# Dmlab vs HolistiCRM

> Two top-rated AI Consulting agencies in Budapest. 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/dmlab-vs-holisticrm
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Dmlab | HolistiCRM |
|---|---|---|
| Editorial score | 61.8/100 | 57.8/100 |
| Hourly rate | not published | €20-40/hr (directory-listed) |
| Team size | 10-49 | 2-9 |
| Location | Budapest, Hungary | Budapest, Hungary |
| Founded | 2007 | 2014 |
| Last reviewed | 2026-09-09 | 2026-09-09 |
| Services | AI Consulting, AI Development | AI Automation, AI Consulting, AI Development |
| Industries | FinTech, Manufacturing, E-commerce | FinTech, E-commerce, SaaS & B2B |

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

The energy-trading system is the one to read, and it is a full operational platform rather than a model handed over. Built for Central Energy Trade Hungary Group, it collects production, consumption and weather data in real time, forecasts solar-farm output and customer demand with machine learning, trades on the power exchange against those forecasts, issues the invoices, and raises an alert when it stops behaving.

The business case is stated the way an operator states it: better forecasts mean less of the expensive balancing energy bought to cover a shortfall. The second published engagement, automated campaign-performance reporting for the Publicis-owned media agency MMS Communication, replaced a monthly Excel cycle rather than applying a model.

Eighteen years in Budapest and a client list running to Egis, Waberer's, K&H and Takarékbank, though only these two engagements are written up.

The weaker fit is a buyer who needs English-language documentation or a supplier outside Hungary, since everything here is published in Hungarian.

### Key strengths
- The energy platform is documented as an operating system, not a model: real-time ingestion, forecasting, exchange trading against the forecast, invoicing, monitoring and alerting
- The economic mechanism is named—forecast accuracy reduces the balancing energy bought to cover a shortfall—rather than left as efficiency language
- Trading since 2007, with clients across pharmaceutical manufacturing, logistics and banking on the reference list

### Good to know
- Two engagements are written up against a much longer client list—ask what was delivered for Egis, Waberer's or K&H
- Neither case study publishes a figure; ask what the forecast error was before and after, since that is where the value sits
- Everything is published in Hungarian and no rate is listed—confirm working language and pricing before scoping

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

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

## Which to choose
Choose Dmlab for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in Manufacturing
- a higher overall editorial score

Choose HolistiCRM for:
- closer collaboration at a smaller team scale
- disclosed specialization in SaaS & B2B
- broader service offering — also covers AI Automation

## Questions buyers ask

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

### Which scores higher overall, Dmlab or HolistiCRM?
Dmlab scores 61.8/100, 4 points higher than HolistiCRM at 57.8/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.

### When should I consider both Dmlab and HolistiCRM?
Consider running parallel discovery briefs with Dmlab and HolistiCRM 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