Clients Stories

See how NorthGravity improves data workflows and decisions.
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From Historical Data to Market Advantage – The OBX Data Success Story

With NorthGravity’s end-to-end data curation and delivery, OBX Data transformed years of historical records into structured insights – fueling faster product launches and measurable growth.

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Flexibility and Scale: How NorthGravity Helped ECTP Analyze 9 PB of Data in Under a Month

Together with ECTP, we built a flexible platform integrating image analytics with Python and R workflows – delivering pre-trade prescriptive results in record time.

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How Alternative Data and NLP Helped Forecast Oil Price Movements

With NorthGravity’s end-to-end data curation and delivery, OBX Data transformed years of historical records into structured insights – fueling faster product launches and measurable growth.

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Forecasting Yields Ahead of the Market – The Global Agri Major Success

By integrating weather data, satellite images, and client ML models, NorthGravity helped produce over 10,000 weekly predictions, enabling accurate yield forecasts across North America.

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WHAT WE PROVIDE

Our platform handles every stage of the data process.
with ng platform
total effort
1 Day
Resources
1 Business User
without ng platform
total effort
60+ Days minimum
Resources
4 resources: Business User, Data Scientist, Data Engineer, Cloud Engineer
Data
  • Select input data
  • Use Pre-built ELT frameworks
  • Use current Notebooks or Python code
Data (data engineer)
  • Setup framework to download data
  • Setup frameworks to translate data
  • Create QA and retry logic
  • Create a monoting
  • Add a scheduler
DataBase
  • Use out of the box data lake and structured storage
DataBase (cloud engineer)
  • Create a data lake
  • Create a database
machine learning
  • Use pre-built Features, ML, and Backtesting Task
machine learning (Data Scientist)
  • Build ML process  
  • Test ML process  
Move to production
  • Save the data flow pipeline to production
  • Schedule or trigger to run
Move to production (ML/Dev ops or Cloud Engineer)
  • Setup cloud framework
  • Move data flow pipeline to production 
quality monitoring
  • Use pre-built data, model, and pipeline quality monitoring
quality monitoring (ML/Dev ops)
  • Create:
  • Data quality monitoring
  • Model quality monitoring
  • Pipeline quality monitoring
Governance
  • Configure access rights
  • Review user logs and version
Governance (Cloud Engineer)
  • Create:
  • Access rights
  • Process to record user logs
  • Process to record version

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