NILG.AI vs Grape Up: full comparison for 2026
Last updated: July 2026
Quick verdict
NILG.AI (4.5/5) edges ahead of Grape Up (4.0/5) 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.. Grape Up is the stronger option for automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm.. The right choice depends on your project size, budget, and required tech stack.
NILG.AI vs Grape Up: head-to-head summary
| Criterion | NILG.AI | Grape Up |
|---|---|---|
| Founded | 2018 | 2006 |
| HQ | Porto, Portugal | Kraków, Poland |
| Team size | 10–49 | Not disclosed |
| Rating | 4.5 / 5 | 4.0 / 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. | Automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm. |
| Pricing model | Consulting engagement, pilot-to-scale retainer | Fixed project, dedicated team |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, scikit-learn, Data pipelines | Python, Kubernetes, Cloud-native platforms |
| Industries served | Public Sector, Cross-industry AI adoption | Automotive, Financial Services, Manufacturing, Aviation |
NILG.AI vs Grape Up: 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.
Grape Up
Grape Up is a Kraków, Poland AI and cloud-native engineering firm founded in 2006, delivering agentic AI, generative-AI-powered legacy modernization, and advanced analytics alongside its own productized platforms: Databoostr for data sharing and monetization, and Cloudboostr, a Kubernetes stack for cloud deployment. Named clients include Porsche, Nissan, Mazda, Ducati, BNP, and Allstate (per company website), concentrated in automotive, finance, manufacturing, and aviation.
Services and capabilities: NILG.AI vs Grape Up
| Capability | NILG.AI | Grape Up |
|---|---|---|
| ML Development | ✓ | ✗ |
| AI Consulting | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✗ | ✓ |
| MLOps | ✗ | ✓ |
| Data Engineering | ✓ | ✗ |
| Staff Augmentation | ✗ | ✗ |
Tech stack comparison: NILG.AI vs Grape Up
| Framework / platform | NILG.AI | Grape Up |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | ✓ |
| PyTorch | N/A | N/A |
| LangChain | N/A | N/A |
| Databricks | N/A | N/A |
Pricing comparison: NILG.AI vs Grape Up
| Criterion | NILG.AI | Grape Up |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Consulting retainer, Fixed-scope pilot | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: NILG.AI vs Grape Up
| Dimension | NILG.AI | Grape Up |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Public Sector, Cross-industry AI adoption | Automotive, Financial Services, Manufacturing |
| Best use cases | AI opportunity discovery workshops, Municipal and public-sector optimization pilots | Agentic AI workflow automation for enterprises, Generative-AI-powered legacy system modernization |
| Typical project type | Consulting retainer | Fixed project |
NILG.AI vs Grape Up: 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 |
| Grape Up | |
|---|---|
| + | Notable automotive and finance client roster (Porsche, Nissan, Mazda, Ducati, BNP, Allstate) per company website |
| + | Own productized platforms (Databoostr, Cloudboostr) show deeper platform-engineering capability than pure staffing vendors |
| + | Founded 2006 — nearly two decades of continuous Kraków-based delivery |
| + | Agentic AI and GenAI-powered legacy modernization address a current enterprise pain point directly |
| - | Team size and detailed employee count are not publicly disclosed |
| - | Cloud-native and Kubernetes engineering roots mean AI/ML depth may be shallower than pure-play ML boutiques |
| - | Public case studies emphasize client logos over specific project outcomes and 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 Grape Up?
Grape Up is the right choice for automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm..
Built its own productized platforms (Databoostr, Cloudboostr) alongside custom delivery — a hybrid product-plus-services model less common among pure consultancies on this list.. Minimum engagement starts at Not published. Works best with clients in Automotive, Financial Services, Manufacturing, Aviation.
Decision matrix: NILG.AI vs Grape Up
| 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 | Grape Up |
| Your budget is at the lower end | Compare: NILG.AI (Not published) vs Grape Up (Not published) |
| You need specialist depth in a specific vertical | Grape Up |
| 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 Grape Up
| Use case | NILG.AI fit | Grape Up fit | Winner |
|---|---|---|---|
| AI opportunity discovery workshops | Strong | Strong | Both equally |
| Municipal and public-sector optimization pilots | Strong | Limited | NILG.AI |
| Agentic AI workflow automation for enterprises | Limited | Strong | Grape Up |
| Generative-AI-powered legacy system modernization | Limited | Strong | Grape Up |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: NILG.AI vs Grape Up
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..
Grape Up (4.0/5) is the better choice when automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm.. If your situation matches those criteria, Grape Up is a competitive option.
Related comparisons
NILG.AI vs Grape Up FAQ
Is NILG.AI better than Grape Up?
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.. Grape Up is better for automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm..
How do NILG.AI and Grape Up differ in pricing?
NILG.AI uses consulting engagement, pilot-to-scale retainer pricing with a minimum engagement of Not published. Grape Up uses fixed project, dedicated team 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 Grape Up?
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 Grape Up?
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.. Grape Up's primary differentiator is: built its own productized platforms (databoostr, cloudboostr) alongside custom delivery — a hybrid product-plus-services model less common among pure consultancies on this list.. They also differ in team size (10–49 vs Not disclosed), minimum engagement (Not published vs Not published), and primary industries served (Public Sector, Cross-industry AI adoption vs Automotive, Financial Services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.