# Datamole vs DataSentics

> Two top-rated AI Development agencies in Prague. 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/datamole-vs-datasentics
Generated: 2026-09-20 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Datamole | DataSentics |
|---|---|---|
| Editorial score | 52.8/100 | 74.8/100 |
| Hourly rate | not published | not published |
| Team size | 50-249 | 50-249 |
| Location | Prague, Czech Republic | Prague, Czech Republic |
| Founded | 2015 | 2016 |
| Last reviewed | 2026-09-20 | 2026-09-03 |
| Services | AI Development, AI Consulting, AI Automation | AI Development, AI Consulting, Generative AI |
| Industries | Manufacturing, Healthcare, SaaS & B2B | FinTech, E-commerce, Healthcare |

## Our take on Datamole
Reviewed 2026-09-20 by Gabor Kiss against published evidence.

The deepest published work is all one client: Lely's milking-routine personalization calculates an optimal treatment per cow on every robot visit, fed by Datamole's IIoT platform collecting from more than 30,000 robots worldwide across 5 million daily milking sessions, and the write-up reports up to 80 percent fewer non-optimal milkings and 0.2 kg more milk per robot-minute.

The Lely Service Support App is the second full write-up and scaled from one service center to more than 200 centers and 2,000 regular users over eight years of collaboration, though it is service analytics and an app rather than a model in the loop.

Beyond those, the case index is two-sentence summaries—Agrifac sprayer telemetry, Cup&Cino device data, Thermo Fisher Scientific microscope condition monitoring—and the third full page, Calf2Cow, is a Eurostars-2 and Horizon 2020 co-funded project run with Triodor and Lely rather than a client engagement, so ask for the list behind the "100+ successful projects" and ">80% of projects deployed" figures.

The weaker fit is a buyer shopping for generative or agentic AI—nothing published describes LLM work, and the visible depth is industrial IoT, predictive models and the applications built on top of them.

### Key strengths
- Testimonials are attributed by name and title—Marc Biermann, CTO at Cup&Cino; Ondřej Krupka, Product Owner at Thermo Fisher Scientific; Rik Steenberger, Senior Product Engineer at Lely
- Engagements run long rather than one-off: the Lely service app is documented across eight years, and the about page states most customers form a multiyear collaboration
- Registered Czech entity (company number 03742709) with a named CEO and CTO who both hold technical leads, plus a Datamole AI & IoT laboratory at CTU Prague's FIT faculty

### Good to know
- Client concentration is the first thing to probe—four of the eight case-index entries are Lely; ask for two speakable references outside agriculture before shortlisting
- No rate is published on the site—ask for a day rate and the team composition behind it early, and check them against the European range on our rate index
- No case study names a model, framework or cloud stack—ask for an architecture walkthrough and for who operates and retrains the models after handover

## Our take on DataSentics
Reviewed 2026-09-03 by Gabor Kiss against published evidence.

DataSentics documents five named production deployments with the stack disclosed down to the model: a LightGBM fraud-detection model live on Databricks and AWS for 2.35 million Esure customers, and an XGBoost and Elasticsearch product-matching pipeline processing tens of millions of Heureka offers a day at over 98% precision.

A Databricks-based personalization system is credited with 1,000 additional advisor meetings at Česká Spořitelna (Erste Group) in three months, and the story is co-published on Databricks' own customer-reference site, independent corroboration of the numbers.

The Seznam.cz case study goes further and documents the MLOps platform itself—feature store, MLflow model registry, CI-triggered retraining—rather than a single model, which is rarer to see published.

The weaker fit is smaller companies without an internal data-science counterpart—every documented engagement pairs DataSentics engineers with the client's own data team, and the published work assumes that counterpart exists.

### Key strengths
- Every case study lists the named engineer or lead behind the work, with a direct email and phone—Petr Dvořák, David Vopelka and Ondřej Havlíček among them.
- Databricks Elite Consulting Partner status is independent, verifiable corroboration of the platform expertise behind these case studies, not a self-reported claim.
- The Nestlé shelf-monitoring model runs at over 97% detection accuracy inside a live mobile app used by field sales reps, not a lab benchmark.

### Good to know
- Confirm which legal entity signs the contract—DataSentics now operates inside the Eviden/Bull group rather than as an independent boutique.
- Ask for a reference case outside retail, insurance and banking—documented deployments cluster tightly there, plus one internal media-platform MLOps project.
- No rate is published on site—benchmark against the European range on our rate index before assuming group-vendor pricing.

## Which to choose
Choose Datamole for:
- disclosed specialization in Manufacturing / SaaS & B2B
- broader service offering — also covers AI Automation

Choose DataSentics for:
- disclosed specialization in FinTech / E-commerce
- broader service offering — also covers Generative AI
- a higher overall editorial score

## Questions buyers ask

### Which has more Healthcare experience, Datamole or DataSentics?
Both agencies show documented Healthcare work and have similar industry breadth. Compare directly on the agency profiles: Datamole and DataSentics.

### Which scores higher overall, Datamole or DataSentics?
DataSentics scores 74.8/100, 22 points higher than Datamole at 52.8/100. See our methodology for how scores are calculated.

### When should I consider both Datamole and DataSentics?
Consider running parallel discovery briefs with Datamole and DataSentics 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.

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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