# Algomine vs deepsense.ai

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

## Facts
| | Algomine | deepsense.ai |
|---|---|---|
| Editorial score | 64/100 | 64/100 |
| Hourly rate | €45-85/hr (directory-listed) | €85-130/hr (directory-listed) |
| Team size | 50-249 | 50-249 |
| Location | Warsaw, Poland | Warsaw, Poland |
| Founded | 2015 | 2014 |
| Last reviewed | 2026-09-09 | 2026-09-07 |
| Services | AI Consulting, AI Development, Generative AI | AI Consulting, AI Development, Generative AI |
| Industries | FinTech, Manufacturing, E-commerce | FinTech, Manufacturing, Healthcare |

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

The multi-agent engagement is the one that shows the shape of the practice. For a Swedish industrial group of more than 15,000 people operating in over a hundred countries, Algomine ran two parallel workstreams for more than two years: maintaining two production AI applications, a document analyzer and a configuration assistant for bespoke heat exchangers, while building the greenfield multi-agent platform meant to replace a hard-coded chatbot the client had bought elsewhere.

The reasons the client gives are worth reading, because they are the reasons enterprises actually change supplier. Siloed applications with no shared governance, rigid predefined workflows that made every new use case expensive, and AI spend concentrating inside a single vendor ecosystem.

Twenty case studies are published across generative research assistants, retail planogram generation, computer vision for pharmacy shelves and defect management in precision manufacturing. Eleven name their client, and the client list runs to PKO Bank Polski, mBank, Rossmann, Eurocash and Warta.

Algomine publishes no rate itself; the €45–85/h shown here comes from a third-party directory listing. The weaker fit is a buyer who needs a published outcome figure, because the studies explain architecture and engagement shape rather than reporting numbers.

### Key strengths
- The industrial engagement is documented as a two-year, two-workstream relationship covering legacy maintenance and greenfield platform work at the same time
- Vendor lock-in is treated as an architectural requirement, with the platform built to be model and vendor agnostic after the client's costs concentrated in one ecosystem
- Twenty published case studies with eleven naming the client, across banking, retail, pharmacy and precision manufacturing

### Good to know
- The flagship multi-agent client is anonymized despite being identifiable from the description—ask for a speakable reference on it
- The studies explain architecture rather than reporting results; ask what the platform changed in cost, cycle time or adoption
- Named clients like PKO Bank Polski and Rossmann carry no described engagement—ask what was delivered for the two closest to your sector

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

The published work is machine learning proper: a computer-vision system for WWF that mapped Poland's oxbow lakes using ResNet vectorization combined with location metadata, cutting a months-long manual process to hours, and a food-production quality system detecting topping defects and sauce smears at over 99% accuracy.

The team is described as 120 AI specialists doing applied research, scientific publications and open-source work alongside client delivery, with leadership named and their backgrounds published.

The pattern worth noting is naming: WWF is named, but the strongest industrial results belong to "one of the biggest foodservice companies" and "a world leader in mobile computing"—normal for enterprise machine learning, and it means your due diligence happens on a reference call rather than on the site.

Best for organizations whose problem has no obvious method—novel vision tasks, forecasting, model selection—rather than a fixed-scope automation, at a rate that reflects a research-capable bench.

### Key strengths
- Technique is named rather than implied: ResNet vectorization with location metadata on the WWF mapping, neural visual detection at over 99% accuracy on the food line
- 120 AI specialists with published research and open-source contributions alongside delivery, and named leadership with checkable backgrounds
- Founded 2014, which in this market means a decade of machine-learning delivery predating the language-model wave

### Good to know
- The strongest industrial outcomes are anonymized—ask for a speakable reference in your sector
- An LLM workshop sits among the published cases; separate the enablement engagements from the built systems when comparing
- €85–130/h is above the typical Polish band—confirm what a research-grade engagement costs against a scoped build

## Which to choose
Choose Algomine for:
- a more cost-effective engagement (~40% lower rate)
- disclosed specialization in E-commerce

Choose deepsense.ai for:
- disclosed specialization in Healthcare
- an overall editorial score of 64/100

## Questions buyers ask

### Which is cheaper, Algomine or deepsense.ai?
Algomine is cheaper by roughly 40% — €65/hr median vs €108/hr at deepsense.ai. Published rates cover different things at different agencies — seniority mix, discovery and included revisions are the usual variables — so treat the gap as a question to ask, not a verdict.

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

### Which scores higher overall, Algomine or deepsense.ai?
Both score equally well overall (64/100). The deciding factor is specialization — see the editorial quotes and Strengths sections above. Full methodology.

### When should I consider both Algomine and deepsense.ai?
Consider running parallel discovery briefs with Algomine and deepsense.ai 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