NILG.AI vs InData Labs: full comparison for 2026
Last updated: July 2026
Quick verdict
NILG.AI (4.5/5) edges ahead of InData Labs (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.. InData Labs is the stronger option for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop.. The right choice depends on your project size, budget, and required tech stack.
NILG.AI vs InData Labs: head-to-head summary
| Criterion | NILG.AI | InData Labs |
|---|---|---|
| Founded | 2018 | 2014 |
| HQ | Porto, Portugal | Nicosia, Cyprus (R&D and delivery centers in Lithuania and the US) |
| Team size | 10–49 | 80+ |
| 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. | Companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop. |
| Pricing model | Consulting engagement, pilot-to-scale retainer | Fixed project, Time & Materials |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, scikit-learn, Data pipelines | Python, Generative AI/GPT tooling, Computer vision frameworks |
| Industries served | Public Sector, Cross-industry AI adoption | Cross-industry, Predictive Analytics |
NILG.AI vs InData Labs: 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.
InData Labs
InData Labs is a data science and AI consultancy legally headquartered in Nicosia, Cyprus, founded in 2014 by video-gaming industry veteran Marat Karpeko, with R&D and delivery centers in Lithuania and the US. The 80+ person firm runs its own R&D center and covers a wide technical band from generative AI and GPT integration through predictive analytics, forecasting, and computer vision. Its Cyprus legal HQ gives clients an EU-entity contracting structure alongside nearshore delivery capacity.
Services and capabilities: NILG.AI vs InData Labs
| Capability | NILG.AI | InData Labs |
|---|---|---|
| ML Development | ✓ | ✓ |
| AI Consulting | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✗ | ✓ |
| MLOps | ✗ | ✗ |
| Data Engineering | ✓ | ✓ |
| Staff Augmentation | ✗ | ✗ |
Tech stack comparison: NILG.AI vs InData Labs
| Framework / platform | NILG.AI | InData Labs |
|---|---|---|
| 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 InData Labs
| Criterion | NILG.AI | InData Labs |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Consulting retainer, Fixed-scope pilot | Fixed project, Time & Materials |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: NILG.AI vs InData Labs
| Dimension | NILG.AI | InData Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Public Sector, Cross-industry AI adoption | Cross-industry, Predictive Analytics |
| Best use cases | AI opportunity discovery workshops, Municipal and public-sector optimization pilots | Generative AI and GPT integration projects, Predictive analytics and forecasting |
| Typical project type | Consulting retainer | Fixed project |
NILG.AI vs InData Labs: 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 |
| InData Labs | |
|---|---|
| + | Founded 2014 — one of the longer-running boutique data science firms in this list |
| + | In-house R&D center is a differentiator versus pure staff-augmentation vendors |
| + | Cyprus legal HQ with Lithuania/US delivery centers gives EU-entity contracting plus nearshore delivery |
| + | Broad technical range from generative AI to classic forecasting and computer vision |
| - | 80+ employee band is imprecise — exact current headcount is not independently published |
| - | Legal HQ (Cyprus) is a smaller AI hub than its Lithuania delivery center, which may matter to buyers wanting an on-the-ground presence |
| - | Pricing model and minimum engagement are not published |
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 InData Labs?
InData Labs is the right choice for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop..
Runs its own R&D center rather than purely project-based delivery, spanning generative AI/GPT integration through classic predictive analytics and computer vision.. Minimum engagement starts at Not published. Works best with clients in Cross-industry, Predictive Analytics.
Decision matrix: NILG.AI vs InData Labs
| 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 | Check each company's engagement model |
| Your budget is at the lower end | Compare: NILG.AI (Not published) vs InData Labs (Not published) |
| 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 InData Labs
| Use case | NILG.AI fit | InData Labs fit | Winner |
|---|---|---|---|
| AI opportunity discovery workshops | Strong | Strong | Both equally |
| Municipal and public-sector optimization pilots | Strong | Limited | NILG.AI |
| Generative AI and GPT integration projects | Limited | Strong | InData Labs |
| Predictive analytics and forecasting | Limited | Strong | InData Labs |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: NILG.AI vs InData Labs
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..
InData Labs (4.4/5) is the better choice when companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop.. If your situation matches those criteria, InData Labs is a competitive option.
Related comparisons
NILG.AI vs InData Labs FAQ
Is NILG.AI better than InData Labs?
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.. InData Labs is better for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop..
How do NILG.AI and InData Labs differ in pricing?
NILG.AI uses consulting engagement, pilot-to-scale retainer pricing with a minimum engagement of Not published. InData Labs uses fixed project, time & materials 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 InData Labs?
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 InData Labs?
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.. InData Labs's primary differentiator is: runs its own r&d center rather than purely project-based delivery, spanning generative ai/gpt integration through classic predictive analytics and computer vision.. They also differ in team size (10–49 vs 80+), minimum engagement (Not published vs Not published), and primary industries served (Public Sector, Cross-industry AI adoption vs Cross-industry, Predictive Analytics).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.