# deepsense.ai vs Sparkbit

> 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/deepsense-ai-vs-sparkbit
Generated: 2026-09-23 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | deepsense.ai | Sparkbit |
|---|---|---|
| Editorial score | 64/100 | 64/100 |
| Hourly rate | €85-130/hr (directory-listed) | €85-130/hr (directory-listed) |
| Team size | 50-249 | 10-49 |
| Location | Warsaw, Poland | Warsaw, Poland |
| Founded | 2014 | 2014 |
| Last reviewed | 2026-09-07 | 2026-09-23 |
| Services | AI Consulting, AI Development, Generative AI | AI Consulting, AI Development |
| Industries | FinTech, Manufacturing, Healthcare | Healthcare, Manufacturing, E-commerce |

## 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

## Our take on Sparkbit
Reviewed 2026-09-23 by Gabor Kiss against published evidence.

Sparkbit publishes ML case studies with the model, the metric and the handover spelled out: for 5x5 Technologies it retrained a YOLOv8 tower-equipment detector from F1 0.72 to 0.86 over a 12-month engagement on a telecom digital-twin platform, and for the New York health-tech startup phy it built a posture-analysis core covering 20+ conditions whose algorithms went into the client's FDA application.

The studies also show where work stopped: the anti-counterfeit label project ended after discovery when the findings argued against continuing, and the context-aware telematics system was a grant-funded R&D program whose hardware rollout, the study says, was held back by the supply chain.

Most dated AI work falls in 2020–2022; the LLM-era record is an anonymized RAG assistant over 7,000+ PDFs for a Swiss device manufacturer (2025), described as in production, and an Italist data-quality pipeline whose GPT-4 stack sits under a 2020–2022 label.

The weaker fit is buyers who need business outcomes measured in money or hours—results here are model metrics and qualitative gains—or a named reference for a recent LLM deployment.

### Key strengths
- Model metrics are published with the stack: F1 0.72 to 0.86 at 5x5, about 0.85–0.95 F1 on key telematics perception functions, and ~8k concurrent users sustained with under 0.002% timeouts
- Five client executives are quoted by name and title—including 5x5 Technologies' CEO, Italist's CTO and Spicerr's CEO—four of them on the case study their quote refers to
- The services page lists what is handed over—repos, infrastructure-as-code, test suite, dashboards, runbooks and IP assignment—and the 5x5 study reports that handover took place

### Good to know
- No founder or engineer is named anywhere on the site—ask who would lead your project and to see that person's prior ML work
- The Italist study lists GPT-4 while the index dates it 2020–2022 and files it under Health-tech—ask for the dates and scope of the LLM work specifically
- Outcomes such as "duplicates down" and "operational savings" carry no figures—ask each reference what the system changed in hours, cost or error rate

## Which to choose
Choose deepsense.ai for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in FinTech
- broader service offering — also covers Generative AI

Choose Sparkbit for:
- closer collaboration at a smaller team scale
- disclosed specialization in E-commerce

## Questions buyers ask

### Which is cheaper, deepsense.ai or Sparkbit?
Pricing is comparable: deepsense.ai at €108/hr median vs Sparkbit at €108/hr. Choose on specialization rather than cost.

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

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

### Which is faster to engage, deepsense.ai or Sparkbit?
Neither publishes lead times, so this is a read on team size rather than a measured answer. Sparkbit runs the smaller team, which usually means fewer procurement gates and a shorter path to kickoff. deepsense.ai runs a larger one, which tends to mean more steps but more capacity to start parallel workstreams. Current kickoff availability is the number that actually decides it — ask both.

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