NILG.AI vs WeAreBrain: full comparison for 2026
Last updated: July 2026
Quick verdict
NILG.AI (4.5/5) edges ahead of WeAreBrain (4.3/5) overall. NILG.AI is the better choice for companies earlier in their AI adoption curve that want a structured discover-pilot-scale engagement model rather than a from-scratch build.. WeAreBrain is the stronger option for startups and scale-ups wanting AI-native product development combined with broader software modernization, not just an isolated ML model.. The right choice depends on your project size, budget, and required tech stack.
NILG.AI vs WeAreBrain: head-to-head summary
| Criterion | NILG.AI | WeAreBrain |
|---|---|---|
| Founded | 2018 | 2015 |
| HQ | Porto, Portugal | Netherlands (internationally distributed team) |
| Team size | 10–49 | 60+ |
| Rating | 4.5 / 5 | 4.3 / 5 |
| Best for | Companies earlier in their AI adoption curve that want a structured discover-pilot-scale engagement model rather than a from-scratch build. | Startups and scale-ups wanting AI-native product development combined with broader software modernization, not just an isolated ML model. |
| Pricing model | Consulting engagement, pilot-to-scale retainer | Dedicated team, fixed project |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, scikit-learn, Data pipelines | Python, AI product tooling, Shopify/SAP Commerce Cloud integrations |
| Industries served | Public Sector, Cross-industry AI adoption | Transport & Logistics, Healthcare, EdTech, Retail/E-commerce |
NILG.AI vs WeAreBrain: overview
NILG.AI
NILG.AI is a Porto, Portugal AI consultancy founded in 2018 by Kelwin Fernandes (PhD, Computer Science, University of Porto) and Nohelia González. It runs a structured discover-pilot-scale methodology to help businesses identify high-impact AI opportunities, validate them, and scale what works, and has assisted over 100 companies across sectors. The company was incubated at UPTEC and was awarded Data Changemaker of the Year at DSPA Insights 2024 for an AI-driven urban waste-management project in the Algarve. Its YouTube education channel has over 100,000 subscribers and NILG.AI was selected for Microsoft's 'Learn with Creators' program.
WeAreBrain
WeAreBrain is a Netherlands-headquartered AI-native product studio founded in 2015, combining AI product development with software modernization, e-commerce integrations, and automation services. It describes itself as 'a winning team, not an agency,' with a 60+ person, 13-nationality team and an average client tenure of 3.8 years, alongside an NPS score above 80 (per company website). Named clients include SidelineSwap and clevergig, which was acquired by Visma.
Services and capabilities: NILG.AI vs WeAreBrain
| Capability | NILG.AI | WeAreBrain |
|---|---|---|
| ML Development | ✓ | ✓ |
| AI Consulting | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✗ | ✓ |
| MLOps | ✗ | ✗ |
| Data Engineering | ✓ | ✗ |
| Staff Augmentation | ✗ | ✗ |
Tech stack comparison: NILG.AI vs WeAreBrain
| Framework / platform | NILG.AI | WeAreBrain |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | N/A |
| PyTorch | N/A | N/A |
| LangChain | N/A | N/A |
| Databricks | N/A | N/A |
Pricing comparison: NILG.AI vs WeAreBrain
| Criterion | NILG.AI | WeAreBrain |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Consulting retainer, Fixed-scope pilot | Dedicated team, Fixed project |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: NILG.AI vs WeAreBrain
| Dimension | NILG.AI | WeAreBrain |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Public Sector, Cross-industry AI adoption | Transport & Logistics, Healthcare, EdTech |
| Best use cases | AI opportunity discovery workshops, Municipal and public-sector optimization pilots | AI-native product MVP development, E-commerce AI personalization |
| Typical project type | Consulting retainer | Dedicated team |
NILG.AI vs WeAreBrain: pros and cons
| NILG.AI | |
|---|---|
| + | Founder-level technical credibility (PhD-led, Microsoft education partner) uncommon at this company size |
| + | Structured discovery-pilot-scale methodology reduces risk for first-time AI buyers |
| + | Public recognition (Data Changemaker of the Year 2024) for a real municipal deployment |
| + | Incubated at UPTEC, giving it ties into Porto's applied-research ecosystem |
| - | 10–49 employee band limits capacity for running several large programs concurrently |
| - | Heavier emphasis on strategy and pilot work than large-scale production ML engineering compared to bigger players |
| - | Public case studies skew toward public-sector and education rather than regulated enterprise sectors |
| WeAreBrain | |
|---|---|
| + | 80+ NPS and 3.8-year average client tenure signal strong retention (per company website) |
| + | 13-nationality team supports multilingual, multi-market European delivery |
| + | Combines AI-native product development with broader software modernization services |
| + | Founded 2015 with a decade of continuous operation |
| - | Broader software, e-commerce, and automation service lines mean ML is one of several offerings, not the sole focus |
| - | 60+ team size is modest relative to enterprise-scale competitors on this list |
| - | Notable named clients (SidelineSwap, clevergig) are smaller-profile than some competitors' enterprise logos |
Who should choose NILG.AI?
NILG.AI is the right choice for companies earlier in their AI adoption curve that want a structured discover-pilot-scale engagement model rather than a from-scratch build..
Founder-led by a University of Porto PhD with a public AI-education arm (100K+ YouTube subscribers, Microsoft education partner) that doubles as a technical credibility signal.. Minimum engagement starts at Not published. Works best with clients in Public Sector, Cross-industry AI adoption.
Who should choose WeAreBrain?
WeAreBrain is the right choice for startups and scale-ups wanting AI-native product development combined with broader software modernization, not just an isolated ML model..
Frames itself around culture and retention — 'a winning team, not an agency' — with a long average client tenure as central to its pitch alongside technical delivery.. Minimum engagement starts at Not published. Works best with clients in Transport & Logistics, Healthcare, EdTech, Retail/E-commerce.
Decision matrix: NILG.AI vs WeAreBrain
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | NILG.AI |
| You need a large dedicated team for an ongoing programme | WeAreBrain |
| Your budget is at the lower end | Compare: NILG.AI (Not published) vs WeAreBrain (Not published) |
| You need specialist depth in a specific vertical | WeAreBrain |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | NILG.AI |
Use case fit: NILG.AI vs WeAreBrain
| Use case | NILG.AI fit | WeAreBrain fit | Winner |
|---|---|---|---|
| AI opportunity discovery workshops | Strong | Strong | Both equally |
| Municipal and public-sector optimization pilots | Strong | Limited | NILG.AI |
| AI-native product MVP development | Limited | Strong | WeAreBrain |
| E-commerce AI personalization | Limited | Strong | WeAreBrain |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: NILG.AI vs WeAreBrain
NILG.AI (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Founder-led by a University of Porto PhD with a public AI-education arm (100K+ YouTube subscribers, Microsoft education partner) that doubles as a technical credibility signal.. It is best for companies earlier in their AI adoption curve that want a structured discover-pilot-scale engagement model rather than a from-scratch build..
WeAreBrain (4.3/5) is the better choice when startups and scale-ups wanting AI-native product development combined with broader software modernization, not just an isolated ML model.. If your situation matches those criteria, WeAreBrain is a competitive option.
Related comparisons
NILG.AI vs WeAreBrain FAQ
Is NILG.AI better than WeAreBrain?
NILG.AI (4.5/5) scores higher overall, but "better" depends on your use case. NILG.AI is better for companies earlier in their AI adoption curve that want a structured discover-pilot-scale engagement model rather than a from-scratch build.. WeAreBrain is better for startups and scale-ups wanting AI-native product development combined with broader software modernization, not just an isolated ML model..
How do NILG.AI and WeAreBrain differ in pricing?
NILG.AI uses consulting engagement, pilot-to-scale retainer pricing with a minimum engagement of Not published. WeAreBrain uses dedicated team, fixed project pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: NILG.AI or WeAreBrain?
NILG.AI is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between NILG.AI and WeAreBrain?
NILG.AI's primary differentiator is: founder-led by a university of porto phd with a public ai-education arm (100k+ youtube subscribers, microsoft education partner) that doubles as a technical credibility signal.. WeAreBrain's primary differentiator is: frames itself around culture and retention — 'a winning team, not an agency' — with a long average client tenure as central to its pitch alongside technical delivery.. They also differ in team size (10–49 vs 60+), minimum engagement (Not published vs Not published), and primary industries served (Public Sector, Cross-industry AI adoption vs Transport & Logistics, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.