# Modulai

> Modulai is a reviewed AI agency in Stockholm, Sweden, listed in the AIAgencies.eu register and last assessed 2026-08-05.

Canonical page: https://www.aiagencies.eu/agency/modulai
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
- Location: Stockholm, Sweden
- Website: https://modulai.io
- Hourly rate: not published
- Team size: 10-49
- Founded: 2018
- Services: AI Consulting, AI Development, Generative AI, AI Agents
- Industries: FinTech, Healthcare, Manufacturing, E-commerce
- LinkedIn: https://www.linkedin.com/company/modulai

## About
Modulai is a Stockholm machine learning consultancy whose engineering teams take AI work from prototype to production, covering custom models, retrieval systems and multi-agent applications.

## Documented clients
Documented engagements: Novo Nordisk, Optilogic, Fuelmatics, Lindex, Castellum, Ramirent, Fishbrain, Abios Gaming, Tracy of Sweden, Synclair Vision, Digital Diabetes Analytics, OpenIR, Klarna, and Ahlsell. 12 of 14 carry a source URL on file.

## Curator assessment
Reviewed 2026-08-05 by Gabor Kiss against published evidence.

The most technically specific portfolio in this register, and the one where a reader with a machine-learning background learns the most.

The NovoNordisk work is a multi-agent retrieval system for clinical trial evidence that retrieves, analyzes and returns results in the right format while keeping traceability—the case index names the client, though the study itself describes it as one of Europe's largest pharmaceutical companies. For an unnamed American software company it built a Bayesian hierarchical logistic regression for small-business credit risk: a model choice, stated as such, in a market where most firms write 'AI-powered'. Fuelmatics needed a robotic arm to find a car's fuel lid in real time, so the detector was optimized to run on CPU rather than assuming a GPU budget. Optilogic got a custom agent embedded in its own supply-chain platform, turning plain language into SQL for users who would otherwise queue behind technical staff.

The published case index runs past a dozen engagements, and the engineering blog carries the same register: how to evaluate retrieval systems with synthetic data and an LLM judge, how a deep-research multi-agent system is built.

The gap is outcomes. Case after case describes what was built and stops, so the portfolio proves capability and never impact. Ask each reference what changed.

### Key strengths
- Names its models and its reasoning—Bayesian hierarchical regression, multimodal RAG, CPU-optimized detection—rather than describing capabilities
- Klarna and Novo Nordisk among named clients, which is a rare pairing of consumer fintech and regulated pharma
- Publishes method openly, including how it evaluates retrieval quality, so the engineering standard is checkable before you engage

### Good to know
- More than a dozen published cases and no outcome figure in any of them—this team documents how it works, not what it earned
- A 30-plus engineering team taking research-grade problems; confirm capacity and timeline before committing to a deadline

## Compared head-to-head
- Modulai vs Alice Labs: https://www.aiagencies.eu/compare/alice-labs-vs-modulai
- Modulai vs Walma: https://www.aiagencies.eu/compare/modulai-vs-walma

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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