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# System prepended metadata

title: Ambee V3.0 PRD

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> # Ambee V3.0 PRD

### PREFACE

There are majorly 4 main parts to the Climate-tech market (keeping renewable energy companies aside) 
1. Risk (finance, insurance) & Resilience
2. ESG (consulting+reporting etc) & Emissions/Carbon accounting and trading
3. Climate Intelligence & forecasts
4. GIS, EOS, Research, Infra & City planning

Every company solving any problem in climate tech can be categorised in the above 4 buckets. This market alone is sized to be over several trillions of dollars, meaning any and all of them are significantly big as a market. That means there are problems / opportunities and gaps that need to be solved.

for example, take the $4.8 billion Earth Observation industry, it continues to grow and progress with each passing day. With over 6000+ satellites orbiting our Earth, it is safe to say that space conquest is well and truly underway.

But what kind of gap do exist in this market? to be more accurate, most have only really aced the part that involves sending satellites to space and bringing them into orbit. Realistically speaking, is that the end goal? Far from it! Getting satellites into orbit is just the means to acquire the valuable Earth Observation data and apply it to improve humanity.
However the Satellites are not enough, they have their own shortcomings. Ground stations take up a bit of the EO. When we combine both there is definitely usable data, however, that too is not enough. 

Read on

### The Crucial Last Mile - Data Delivery and Integration
We launched the satellite into space, we got the valuable data back into the ground stations, processed it into analytic ready content, and eventually created beautiful data products and solutions out of it. 

But what good is any of that if data products can’t reach their end user, seamlessly and on time? Believe it or not, but the problem of data delivery and data integration is a lot bigger than you think.

Many companies have built their own APIs. That is a great step to creating an access point. But often, just an API doesn’t cut it. 

A delivery method that is fit for purpose for both the use case and the user persona is the need of the hour. And that is a tall order considering the broad market that NewSpace services speak to! However, no expenses have been spared from the start of the mission. Then why compromise in this last part which is on the critical path to success?

Processes such as data integration, data migration, data warehousing, and data wrangling all may involve data transformation. Data formats, data types, data compressions, they all get in the way and stakeholders end up spending more time cleaning and structuring the data than getting value out of it.

This is where Ambee can come in. 

Further, take Risk as a sector. They need a lot of accurate & verified data, developer tools and cloud to assess risk. Ambee can provide them the data and tools.

**Improving technology**

New Space, Web3, Blockchain, 5G, Data Storage, Cloud, AI & ML are all adding momentum to Climate Tech growth.
The space industry should reach $1 trillion in annual revenue by 2040, with launch costs dropping 95%, Citigroup analysts said in an extensive report that was recently published.

Citi’s estimates for the industry match forecasts published in recent years by Morgan Stanley, Bank of America and others. The global space economy’s value reached $424 billion in 2020, according to research from Space Foundation, having expanded 70% since 2010.

![](https://hackmd.io/_uploads/rJdpkOUn9.png)


**Introducing Ambee.io API First Last Mile Data Delivery and Application Climate Platform**

Takes all EO data + proprietary ground sensor data, aggregates it to various applications for multiple use cases in the following sectors. 

1. Advertising - Priority 1
1. Agriculture - SFD
2. Analytics (climate anlytics, dashboards, heatmaps)
3. Civil Government
4. City Planning - Emissions.world
5. Data Science - Gspatial.ai
6. Defense & Intelligence
7. Drought Response
8. Science Programs
9. Emissions and Carbon Accounting
10. Energy & Infrastructure
11. Finance & Insurance
12. Forestry & Land Use
13. Mapping
14. Maritime
15. Pharma
16. Risk
17. Sustainability 
18. Tools & SDKs

**Cloud Applications that we should build**

1. Climate Intelligence Platform (Weather, AQ, Fire, Pollen, map with analytics, routing & embeddable widgets with APIs). Call to Action - Equip Your Teams with Predictive and Actionable Insights. - Improve operational efficiency, Predictive analytics for Energy infra and preventive maintenance, automate risk management and customize resiliency plans to meet sustainability goals). (PS: Separate PRD will be made) **Flagship 1**
2. Codeless AI & Data driven Digital advertising (see pixis) **Flagship 2**
3.  AutoML  **Flagship 3**
4.  Codeless Build your own climate/weather/pollen/AQ/FF app, heatmap, carbon calculator & more (see builder.ai)  **Future Flagship 4**
5.  GHG Emissions calculator and Offsets (WIP)
6.  APIs (current and growing)
7.  Sat images, data and cloud storage (Gspatial)
8.  Hourly Emissions
9.  Environmental Widgets (widgets for pollen, AQ, weather & FF)
10.  SDKs for app development
11. Planet Glance - simulation of world 2030
12. Climate Forecast data (2023)
13. Interactive Heatmap
14. Enterprise Climate Planning Tool - SFD rebranded for non-agri like BFSI, Insurance etc., API first
15. Mobile app
16. APIs
17. NET ZERO CLOUD SOLUTIONS - TBD - ZERO EMISSIONS CLOUD DATA CENTER. ANYTHING YOU WANT TO BUILD IS NET ZERO!


**UN Environment has identified six major categories where artificial intelligence can contribute to climate action:** ([Source](https://unfccc.int/ttclear/misc_/StaticFiles/gnwoerk_static/tn_meetings/ef83c3e2cbdc4b16a525a60e3d53014c/b5eb84f99ff845878db9eb7c9e2a2660.pdf))
1. Automated dection and monitoring (emissions; RE potential;
climate-related hazards);
2. Risk assessments and impact modelling (security, species/ecosystem
distribution; insurance)
3. Predictive analytics, forecasting and scenarios for decision-support
(solar/clouds; temperature; agriculture; water; air quality);
4. Optimization of energy and materials use (Smart cities, agriculture,
electrical grids/load management; product design; supply chains on
carbon intensity; oil and gas reserve);
5. Consumer awareness and behavior nudging (calculation of carbon
footprint; peak load periods);
6. Quality control (Fake news/fake data; hackers and gaming the
system).
It has been estimated, for instance, that ‘using AI for environmental
applications could boost the global economy by up to USD 5.2 trillion
in 2030, a 4.4 per cent increase on the business-as-usual scenario.
ML and AI will require large amounts of computing power .
Decarbonization of the energy system to ensure that AI and ML can
fulfil their sustainability potential is a crucial factor. Studies are
showing that typical current ML processes can ‘emit more than 626
000 pounds of carbon dioxide equivalent (CO2e) – nearly five times
the lifetime emissions of the average American car (and this includes
manufacture of the car itself.























