Scope-3-AnalyseRare
Used by 1 of 2.863 companies (0.0%)
Tech-Stack
Tech stack in context
Each entry is benchmarked against all 2.863 companies in the dataset. Rare items first — those are the more interesting choices.
RareUncommonCommonMainstream
Statistical ModelsRare
Used by 1 of 2.863 companies (0.0%)
AIUncommon
Used by 309 of 2.863 companies (10.8%)
MLUncommon
Used by 323 of 2.863 companies (11.3%)
PythonCommon
Used by 1.127 of 2.863 companies (39.4%)
In context
Raw numbers say little — here's how ctrl+s compares to its industry and city.
Industry peers
30
30 companies in the same industry
7 of those in Berlin
Similar companies
Similar companies
Same industry
Competitors and peers
- IONITYMünchencleantechGoKotlinKubernetesOCPP
- 1KOMMA5°HamburgcleantechIoTSmart EnergyHeartbeat AISolar
- The Mobility HouseMünchencleantechPythonGoJavaKubernetes
- InstagridStuttgartcleantechEmbedded SystemsPythonFirmwareIoT
- Solar MaterialsMagdeburgcleantechPythonIoTRecycling-Technologie
- Feldwerke GmbHMünchencleantechIoTSolarTracking-Systeme
Similar stack
At least 2 shared technologies
- 2NA Fish3 shared techsMünchenBioTech / Cancer Diagnostics / Spatial TranscriptomicsDNA-NanotechnologyAIMLSpatial Transcriptomics
- Abaut3 shared techsMünchenai / constructionPythonComputer VisionSensor DataAI
- aiomatic (ai-omatic solutions GmbH)3 shared techsHamburgai / industrialtechPythonAIMLPredictive Maintenance
- Alcemy3 shared techsBerlincleantech / aiPythonAIMLPredictive Analytics
- Algorized3 shared techsrobotics / aiPythonROSAIML
- amaiko3 shared techsViechtachaiPythonAIMLNLP
Same location
Local tech ecosystem
Consulting on this stack
Working with a similar stack?
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