Agency Comparison
deepsense.ai vs Sparkbit
Two top-rated AI Consulting agencies in Warsaw.
Comparison updated · Newest review
Side by side
Published data on both agencies. Where one side measurably differs, the dot marks it: the higher score, the lower published rate, the larger team, the more recent review. Which of those is an advantage depends on your brief.
| Attribute | deepsense.ai | Sparkbit |
|---|---|---|
| Editorial score | 64/100 | 64/100 |
| Hourly rate | €85-130/hr | €85-130/hr |
| Team size | Larger team: 50-249 | 10-49 |
| Location | Warsaw, Poland | Warsaw, Poland |
| Founded | 2014 | 2014 |
| Last reviewed | Sep 7, 2026 | More recent review: Sep 23, 2026 |
What we said about each
Our take on deepsense.ai
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…
Reviewed Sep 7, 2026
Read the full assessment →Our take on Sparkbit
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.
Reviewed Sep 23, 2026
Read the full assessment →Best for
Where each agency measurably leads on the published data — team capacity, declared specializations, editorial scoring, and rates where both publish them. Use these to match an agency to your project priorities.
Choose deepsense.ai if you need
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in FinTech
- broader service offering — also covers Generative AI
Choose Sparkbit if you need
- closer collaboration at a smaller team scale
- disclosed specialization in E-commerce
Strengths and watch-outs
Both upsides and risks, straight from our editorial assessments.
deepsense.ai
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
Watch-outs
- 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
Sparkbit
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
Watch-outs
- 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
Service coverage
Where the two overlap, and where each covers ground the other does not.
Industry coverage
Where the two overlap, and where each covers ground the other does not.
Pricing math
What a 12-week engagement at one full-time equivalent (~480 hours) costs at each agency’s published rate. An arithmetic projection, not a quote — real scopes move with seniority mix and team shape.
deepsense.ai
Rate: €85-130/hr
€40,800–€62,400
Sparkbit
Rate: €85-130/hr
€40,800–€62,400
Published midpoints are within 5% of each other — at this scope the difference is smaller than the estimate's own error bars. Decide on specialization, not cost.
Frequently asked questions
- 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.
Read the full reviews
A side-by-side is a starting point. The full assessments carry the portfolio analysis, documented engagements, and the evidence behind every strength and watch-out above.