Plain Concepts
Madrid-founded Microsoft Gold and AI Partner delivering AI, mixed reality, and cloud engineering since 2006.
What is Plain Concepts?
Plain Concepts, founded in 2006 and headquartered in Madrid, Spain, is a 450-plus person technology consultancy with offices across the USA, UK, Spain, Germany, the Netherlands, and Romania. As a Microsoft Gold Partner, Microsoft AI Partner, and 2016 Microsoft Partner of the Year, Plain Concepts brings deep Azure-native AI and machine learning delivery experience alongside mixed reality and IoT engineering.
Plain Concepts was founded in 2006 and is headquartered in Madrid, Spain. The firm employs 201–500 people and works primarily with clients in Enterprise, Retail, Healthcare, Financial Services sectors. Its primary differentiator is: Deep Azure-native AI and ML delivery credentials as a Microsoft Gold and AI Partner, plus mixed reality expertise.
Plain Concepts tech stack and services
| Service area |
|---|
| ML Development |
| AI Consulting |
| MLOps |
| Data Engineering |
Plain Concepts use cases
Short answer: Plain Concepts is best suited for azure-standardized enterprises, certified Microsoft AI partner.
| Use case |
|---|
| Azure-native ML model deployment for an enterprise client |
| Mixed reality plus AI product development |
| Microsoft-ecosystem AI modernization programmes |
| Enterprise IoT with embedded predictive analytics |
Plain Concepts pricing
Short answer: Plain Concepts uses a dedicated team, fixed project, retainer pricing approach. Minimum engagement starts at $35K.
| Engagement model | Typical range | Best for |
|---|---|---|
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
| Fixed project | From $35K | Well-defined scope |
| Retainer | Monthly rate; not public | Ongoing AI engineering |
Plain Concepts pros and cons
| Advantages | Things to consider |
|---|---|
| +Two decades of operating history since founding in 2006, with Microsoft Gold and AI Partner status | -Azure-centric specialization may be less ideal for clients standardized on AWS or GCP |
| +Multi-country office footprint across Spain, the UK, Germany, the Netherlands, Romania, and the US for broad coverage | -Broader technology consultancy scope, including mixed reality and IoT, means ML is one of several core practices |
| +Deep Azure-native ML and AI delivery credentials, useful for Microsoft-standardized enterprises | -Larger enterprise-oriented engagement sizes, less accessible for very small startup budgets |
| +Recognized with Microsoft Partner of the Year award in 2016 |
Plain Concepts vs alternatives
How Plain Concepts compares to the other top Machine Learning Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Startups and mid-market, senior dedicated ML team. | a full-stack ML delivery team (data science, MLOps, QA) inherited from an established parent software company, at boutique-agency pricing. | 4.9 | Full comparison |
| ML6 | Enterprises, production MLOps at scale. | Official OpenAI Services Partner status combined with over a decade of pure-play ML engineering focus | 4.7 | Full comparison |
| Alexander Thamm | DACH manufacturers, AI strategy plus ML delivery. | Deep specialization in industrial and automotive ML use cases across the German Mittelstand | 4.6 | Full comparison |
| Kineo.ai | Mid-market EU businesses, lean AI consulting. | All-Germany team of AI consultants focused specifically on operational-efficiency ML use cases | 4.6 | Full comparison |
| DataRoot Labs | Startups and SMBs, lean ML team, competitive rates. | Founder-led, unfunded boutique with nearly a decade of focused custom ML delivery experience | 4.5 | Full comparison |
| Twistag | Growth-stage brands, senior-only AI agent builds. | Senior-only engineering team with a client roster including well-known global brands | 4.5 | Full comparison |
| Preste | EU companies, custom CV/NLP, French presence. | Dual Paris and Kyiv structure pairing French market presence with dedicated computer vision and NLP engineering delivery | 4.4 | Full comparison |
| STX Next | Companies wanting ML plus large-scale Python engineering. | One of Europe's largest dedicated Python engineering companies, with ML and data practices built on that scale | 4.3 | Full comparison |
| Neoteric | Mid-market companies, gen-AI PoC to production. | Two-decade-old Polish software house with a dedicated generative AI practice and a US-facing New York office | 4.3 | Full comparison |
| Tooploox | Hard ML/AI research-engineering problems. | Research-grade ML engineering with peer-reviewed academic recognition at ECCV 2024, alongside client delivery | 4.3 | Full comparison |
| Opinov8 | Enterprises and startups, AI across cloud engineering. | AI treated as a foundational layer across the entire engineering lifecycle, not a bolt-on service | 4.2 | Full comparison |
| FELD M | EU enterprises, long-established multi-country AI consulting. | Over two decades of operating history since founding in 2002, with organic growth into a five-office pan-European practice | 4.2 | Full comparison |
| WeAreBrain | Companies wanting AI within digital product builds. | Digital product agency DNA combined with a dedicated AI, ML, and intelligent automation practice | 4.2 | Full comparison |
| DATAFOREST | Small/mid-market, data engineering plus ML analytics. | Combined data engineering (ETL) and ML analytics practice with a growing review base | 4.1 | Full comparison |
| Probayes | Automotive, defense, finance — Bayesian modeling expertise. | Over two decades of specialization in Bayesian AI and predictive analytics, predating the current ML and AI boom | 4.1 | Full comparison |
| Digica | Regulated industries, ML plus embedded systems. | Combines ML model development with embedded systems and IoT engineering for regulated hardware-adjacent industries | 4.1 | Full comparison |
| Imaginary Cloud | Companies wanting ML plus strong product design. | Design-led software development studio with AI positioned as a first-class capability, not an afterthought | 4.0 | Full comparison |
| N-iX | Enterprises, ML bundled with large-scale engineering. | Over two decades of engineering scale, over 1,000 staff, with an EU-registered legal entity in Malta | 4.0 | Full comparison |
| Gemmo | Companies wanting a structured, staged AI engagement. | Structured three-phase engagement model of Pathfinder, Implementation, and Optimization, rather than an open-ended consulting retainer | 4.0 | Full comparison |
| Edvantis | Enterprises, EU-registered nearshore ML engineering. | EU legal registration in Poland combined with substantial delivery scale across Ukraine and Germany | 3.9 | Full comparison |
| CodeLeap | Early-stage startups, fast founder-friendly AI features. | Founder-friendly, speed-oriented delivery model built specifically for startup-stage product timelines | 3.9 | Full comparison |
| High-Tech Systems & Software | Healthcare orgs, AI bundled with healthcare software. | Deep healthcare-sector software specialization in supply chain and telemedicine, with AI and ML layered on top | 3.8 | Full comparison |
| DEPT | Large brands, ML-driven marketing personalization at scale. | Proprietary AI marketing platform, Ada, and global scale of over 4,000 specialists across 30-plus offices, unmatched by any other firm on this list | 4.0 | Full comparison |
| Software Mind | Enterprises, ML bundled with multi-region engineering. | Over 25 years of operating history and enterprise-scale delivery capacity across three continents | 3.8 | Full comparison |
| Innowise | Enterprises, low-cost nearshore staff augmentation with AI. | Very large delivery scale and broad geographic reach, positioned for volume staff augmentation over specialist ML depth | 3.8 | Full comparison |
| BJSS | UK public sector, enterprise-grade AI, proven consultancy. | Over three decades of operating history and deep specialization in regulated, complex enterprise environments | 3.8 | Full comparison |
| Siili Solutions | Nordic/EU enterprises, publicly listed IT consultancy. | Publicly traded on Nasdaq Helsinki, offering financial transparency uncommon among privately held ML firms | 3.7 | Full comparison |
| SDG Group | Large enterprises, ML analytics within BPM programmes. | Three decades of management consulting heritage applied to enterprise-scale analytics and AI programmes | 3.7 | Full comparison |
| Transparity | UK Azure enterprises, certified Microsoft AI partner. | Proprietary AI Factory framework built specifically around Microsoft Azure and Copilot technologies | 3.7 | Full comparison |
Plain Concepts FAQ
What is Plain Concepts?
Plain Concepts, founded in 2006 and headquartered in Madrid, Spain, is a 450-plus person technology consultancy with offices across the USA, UK, Spain, Germany, the Netherlands, and Romania. As a Microsoft Gold Partner, Microsoft AI Partner, and 2016 Microsoft Partner of the Year, Plain Concepts brings deep Azure-native AI and machine learning delivery experience alongside mixed reality and IoT engineering.
How much does Plain Concepts charge?
Plain Concepts uses dedicated team, fixed project, retainer pricing. Minimum engagement starts at $35K. A discovery call is required to get project-specific quotes.
What tech stack does Plain Concepts use?
Plain Concepts works with Python, Azure ML, Azure OpenAI Service, .NET, Power BI, Kubernetes. Primary industries served include Enterprise, Retail, Healthcare, Financial Services.
Is Plain Concepts right for enterprise?
Azure-standardized enterprises, certified Microsoft AI partner. 201–500 team size. Key consideration: Azure-centric specialization may be less ideal for clients standardized on AWS or GCP.
What are the best Plain Concepts alternatives?
The best alternatives to Plain Concepts depend on your use case. Top options are:
- Tensorway: a full-stack ml delivery team (data science, mlops, qa) inherited from an established parent software company, at boutique-agency pricing.
- ML6: official openai services partner status combined with over a decade of pure-play ml engineering focus
- Alexander Thamm: deep specialization in industrial and automotive ml use cases across the german mittelstand