Nexus, the platform handles the infrastructure layer. Agents adapt to system changes without requiring rebuilds. New data sources integrate without starting over. Forward Deployed Engineers help analyze agent performance, refine escalation logic, and scale to new teams and processes. The business team focuses on outcomes, not maintenance. There is no billable re-engagement model because the platform is designed to eliminate the need for one.
Lambda described this difference directly: "We're not maintaining automation. The agent adapts as we grow."
Frequently asked questions
We have a good relationship with ML6. Can we use both?
Yes. Several enterprises use consultancies for specialized ML challenges (custom models, predictive analytics, computer vision) and Nexus for AI agents that automate business workflows. These solve different problems, and the structural incentive question matters less for bounded, well-defined ML projects with clear endpoints. ML6 is strong at building custom models for unique technical challenges. Nexus is built for agents that complete enterprise workflows autonomously, owned by business teams, scaling across departments. The two can coexist without conflict. Where the distinction matters is for ongoing, evolving agent workflows: that is where time-based billing creates misalignment.
ML6 is building an "Enterprise Superintelligence" platform. Does that compete with Nexus directly?
ML6 has announced Unum, their Enterprise Superintelligence platform. This represents ML6's evolution from pure services toward a platform model, which is itself an acknowledgment that the services-only model has structural limitations for enterprise AI at scale. It is still early, and the details of how it compares to Nexus's production-ready platform (4,000+ integrations, SOC 2 Type II, ISO 27001, 100% POC-to-contract conversion rate across enterprise deployments) will become clearer as it matures. The key question will be whether Unum shifts ML6's revenue model from time-based billing to outcome-based pricing, or whether the platform becomes another vehicle for billable services work. Nexus has been operating as a platform + service with outcome-aligned pricing from the start, with enterprise customers in production. Orange, Lambda, and others are running agents on Nexus today, at scale, with measurable financial outcomes.
ML6 has 400+ projects delivered. How does Nexus compare on track record?
ML6's track record is impressive and spans a wide range of ML and AI applications over more than a decade. Those 400+ projects were each billed as separate engagements, which is the nature of a services business. Nexus is focused specifically on enterprise AI agents for business workflows. Our proof points include named references (Orange Group, Lambda.ai) with documented financial outcomes ($4M+ incremental revenue, $4B+ pipeline identified, $7M+ projected value) and a 100% POC-to-contract conversion rate. The comparison is depth in a specific domain (autonomous enterprise agents) versus breadth across ML applications. One metric worth noting: Nexus measures success by outcomes delivered, not by use cases billed.
ML6 is local to the Benelux. Does Nexus have a European presence?
Nexus is headquartered in Brussels with an office in San Francisco. The founding team has deep European enterprise experience (CEO is ex-McKinsey, co-founder ran an AI development agency in Europe). Nexus serves enterprises across Europe and the US, with Forward Deployed Engineers who work embedded with your team regardless of location.
How does pricing compare?
ML6 typically charges day rates for senior AI engineers or project-based fees. For a multi-month custom build, costs scale with scope, team size, and duration. The structural reality: ML6 earns more when projects take longer. This is not a commentary on their intentions; it is the math of time-based billing. Nexus charges per-agent pricing tied to value delivered, starting with a 3-month POC tied to measurable outcomes. You do not pay for FDEs or billable hours. You see results before committing to an annual contract. The pricing models reflect fundamentally different incentive structures: ML6 charges for engineering time spent (more time equals more revenue for the firm); Nexus charges for agents delivering value (faster delivery means faster value for both sides).
What if we already have a custom solution built by ML6 (or another consultancy)?
The investment is not wasted. Custom-built solutions may continue to serve their specific purpose. But if maintaining and evolving that solution has become a burden (and the only option is re-engaging the consultancy at day rates), or if you need to scale AI agents across multiple business workflows, Nexus can handle that layer while the existing solution remains in place. Many enterprises find that their first custom build teaches them what they actually need, and that a platform approach is more sustainable for the second, third, and fourth use cases. It also breaks the cycle of billable re-engagement that time-based models create.
What does the 3-month POC look like?
Every 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.
Worth exploring?
If your team has been working with consultancies for AI initiatives and questioning whether the model scales (custom builds that take months, solutions that need re-engagement for every change, maintenance that falls on your team after handoff, and a billing model where the firm profits from longer timelines), it might be worth seeing how enterprises like Orange and Lambda approached the same decision.
Orange had internal resources and every option available. They deployed in 4 weeks. Lambda had world-class AI engineers. They chose to buy. Both recognized that paying for outcomes is fundamentally different from paying for time. Both have business teams owning the result without external dependency.
Every Nexus engagement starts with a 3-month proof of concept tied to specific outcomes. Forward Deployed Engineers work alongside your team from day one. You see results before committing. You can exit anytime.
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Every engagement starts with a 3-month proof of concept tied to specific, measurable business outcomes. Forward Deployed Engineers embed with your team from day one.