Nexus vs Artefact: Platform vs Data & AI Consulting

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Quick honest summary

Artefact is a global data and AI consulting company founded in Paris in 2014 by three Ecole Polytechnique alumni. With 1,700+ employees across 31 offices in 25 countries, Artefact is one of the most established data-specialized consultancies in the world. They work with major brands (Samsung, L'Oreal, Orange, Sanofi, Carrefour) and offer end-to-end services spanning data strategy, data engineering, AI model deployment, digital marketing analytics, and organizational transformation. In 2025, Cinven acquired a majority stake in a deal valuing Artefact at over 1 billion euros, with ambitions to triple the business to 5,000+ staff by 2030. They are a Google Cloud Premier Partner and EMEA AI Partner of the Year. Their data science capabilities are genuine, and their AI specialization runs deeper than what generalist consulting firms typically offer.

The honest tension is structural, not about talent. Artefact's revenue model is time-based: day rates, project phases, ongoing retainers. The longer something takes, the more the firm earns. Even with the best intentions and real expertise, this creates an incentive misalignment. The client pays for effort and consultant hours; the firm profits when projects expand in scope and duration. This is not unique to Artefact. It is the fundamental economics of every consulting and outsourcing model.

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 configure on your own. Nexus is built for enterprises that need AI agents completing business workflows in production, with business teams owning the outcome. The pricing model is tied to outcomes, not hours. Nexus is incentivized to deliver results quickly, because you do not pay for FDEs, you pay for agents that work.

The right choice depends on what you are solving for.

If you need a data strategy overhaul, custom ML model development, data infrastructure modernization, or deep analytical capabilities built from scratch by specialized consultants, Artefact brings genuine expertise and a strong track record. They understand the data layer deeply. Just go in with clear scope boundaries and milestone-based checkpoints, because the billing model will not naturally constrain timelines for you.

If the goal is AI agents completing enterprise workflows in production (sales operations, customer support, HR, marketing), deployed in weeks rather than months, with your business teams owning and iterating on the agents, that is where Nexus fits. Nexus is a platform plus embedded engineering support. You get agents in production fast, with Forward Deployed Engineers handling the complexity, and your team owns the result when the engagement matures. No ongoing consultant dependency. No day rates that expand with scope. No incentive for the vendor to slow down.

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Side-by-side comparison

Dimension	Artefact	Nexus
What it is	Global data and AI consulting firm 1,700+ employees across 25 countries End-to-end: data strategy to AI deployment Marketing analytics included	Enterprise AI agent platform + embedded service Forward Deployed Engineers on your team Change management support Ongoing optimization included
Who builds and owns it	Artefact consultants design, build, and deliver Your team receives the output Typically depends on Artefact for iteration Modifications and scaling require re-engagement (and additional billing)	Business teams build and deploy agents with FDE support They own the outcome from day one No permanent consultant dependency No re-engagement fees for changes
Delivery model	Project-based engagements with day rates Typical timelines: 3-12 months Ongoing retainers common for continued support Revenue scales with duration: longer projects earn more	3-month POC tied to measurable business outcomes Most agents in production within 2-6 weeks Per-agent pricing tied to value delivered Nexus earns when agents work, not when projects drag
Time to production	Custom AI builds take 6-18 months Requirements gathering, data engineering, model development, testing, integration Each phase generates billable hours At one Nexus client, an outsourcing firm spent 1 year in "project management mode" before a single agent reached production	Days to weeks FDEs handle configuration and integration Testing and deployment included Nexus delivered in 4 weeks at the same client
What gets built	Custom ML models, data pipelines Recommendation engines, NLP systems Computer vision, marketing attribution Data platforms, tailored to project scope	Autonomous AI agents completing workflows end-to-end Sales ops, support, HR, marketing Agents adapt to exceptions, escalate intelligently Integrate with 4,000+ systems
Handles exceptions?	Handles what solutions were designed for Scope changes need additional engagement (and additional billing) New edge cases require consulting hours Shifting requirements add cost and delay; the firm benefits from this structurally	Agents adapt or escalate with full context No silent failures No need to re-engage consultants for changes No additional cost when requirements evolve
Ownership after delivery	Artefact delivers the solution Modifications often require re-engaging consultants Scaling needs their involvement Knowledge transfer varies by engagement	Business teams own and iterate on agents directly Lambda's Head of Sales adjusted data sources himself No consultant tickets, no backlog
Pricing model	Day rates: $1,000-$2,500/day per consultant Project-based pricing for defined scopes Retainer models for ongoing support Incentive structure: more days billed = more revenue for the firm	Per-agent pricing tied to value delivered 3-month POC before annual commitment No day rates, no billable hours Incentive structure: faster delivery = faster value for both sides
Security and compliance	Depends on engagement scope Artefact builds within your infrastructure Compliance certifications are your responsibility	SOC 2 Type II, ISO 27001, ISO 42001, GDPR Full audit trails, decision traceability Role-based access from day one
Support model	Dedicated project team for engagement duration Post-delivery support depends on contract terms Long-term optimization requires ongoing retainer (additional revenue for the firm)	FDEs embedded with your team Change management guidance Ongoing optimization included, not billed separately White-glove partnership from day one through scaling
Best for	Data strategy transformation Custom ML models Data infrastructure modernization Deep analytical capabilities not yet built	Production agents completing enterprise workflows Engineering-grade support included No ongoing consulting dependency

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When Artefact is the better choice

Artefact is a serious consultancy with genuine strengths, and there are scenarios where they are the right partner. The structural incentive point above does not negate their capabilities; it means you should scope engagements tightly and insist on milestone-based deliverables rather than open-ended timelines.

• You need a data strategy and organizational transformation. If your enterprise does not have a coherent data strategy, if your data infrastructure is fragmented, or if you need help designing data governance frameworks, Artefact's consulting practice is built for this. They combine data strategy with organizational change management, helping companies define how data should flow, who owns it, and how decisions get made. This is foundational work that a platform cannot replace. Be aware that data strategy, data governance, and data quality assessments can expand into multi-quarter engagements; define exit criteria upfront.

• You need custom ML models for specific analytical problems. Trend detection for L'Oreal. Sales forecasting algorithms for Carrefour. Computer vision for quality control. If the problem requires building a custom machine learning model from scratch, trained on your data, for a specific analytical or prediction task, Artefact has the data science talent to deliver. Their AI & GenAI Factory offering is designed for exactly this: moving from proof of concept to production ML systems.

• You need deep data engineering and infrastructure work. Migrating to a cloud data platform, building data lakes, implementing data quality frameworks, connecting disparate systems at the data layer. Artefact's data engineering capabilities are substantial, and this type of infrastructure work requires hands-on consulting. Just structure the engagement around deliverables with fixed timelines, not open-ended phases.

• Your primary challenge is digital marketing analytics and ROI optimization. Artefact's roots include digital marketing (the company merged with NetBooster, a digital marketing group, in 2017, and the combined entity adopted the Artefact name in 2018). They bring strong capabilities in marketing attribution, customer data platforms, and media optimization that are distinct from what an agent platform addresses.

• You are early in your data maturity journey. If your organization has not yet established the data foundations needed for AI (clean data pipelines, governance, a centralized data platform), Artefact can help you build that foundation. Without solid data infrastructure, deploying AI agents or any other AI solution will underdeliver. The risk here is "complexity inflation": data quality assessments, governance frameworks, and extensive preparation phases can become self-perpetuating engagements. That is not always intentional, but the billing model does not discourage it.

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When Nexus is the better choice

Enterprises that partner with Nexus tend to share a specific pattern: they have already invested in data and AI initiatives (sometimes with firms like Artefact), and now need AI that completes real business work in production, quickly, with business team ownership. Often, they have experienced the structural slowness of consulting engagements firsthand.

• You need AI agents in production in weeks, not quarters. Custom consulting engagements follow a familiar arc: scoping (2-4 weeks), requirements (2-4 weeks), development (8-24 weeks), testing (2-4 weeks), deployment (2-4 weeks), optimization (ongoing). Each phase generates billable hours. The structure does not reward speed. With Nexus, most enterprise agents go live within 2-6 weeks. Orange deployed customer onboarding agents across multiple European markets in 4 weeks. A real example: at one Nexus client, an outsourcing firm spent a full year in "project management mode," only finalizing planning for a first knowledge assistant. Nexus came in, scraped the data, implemented the agent, and pushed it to production in 4 weeks. The difference is not marginal; it is structural.

• You want your business teams to own the AI, not depend on external consultants. With a consulting model, every modification, expansion, or optimization typically requires re-engaging the consultancy. That creates an ongoing dependency and a revenue stream for the firm: your team files a request, waits for consultant availability, pays for additional hours, and receives the update weeks later. The consultancy is not incentivized to make you self-sufficient, because your independence ends their revenue. With Nexus, business teams own and iterate on agents directly. Lambda's Head of Sales Intelligence, Joaquin Paz, built an autonomous research agent monitoring 12,000+ accounts, adjusted data sources and account segmentation himself, and expanded to new use cases. He is not an engineer.

• You want predictable pricing, not day rates that scale with scope. This is a key factor in the build vs buy equation. Consulting day rates compound quickly. A team of 3-5 consultants working for 6 months at market rates adds up fast. And scope changes (which always happen) mean additional cost, which the firm has no structural incentive to prevent. Nexus uses per-agent pricing tied to the value delivered. The cost model is transparent and predictable. Nexus earns when agents deliver results, not when projects take longer.

• Your challenge is deploying AI across business workflows, not building custom ML models. If the problem is: "How do we automate customer onboarding, sales research, proposal generation, support triage, or HR coordination?", you do not need a consulting engagement to build something from scratch. Nor do you need to build with developer frameworks that require months of engineering. You need agents that integrate with your existing systems (CRM, ERP, Slack, Teams, email, WhatsApp) and complete work end-to-end. Nexus connects to 4,000+ enterprise systems natively. Consulting firms sometimes frame these workflow problems as requiring extensive data preparation, governance frameworks, and custom builds; that is complexity inflation that generates billable hours without accelerating time to production.

• You need enterprise governance from day one. Nexus ships with SOC 2 Type II, ISO 27001, ISO 42001, GDPR compliance, full audit trails, and decision traceability built in. With a consulting engagement, compliance and security certifications are typically your responsibility to implement on top of whatever gets built, often through additional phases and additional billing.

• You need more than software, and more than consultants. You need a partner with aligned incentives. Nexus embeds Forward Deployed Engineers with your team. FDEs are real engineers who identify the highest-impact use cases, design agents tailored to your workflows, handle integration complexity, manage organizational change, and optimize continuously. You do not pay for FDEs by the hour. This is not consultants delivering a project and looking for the next billable phase. It is an embedded partnership where Nexus is incentivized to deliver results fast, because that is how both sides win.

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What enterprises experienced

Orange: a multi-billion euro telecom deployed in 4 weeks

Orange Group is a multi-billion euro telecom operator with 120,000+ employees across Europe and Africa. They have substantial internal engineering resources and the budget to engage any consultancy or build anything internally. They had every option available, including firms that would happily scope a 6-to-12-month engagement with a team of consultants billing daily.

Where consulting data projects typically begin with months of data strategy and governance, Orange's business team went straight to production. They built customer onboarding agents using the Nexus platform, deployed across multiple European markets in 4 weeks. No scoping phase. No requirements gathering phase. No data governance workstream. Production in 4 weeks.

The results:

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

When the agent can confidently approve, it approves. When uncertain, it escalates to the salesperson with full context. Every step visible, every decision logged. Governance woven into the workflow itself.

Lambda: a $4B+ AI company chose to buy instead of build

Lambda is a $4B+ AI cloud infrastructure company with world-class engineers who build supercomputers for a living. If any company could build custom AI solutions internally, or hire the best data consultants to do it, it was Lambda. They could have engaged Artefact or any top-tier AI consultancy and paid day rates for a team of data scientists to build a custom solution over 6 to 12 months.

Lambda, a $4B+ AI company, chose a platform approach over engaging data consultants or building in-house. The reason is instructive.

What they tried first: Lambda explored open-ended AI agents (like ChatGPT Deep Search) and traditional workflow automation. Open-ended agents were intelligent but inconsistent. Workflow automation was reliable but rigid. Neither worked for enterprise-grade sales intelligence. A consulting engagement would have meant months of scoping and building before Lambda could evaluate if the approach even worked.

What they built with Nexus: Joaquin Paz, Lambda's Head of Sales Intelligence, built an autonomous research agent that monitors 12,000+ enterprise accounts annually, identifies buying signals across dozens of data sources, and synthesizes competitive intelligence. Joaquin is not an engineer. He built this in days. No consultants. No day rates. No waiting for a project team to be assembled and staffed.

The results:

• $4B+ in cumulative pipeline identified
• 24,000+ research hours added annually (equivalent to 12 full-time analysts)
• 12,000+ enterprise accounts analyzed with deep intelligence
• Deployed in weeks, not the months a consulting engagement would require

Lambda has since expanded from one agent to a fleet across sales and marketing. Anticipated value: more than $7M by 2026. The expansion happened because business teams own the agents and can iterate without re-engaging an external firm.

"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

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Key differences explained

Services model vs. platform + service model: fundamentally different incentives

This is the core distinction, and it matters more than any feature comparison. It is about structural incentive alignment.

Artefact operates a consulting services model. You engage a team of consultants (data scientists, engineers, strategists) who scope your problem, design a solution, build it, and deliver it. The value is real, but the economics create a misalignment that even the best-intentioned firm cannot escape: the more complex the project, the more consultants you need, the longer the timeline, and the higher the revenue for the firm. Scope changes (which always happen in enterprise AI projects) mean additional hours and additional budget. After delivery, ongoing optimization and iteration typically require re-engaging the same team, generating more billable days. The firm profits when projects take longer. The client pays for effort, not outcomes.

This is not a criticism of Artefact's talent or intentions. It is a structural observation about every time-based billing model. The incentives are misaligned by design.

Nexus operates a platform + service model. The platform handles agent creation, integrations, deployment, security, and compliance. Forward Deployed Engineers handle the complexity of identifying use cases, designing agents, managing integrations, driving organizational change, and optimizing over time. But the business team owns the agents. They iterate directly. They do not need to re-engage an external team for every change. Critically, you do not pay for FDEs by the hour. Nexus is incentivized to deliver results quickly, because that is when you renew. The platform scales; the service model does not create linear cost growth.

The question is: do you want to pay a vendor whose revenue grows when your project takes longer, or one whose revenue depends on delivering measurable outcomes?

Custom builds vs. agent deployment: 6-18 months vs. 2-6 weeks

Consulting engagements follow a sequential process: discovery, scoping, requirements, design, development, testing, deployment, and optimization. Each phase depends on the previous one. Each phase generates billable hours. Timelines are measured in months, not weeks. Some of these phases are genuinely necessary for complex custom solutions. Others, particularly extended data quality assessments, governance framework design, and multi-month "preparation" phases before any agent gets built, can become a form of complexity inflation. The firm's billing model does not penalize adding phases; it rewards it.

At one Nexus client, an outsourcing firm spent a full year in "project management mode." Twelve months of scoping, planning, and preparation, only finalizing the plan for a first knowledge assistant. Nexus came in, scraped the data, implemented the agent, and pushed it to production in 4 weeks. That is not 10% faster. It is a structural difference in how delivery works when the vendor is incentivized to ship, not to bill.

Most enterprise business workflows (sales operations, customer support, HR, marketing) do not need a custom-built solution designed from scratch. They need agents that understand business logic, integrate with existing systems, complete work end-to-end, and escalate intelligently when they encounter something unexpected.

Orange deployed customer onboarding agents in 4 weeks. Lambda deployed sales research agents in days. These timelines are not because the problems were simple. They are because the platform handles the infrastructure, integrations, and compliance, FDEs handle the complexity of tailoring the solution, and nobody is incentivized to stretch the timeline.

Forward Deployed Engineers: why Nexus is a solution, not just software (and not just consultants)

Artefact's consultants are skilled. The people they hire from Ecole Polytechnique, data science programs, and AI research backgrounds are genuinely capable. The difference is not talent. It is the model and the incentives behind it.

Consulting engagements are project-based. Consultants arrive, build, deliver, and eventually move on. Knowledge transfer happens, but depth varies. Your team inherits what was built but may not fully own the iteration and optimization cycle. If something needs to change, you often need to re-engage, which means more billable days for the consultancy. The firm's interests and your interests diverge at the point of delivery: you want a finished product you can own; they want an ongoing relationship that generates revenue.

Nexus Forward Deployed Engineers are embedded with your team, not on a separate project track. You do not pay for FDEs by the hour; their work is included. This changes the incentive structure entirely. FDEs are motivated to make your team self-sufficient, not dependent. They:

• Identify the highest-impact use cases first. Not guessing based on generic templates, but analyzing your specific operations to find where agents deliver the most value. No multi-week "discovery phase" that generates a report recommending more consulting.
• Design agents that fit your reality. Not off-the-shelf configurations, but agents tailored to your workflows, systems, edge cases, and business logic.
• Handle integration complexity. So your team does not need to learn a new platform or pull engineers off product work.
• 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.
• Optimize continuously. Agents improve with use. FDEs help analyze performance, refine escalation logic, and scale agents to new teams and processes. This optimization is included, not billed as a separate engagement.

This is why Nexus converts 100% of POCs to annual contracts. The engagement is structured to deliver measurable value before you commit. The incentives are aligned: Nexus earns your renewal by delivering results, not by extending the engagement.

Ongoing dependency vs. business ownership

This is the difference that compounds over time, and it is where the structural incentive misalignment becomes most visible.

With a consulting model, the consultancy holds the deep knowledge of what was built and why. Your team uses the output, but when priorities shift, new use cases emerge, or the business evolves, you typically need to go back to the consultancy. This is not accidental; it is the nature of services businesses. The consultancy profits from your ongoing dependency. Every time you need a change, an expansion, or an optimization, that is another engagement, another set of billable days. Making you fully self-sufficient would mean ending a revenue stream. Even well-intentioned firms face this structural pull.

With Nexus, the goal from day one is business team ownership. Agents live in the tools your team already uses (Slack, Teams, WhatsApp, email, CRM). Business teams iterate on agents directly. When Lambda changed data sources, updated account segmentation, and adjusted priorities, their team made those changes without filing a ticket with anyone. No consultant re-engagement. No additional billing. The agent adapted.

The question is not just what gets built. It is who controls it after it is built, and whether the vendor's incentives align with your independence or your dependency.

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Frequently asked questions

Can Artefact and Nexus be complementary?

Yes. Some enterprises use data consultancies for foundational work (data strategy, data infrastructure, custom ML models) and Nexus for deploying AI agents across business workflows. If Artefact has already helped you build your data foundation, Nexus agents can sit on top of that foundation and turn data into autonomous action. The two solve different problems: Artefact builds the data layer; Nexus deploys the agent layer. Just be clear about where one engagement ends and the other begins, so foundational work does not expand indefinitely before agents reach production.

We have already engaged Artefact for a data transformation. Do we still need Nexus?

It depends on what comes next. If the goal is to build custom analytical models or continue data infrastructure work, Artefact is the right partner for that scope. But if the next step is deploying AI that completes business workflows in production (automating customer support, sales operations, HR processes, marketing workflows), you are looking at a different problem. The natural tendency of a consulting engagement is to expand: after data strategy comes data governance, after governance comes data quality, after quality comes "readiness assessment," and each phase generates billable hours before any agent reaches production. Nexus deploys agents in weeks, and your business teams own the result. Sometimes the best move is to run both in parallel rather than waiting for one engagement to finish expanding before starting the next.

Artefact has an "AI & GenAI Factory" offering. How does that compare to Nexus agents?

Artefact's AI & GenAI Factory is a delivery framework for building and scaling AI solutions, often using Google Cloud technologies (Gemini, Vertex AI). It is a structured consulting methodology for moving from proof of concept to production AI. The output is typically custom models and data pipelines built by Artefact's team, maintained by Artefact's team, and modified by Artefact's team (for additional fees). Nexus is a platform where your business teams build and deploy autonomous AI agents, supported by Forward Deployed Engineers. The distinction: Artefact's AI & GenAI Factory produces custom solutions that keep you in the consulting orbit; Nexus produces agents owned and iterated by your business teams. One model creates ongoing dependency and ongoing billing. The other creates ownership.

Artefact has 1,700+ people. Nexus is smaller. Does scale matter?

Artefact's scale is a strength for large, multi-country data transformation programs where you need dozens of consultants across regions. It is also worth noting that more headcount in a time-based billing model means more capacity to bill, not necessarily faster delivery. For deploying AI agents across business workflows, the question is not how many people the vendor has. It is how quickly agents get to production and who owns them afterward. Orange deployed with Nexus in 4 weeks. Lambda deployed in days. The outsourcing firm at another Nexus client had plenty of people; it still took them a year to finalize planning. Nexus's 100% POC-to-contract conversion rate suggests that scale of impact, not headcount, is what matters for this category.

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.

How does pricing compare?

Artefact charges day rates for consultants (industry standard for specialized AI consultancies: $1,000-$2,500 per consultant per day) and project-based fees for defined scopes. A 6-month engagement with a team of 3-5 consultants can run into hundreds of thousands of dollars, and scope changes add to the total. The firm earns more when the engagement takes longer or when scope expands, which is the structural tension at the heart of every time-based billing model. Nexus charges per-agent pricing tied to value delivered, with a 3-month POC before annual commitment. The cost model is fundamentally different: you pay for outcomes, not hours. Nexus does not earn more when projects take longer; it earns when agents deliver results and you renew.

Is Nexus enterprise-grade enough for our requirements?

Nexus is SOC 2 Type II, ISO 27001, ISO 42001, and GDPR certified. Full audit trails, decision traceability, role-based access control. Deployed at Orange (multi-billion euro public telecom, 120,000+ employees), Lambda ($4B+ valuation), and other large regulated enterprises. Y Combinator F25 batch. $4M seed from General Catalyst and Y Combinator. Offices in Brussels (HQ) and San Francisco.

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Worth exploring?

If your team has been evaluating data and AI consultancies and weighing the trade-offs (how long until production, who owns the solution after delivery, what happens when requirements change, how costs scale over time, and whether the vendor's incentives align with yours), it might be worth seeing how Orange and Lambda approached the same decision.

Orange, a multi-billion euro telecom with every option available, deployed with Nexus in 4 weeks. 50% conversion improvement. $4M+ yearly revenue impact. Business teams own the agents. No day rates. No ongoing consulting dependency. Lambda, a $4B+ AI company with world-class engineers, chose to buy instead of build. $4B+ in pipeline identified. Anticipated value: more than $7M by 2026. At another enterprise, an outsourcing firm spent a year planning; Nexus delivered in 4 weeks.

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. The incentives are simple: Nexus earns your renewal by delivering results, not by billing hours.

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Related comparisons

• Nexus vs McKinsey/Accenture (AI consulting), Generalist consulting vs. platform + service: when you need strategy vs. when you need agents in production
• Nexus vs ML6, Another specialized AI consultancy comparison: Belgian-origin, strong technical talent, same structural incentive question
• Nexus vs LangGraph, Developer framework comparison: if you are considering building internally with frameworks
• Nexus vs Microsoft Copilot, AI assistant vs. autonomous agents: assists individuals vs. completes workflows
• Build vs Buy: AI Agents, The full build vs. buy comparison
• Back to all comparisons -->