NILG.AI vs Reaktor: full comparison for 2026
Last updated: July 2026
Quick verdict
NILG.AI (4.5/5) edges ahead of Reaktor (3.8/5) 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.. Reaktor is the stronger option for enterprises wanting AI capability embedded within a broader human-centred digital product and design consultancy, rather than a standalone ML vendor.. The right choice depends on your project size, budget, and required tech stack.
NILG.AI vs Reaktor: head-to-head summary
| Criterion | NILG.AI | Reaktor |
|---|---|---|
| Founded | 2018 | 2000 |
| HQ | Porto, Portugal | Helsinki, Finland |
| Team size | 10–49 | 700 |
| Rating | 4.5 / 5 | 3.8 / 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. | Enterprises wanting AI capability embedded within a broader human-centred digital product and design consultancy, rather than a standalone ML vendor. |
| Pricing model | Consulting engagement, pilot-to-scale retainer | Dedicated team, project-based consulting |
| Min. engagement | Not published | Not published (large enterprise engagements) |
| Primary tech stack | Python, scikit-learn, Data pipelines | Python, AI/data-driven product tooling, Cloud platforms |
| Industries served | Public Sector, Cross-industry AI adoption | Cross-industry digital product development |
NILG.AI vs Reaktor: 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.
Reaktor
Reaktor is a Helsinki, Finland digital consultancy founded in 2000, with 700 employees across nine offices including Helsinki, New York, Amsterdam, Stockholm, and Tokyo. It co-created 'Elements of AI,' a free AI-literacy MOOC with the University of Helsinki taken by over half a million people worldwide, and integrates AI and data-driven technology across a broader human-centred design and engineering practice rather than positioning itself as a standalone ML vendor.
Services and capabilities: NILG.AI vs Reaktor
| Capability | NILG.AI | Reaktor |
|---|---|---|
| ML Development | ✓ | ✓ |
| AI Consulting | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| Data Engineering | ✓ | ✓ |
| Staff Augmentation | ✗ | ✗ |
Tech stack comparison: NILG.AI vs Reaktor
| Framework / platform | NILG.AI | Reaktor |
|---|---|---|
| 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 Reaktor
| Criterion | NILG.AI | Reaktor |
|---|---|---|
| Minimum engagement | Not published | Not published (large enterprise engagements) |
| Engagement models | Consulting retainer, Fixed-scope pilot | Dedicated team, Project-based consulting |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: NILG.AI vs Reaktor
| Dimension | NILG.AI | Reaktor |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Public Sector, Cross-industry AI adoption | Cross-industry digital product development |
| Best use cases | AI opportunity discovery workshops, Municipal and public-sector optimization pilots | Human-centred AI product design and development, Enterprise AI literacy training programs |
| Typical project type | Consulting retainer | Dedicated team |
NILG.AI vs Reaktor: 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 |
| Reaktor | |
|---|---|
| + | 700 employees across nine global offices (Helsinki, New York, Amsterdam, Stockholm, Tokyo, and more) give major delivery scale |
| + | 'Elements of AI' MOOC, with 500,000+ participants, is a uniquely large-scale public AI-education contribution |
| + | Human-centred design integrated directly with AI and data engineering, useful for consumer-facing AI products |
| + | Founded 2000 — a quarter-century of continuous Helsinki-based operation |
| - | AI/ML is one capability within a much broader design-and-engineering digital consultancy, not the firm's primary specialization |
| - | 700-person, nine-office scale trades boutique-level AI focus for broad digital-consultancy breadth |
| - | Public case studies emphasize design and product outcomes more than specific ML model performance metrics |
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 Reaktor?
Reaktor is the right choice for enterprises wanting AI capability embedded within a broader human-centred digital product and design consultancy, rather than a standalone ML vendor..
Co-created 'Elements of AI,' a free AI literacy MOOC with the University of Helsinki taken by over half a million people worldwide — a public-education contribution unmatched by any other company on this list.. Minimum engagement starts at Not published (large enterprise engagements). Works best with clients in Cross-industry digital product development.
Decision matrix: NILG.AI vs Reaktor
| 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 | Reaktor |
| Your budget is at the lower end | Compare: NILG.AI (Not published) vs Reaktor (Not published (large enterprise engagements)) |
| You need specialist depth in a specific vertical | NILG.AI |
| 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 Reaktor
| Use case | NILG.AI fit | Reaktor fit | Winner |
|---|---|---|---|
| AI opportunity discovery workshops | Strong | Strong | Both equally |
| Municipal and public-sector optimization pilots | Strong | Limited | NILG.AI |
| Human-centred AI product design and development | Limited | Strong | Reaktor |
| Enterprise AI literacy training programs | Limited | Strong | Reaktor |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: NILG.AI vs Reaktor
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..
Reaktor (3.8/5) is the better choice when enterprises wanting AI capability embedded within a broader human-centred digital product and design consultancy, rather than a standalone ML vendor.. If your situation matches those criteria, Reaktor is a competitive option.
Related comparisons
NILG.AI vs Reaktor FAQ
Is NILG.AI better than Reaktor?
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.. Reaktor is better for enterprises wanting AI capability embedded within a broader human-centred digital product and design consultancy, rather than a standalone ML vendor..
How do NILG.AI and Reaktor differ in pricing?
NILG.AI uses consulting engagement, pilot-to-scale retainer pricing with a minimum engagement of Not published. Reaktor uses dedicated team, project-based consulting pricing with a minimum engagement of Not published (large enterprise engagements). Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: NILG.AI or Reaktor?
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 Reaktor?
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.. Reaktor's primary differentiator is: co-created 'elements of ai,' a free ai literacy mooc with the university of helsinki taken by over half a million people worldwide — a public-education contribution unmatched by any other company on this list.. They also differ in team size (10–49 vs 700), minimum engagement (Not published vs Not published (large enterprise engagements)), and primary industries served (Public Sector, Cross-industry AI adoption vs Cross-industry digital product development).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.