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#3 of 11 AI agencies in Budapest

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HolistiCRM

Budapest, Hungary

2-9€20-40/hrFounded 2014

About

HolistiCRM is a Budapest machine-learning consultancy building propensity, churn and next-best-offer models and location analytics for banks, telecom operators and retail groups.

Who They Work With

Banks, retailers and utilities that want a propensity or next-best-offer model on their own data, scored on a business rate.

Engagements:Retainer

Based on 7 client engagements with a source URL · researched 27 Sept 2026

Our Take

Seven named enterprise engagements, each with the stack and the number attached. Location models for MolGroup picked the retail format for every service station from a few hundred geospatial features. Binary classifiers and a recommender for Delivery Hero's Hungarian brand moved open rates 250 percent and click-throughs 350 percent. Affinity models for Citibank's retail bank improved the booking rate more than sixfold; a response model for a utility improved it 17 percent; OTP Bank has branch-visit prediction and Rossmann daily sales prediction. The stack is the same throughout: Azure Machine Learning, Data Factory and Power BI.

The thing to establish before anything else is who delivered it. The engagements are bank and retail scale, the team is under ten people, and the company LinkedIn link resolves to a personal profile.

Everything sits on one references page with no dates and no individual write-ups, so the sequence and the recency are not visible.

HolistiCRM publishes no rate itself; the €20–40/h shown here comes from a third-party directory listing, and it is far below the European range on our rate index for work of this kind.

Key strengths

  • Seven named enterprise clients with a measured result on each—open rate, click-through, booking rate, prediction accuracy—rather than capability claims
  • The stack is stated consistently across engagements: Azure Machine Learning, Azure Data Factory and Power BI, with automated retraining described
  • The problems are commercially specific: retail format selection from geospatial features, branch-visit prediction, next-best-offer, churn-based retention

Good to know

  • Bank and retail-scale engagements against a team under ten and a company link that resolves to a personal profile—ask which were delivered by this firm and which by its founder elsewhere
  • All seven sit on one references page with no dates and no individual studies; ask when each ran and what is still in production
  • The directory rate sits far below the European range on our rate index for enterprise model work—ask who does the delivery and where
Gabor Kiss

Reviewed by Gabor Kiss

Founder & Curator, AIAgencies.eu · 9 Sept 2026

Based on the agency's publicly available portfolio. Own this agency? Claim your profile to provide additional context or request a review update.


How we evaluate agencies

Notable Clients

Every engagement documented in a published case study

Documented engagements: Delivery Hero (Netpincér), Citibank, MolGroup, OTP Bank, Rossmann, Mobiliti, and Energia Hungary. All 7 carry a source URL on file.

Delivery Hero (Netpincér)Binary classification models for email campaign response, extended into a recommender with geospatial indexes: 250 percent open-rate increase and 350 percent click-through increase, with automated retraining and scoringSource
CitibankCustomer affinity models for cross-sell in retail banking, reported at more than sixfold booking-rate improvement, alongside a churn-probability model for credit-card retentionSource
MolGroupMachine-learning selection of the right retail format for each service station from several hundred geospatial features, including catchment-area and competition analysisSource
OTP BankCustomer-level branch-visit prediction from transaction data and geospatial featuresSource
RossmannDaily store sales prediction, reported at a 5 percent accuracy improvement once external data was addedSource
MobilitiModel predicting the performance of candidate electric-vehicle charging locations before deploymentSource
Energia HungaryModels predicting utility customers' response to insurance offers, reported at a 17 percent booking-rate improvementSource

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