NILG.AI vs Addepto: full comparison for 2026
Last updated: July 2026
Quick verdict
NILG.AI (4.5/5) edges ahead of Addepto (4.4/5) 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.. Addepto is the stronger option for mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline.. The right choice depends on your project size, budget, and required tech stack.
NILG.AI vs Addepto: head-to-head summary
| Criterion | NILG.AI | Addepto |
|---|---|---|
| Founded | 2018 | 2017 |
| HQ | Porto, Portugal | Warsaw, Poland |
| Team size | 10–49 | 50–249 |
| Rating | 4.5 / 5 | 4.4 / 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. | Mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline. |
| Pricing model | Consulting engagement, pilot-to-scale retainer | Fixed project, Time & Materials |
| Min. engagement | Not published | $10,000+ |
| Primary tech stack | Python, scikit-learn, Data pipelines | Python, MLOps pipelines, AWS |
| Industries served | Public Sector, Cross-industry AI adoption | Aviation, Manufacturing, Automotive, Financial Services, Retail/E-commerce, Logistics |
NILG.AI vs Addepto: 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.
Addepto
Addepto is a Warsaw, Poland AI consultancy founded in 2017 that explicitly positions its value around production-grade delivery — moving clients from proof-of-concept to production — rather than research exploration. It covers AI consulting, generative AI development, data engineering, MLOps, document processing, and computer vision, serving aviation, manufacturing, automotive, finance, retail, healthcare, and logistics clients. Addepto is a GoodFirms top-rated firm for Big Data and Business Intelligence services, with a 50–249 employee band per Clutch.
Services and capabilities: NILG.AI vs Addepto
| Capability | NILG.AI | Addepto |
|---|---|---|
| ML Development | ✓ | ✓ |
| AI Consulting | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Data Engineering | ✓ | ✓ |
| Staff Augmentation | ✗ | ✗ |
Tech stack comparison: NILG.AI vs Addepto
| Framework / platform | NILG.AI | Addepto |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | N/A | ✓ |
| Microsoft Azure | N/A | N/A |
| Google Cloud | 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 Addepto
| Criterion | NILG.AI | Addepto |
|---|---|---|
| Minimum engagement | Not published | $10,000+ |
| Engagement models | Consulting retainer, Fixed-scope pilot | Fixed project, Time & Materials, Dedicated team |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Enterprise / mid-market | Accessible |
Target audience comparison: NILG.AI vs Addepto
| Dimension | NILG.AI | Addepto |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Public Sector, Cross-industry AI adoption | Aviation, Manufacturing, Automotive |
| Best use cases | AI opportunity discovery workshops, Municipal and public-sector optimization pilots | Computer vision for document processing, MLOps pipeline hardening for existing proof-of-concepts |
| Typical project type | Consulting retainer | Fixed project |
NILG.AI vs Addepto: 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 |
| Addepto | |
|---|---|
| + | Broad industry coverage from aviation to legal shows delivery flexibility beyond a single vertical |
| + | Explicit MLOps and production focus addresses the common 'stuck in proof-of-concept' failure mode |
| + | $10K entry point is accessible for a mid-market pilot engagement |
| + | GoodFirms top-rated recognition for Big Data and Business Intelligence services |
| - | Broad industry spread can mean less depth in any single regulated vertical than a specialist boutique |
| - | Exact team size within the 50–249 Clutch band is not broken out by function |
| - | Public case studies are largely testimonial-based rather than published with hard 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 Addepto?
Addepto is the right choice for mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline..
Explicit 'proof-of-concept to production' positioning addresses the common failure mode where enterprise ML pilots never reach deployment.. Minimum engagement starts at $10,000+. Works best with clients in Aviation, Manufacturing, Automotive, Financial Services, Retail/E-commerce, Logistics.
Decision matrix: NILG.AI vs Addepto
| 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 | Addepto |
| Your budget is at the lower end | Compare: NILG.AI (Not published) vs Addepto ($10,000+) |
| You need specialist depth in a specific vertical | Addepto |
| 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 Addepto
| Use case | NILG.AI fit | Addepto fit | Winner |
|---|---|---|---|
| AI opportunity discovery workshops | Strong | Strong | Both equally |
| Municipal and public-sector optimization pilots | Strong | Limited | NILG.AI |
| Computer vision for document processing | Limited | Strong | Addepto |
| MLOps pipeline hardening for existing proof-of-concepts | Limited | Strong | Addepto |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: NILG.AI vs Addepto
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..
Addepto (4.4/5) is the better choice when mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline.. If your situation matches those criteria, Addepto is a competitive option.
Related comparisons
NILG.AI vs Addepto FAQ
Is NILG.AI better than Addepto?
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.. Addepto is better for mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline..
How do NILG.AI and Addepto differ in pricing?
NILG.AI uses consulting engagement, pilot-to-scale retainer pricing with a minimum engagement of Not published. Addepto uses fixed project, time & materials pricing with a minimum engagement of $10,000+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: NILG.AI or Addepto?
Addepto 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 Addepto?
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.. Addepto's primary differentiator is: explicit 'proof-of-concept to production' positioning addresses the common failure mode where enterprise ml pilots never reach deployment.. They also differ in team size (10–49 vs 50–249), minimum engagement (Not published vs $10,000+), and primary industries served (Public Sector, Cross-industry AI adoption vs Aviation, Manufacturing).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.