NILG.AI vs SPD Technology: full comparison for 2026
Last updated: July 2026
Quick verdict
NILG.AI (4.5/5) edges ahead of SPD Technology (3.9/5) 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.. SPD Technology is the stronger option for fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience.. The right choice depends on your project size, budget, and required tech stack.
NILG.AI vs SPD Technology: head-to-head summary
| Criterion | NILG.AI | SPD Technology |
|---|---|---|
| Founded | 2018 | 2006 |
| HQ | Porto, Portugal | London, United Kingdom |
| Team size | 10–49 | 650+ |
| Rating | 4.5 / 5 | 3.9 / 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. | Fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience. |
| 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, OpenAI API, Anthropic API |
| Industries served | Public Sector, Cross-industry AI adoption | Fintech, Financial Services |
NILG.AI vs SPD Technology: 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.
SPD Technology
SPD Technology is a London, UK software product development company founded in 2006, with 650+ engineers across 30+ countries and 460+ delivered custom projects. It secured direct partnerships with OpenAI, Anthropic, and AWS specifically to reinforce its cloud and AI/ML capabilities, serving fintech, digital payments, and data-engineering clients including PitchBook, Morningstar, and Blackhawk Network.
Services and capabilities: NILG.AI vs SPD Technology
| Capability | NILG.AI | SPD Technology |
|---|---|---|
| ML Development | ✓ | ✓ |
| AI Consulting | ✓ | ✗ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✗ | ✓ |
| MLOps | ✗ | ✓ |
| Data Engineering | ✓ | ✗ |
| Staff Augmentation | ✗ | ✓ |
Tech stack comparison: NILG.AI vs SPD Technology
| Framework / platform | NILG.AI | SPD Technology |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | 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 SPD Technology
| Criterion | NILG.AI | SPD Technology |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Consulting retainer, Fixed-scope pilot | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: NILG.AI vs SPD Technology
| Dimension | NILG.AI | SPD Technology |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Public Sector, Cross-industry AI adoption | Fintech, Financial Services |
| Best use cases | AI opportunity discovery workshops, Municipal and public-sector optimization pilots | Fintech and payments platform AI features, OpenAI/Anthropic-based generative AI integrations |
| Typical project type | Consulting retainer | Fixed project |
NILG.AI vs SPD Technology: 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 |
| SPD Technology | |
|---|---|
| + | Direct partnerships with OpenAI and Anthropic, in addition to AWS, are a distinctive and verifiable technology relationship |
| + | 650+ engineers across 30+ countries and 460+ delivered custom projects show significant scale and reach |
| + | Notable enterprise clients including PitchBook, Morningstar, and Blackhawk Network |
| + | London HQ combined with globally distributed delivery centers balances local client access with cost-effective delivery |
| - | AI/ML is one of several practices (fintech, payments, data engineering, cloud) rather than the company's sole focus |
| - | 650+ person, 30+ country delivery footprint can mean variable team consistency across engagements |
| - | Founded 2006 as a general software product company — AI/ML partnerships are a comparatively recent strategic addition |
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 SPD Technology?
SPD Technology is the right choice for fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience..
Secured direct partnerships with OpenAI, Anthropic, and AWS specifically to reinforce its cloud and AI/ML capabilities — a more direct foundation-model-vendor relationship than most peers on this list disclose.. Minimum engagement starts at Not published. Works best with clients in Fintech, Financial Services.
Decision matrix: NILG.AI vs SPD Technology
| 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 | SPD Technology |
| Your budget is at the lower end | Compare: NILG.AI (Not published) vs SPD Technology (Not published) |
| You need specialist depth in a specific vertical | NILG.AI |
| You need staff augmentation or team extension | SPD Technology |
| You need consulting before committing to a build | NILG.AI |
Use case fit: NILG.AI vs SPD Technology
| Use case | NILG.AI fit | SPD Technology fit | Winner |
|---|---|---|---|
| AI opportunity discovery workshops | Strong | Strong | Both equally |
| Municipal and public-sector optimization pilots | Strong | Limited | NILG.AI |
| Fintech and payments platform AI features | Limited | Strong | SPD Technology |
| OpenAI/Anthropic-based generative AI integrations | Limited | Strong | SPD Technology |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: NILG.AI vs SPD Technology
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..
SPD Technology (3.9/5) is the better choice when fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience.. If your situation matches those criteria, SPD Technology is a competitive option.
Related comparisons
NILG.AI vs SPD Technology FAQ
Is NILG.AI better than SPD Technology?
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.. SPD Technology is better for fintech and payments companies wanting AI/ML delivered by a vendor with direct OpenAI and Anthropic partnerships and deep MVP-to-enterprise software product experience..
How do NILG.AI and SPD Technology differ in pricing?
NILG.AI uses consulting engagement, pilot-to-scale retainer pricing with a minimum engagement of Not published. SPD Technology 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 SPD Technology?
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 SPD Technology?
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.. SPD Technology's primary differentiator is: secured direct partnerships with openai, anthropic, and aws specifically to reinforce its cloud and ai/ml capabilities — a more direct foundation-model-vendor relationship than most peers on this list disclose.. They also differ in team size (10–49 vs 650+), minimum engagement (Not published vs Not published), and primary industries served (Public Sector, Cross-industry AI adoption vs Fintech, Financial Services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.