# Plain Concepts vs QUANT AI Lab

> Two top-rated AI Consulting agencies in Madrid. Side by side: published rates, team size, service and industry coverage, and the curator's assessment of both — pairings are never sold.

Canonical page: https://www.aiagencies.eu/compare/plain-concepts-vs-quant-ai-lab
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Plain Concepts | QUANT AI Lab |
|---|---|---|
| Editorial score | 68/100 | 61.8/100 |
| Hourly rate | €85-130/hr (directory-listed) | not published |
| Team size | 250+ | 50-249 |
| Location | Madrid, Spain | Madrid, Spain |
| Founded | 2006 | 2019 |
| Last reviewed | 2026-09-07 | 2026-08-05 |
| Services | AI Consulting, AI Development, Generative AI | AI Consulting, AI Development, AI Automation |
| Industries | Healthcare, FinTech, Manufacturing | FinTech, Manufacturing, E-commerce |

## Our take on Plain Concepts
Reviewed 2026-09-07 by Gabor Kiss against published evidence.

The AI portfolio is named, dated and Azure-deep: ALEX writes cataract discharge reports for the Ribera healthcare group on Azure OpenAI and Azure AI Search with dynamic few-shot retrieval, validated at 96% of reports needing no physician correction, with the head of ophthalmology quoted by name.

AENOR's standards search pairs a machine-learning search engine with an assistant over 50,000 published certifications, Thera4All's Brain runs Azure OpenAI inside Plain Concepts' own Evergine engine behind a content-moderation layer, and at Nestlé the work spans phishing-URL vision inspection, an MLOps platform and a governed generative-AI platform for employees.

The caveat is measurement—most studies close on qualitative bullets like "more efficient processes", so the Ribera 96% is the exception rather than the pattern.

The weaker fit is buyers outside the Microsoft stack: nearly every published build lands on Azure, and at €85–130/h that stack opinion is priced at the top of the Spanish market.

### Key strengths
- Four named-client AI systems published with the stack named, including a healthcare deployment validated at 96% of generated reports requiring no physician correction
- Own graphics engine (Evergine) and a Microsoft relationship deep enough that Nestlé's 700-expert Global IT Hub uses them across security, MLOps and generative AI
- More than 700 employees published, so a multi-workstream program is staffable without subcontracting

### Good to know
- Most case studies close on qualitative bullets rather than measured outcomes—ask for the metric that moved on the engagement closest to yours
- Nearly all published work is Azure-native; if you run on AWS or GCP, ask for a delivered system on your stack
- At €85–130/h they price above most of the Spanish market—scope tightly, since published projects range from multi-year programs to single features

## Our take on QUANT AI Lab
Reviewed 2026-08-05 by Gabor Kiss against published evidence.

Twenty-eight published use cases, and what makes them worth reading is that each one names its stack.

Fraud detection on Databricks, Neo4j and Snowflake. A destination recommender for airline revenue built with SageMaker, Hugging Face, TensorFlow and Airflow. Retrieval-augmented chat on LangChain and FAISS. A Bayesian library industrialized with pgmpy, Docker and FastAPI. Climate transition risk, ESG flood-risk assessment against CSRD, PPE detection, predictive maintenance, unsupervised fraud detection. That is an engineering catalog rather than a marketing one, and it is the most technically itemized portfolio on this register.

The practice is anchored in quantitative finance—the group chief executive is a known figure in model risk—and Virgo, its own architecture, is aimed at regulated and mission-critical deployment.

The weakness is attribution. BNP Paribas, Santander, BBVA and Repsol appear as logos, every use case is anonymized, and none carries a result. The technical credibility is high and the delivery record is unverifiable from outside. Ask which logo maps to which build.

### Key strengths
- Twenty-eight use cases each naming its technology stack, which lets an engineering buyer assess the team before the first call
- Genuine quantitative depth—Bayesian networks, hierarchical models, optimization—rather than an LLM-only practice
- A registered Spanish company with a named executive team and offices in Madrid, Paris, Bordeaux and London

### Good to know
- Every use case is anonymized and the four bank logos attach to none of them—ask which client corresponds to which build
- No outcome figures anywhere; the catalog proves capability and says nothing about impact

## Which to choose
Choose Plain Concepts for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in Healthcare
- broader service offering — also covers Generative AI
- a higher overall editorial score

Choose QUANT AI Lab for:
- closer collaboration at a smaller team scale
- disclosed specialization in E-commerce
- broader service offering — also covers AI Automation

## Questions buyers ask

### Which has more FinTech experience, Plain Concepts or QUANT AI Lab?
Both agencies show documented FinTech work and have similar industry breadth. Compare directly on the agency profiles: Plain Concepts and QUANT AI Lab.

### Which scores higher overall, Plain Concepts or QUANT AI Lab?
Plain Concepts scores 68/100, 6.2 points higher than QUANT AI Lab at 61.8/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.

### When should I consider both Plain Concepts and QUANT AI Lab?
Consider running parallel discovery briefs with Plain Concepts and QUANT AI Lab if your project spans multiple workstreams, you want competitive proposals to compare scope and approach, or you're undecided between the specialization angles each brings (see the Best for cards above). Most engagements ultimately go with one — but the parallel-brief phase is a low-cost way to validate fit.

---
Source: AIAgencies.eu — the curated register of European AI agencies. Rate provenance: "agency-confirmed" = disclosed via submission or claimed profile; "directory-listed" = Clutch or two agreeing B2B directories (lone unverified sources are never written). Full method: https://www.aiagencies.eu/methodology · Rate index: https://www.aiagencies.eu/rates