diff --git a/Readme.md b/Readme.md
index 93dfe360..b3d58fd6 100644
--- a/Readme.md
+++ b/Readme.md
@@ -29,25 +29,24 @@ See below a suggested product roadmap [subject to change] that provides high-lev
 
 ![fAIr1](https://github.com/hotosm/fAIr/assets/97789856/01c0e3b6-a00c-439d-a2ed-1c14b62e6364)
 
-1. Project Area by tha project manager and imagery from Open Areal Map is submitted to the task manager which then is sent to Open Street Map after undergoing the process of manual mapping and validation.
-2. Local dataset(created using the imagery and raw API data as inputs) is trained and local model is created and trained.
-3. It is then validated , published , mapped and pushed back into Open Street Map.
-4. Finally according to the feedback , the published model is sent for improvement and training .
+1. First We expect there should be a fully mapped and validated task in project Area where model will be trained on 
+2. fAIr uses OSM features as labels which are fetched from [Raw Data API] (https://github.com/hotosm/raw-data-api) and Tiles from OpenAerialMap (https://map.openaerialmap.org/)
+4. Once data is ready fAIr supports creation of local model with the input area provided , Publishes model for that area which can be implemented on the rest of the similar area 
+5. Feedback is important aspect , If mappers is not satisfied with the prediction that fAIr is making they can submit their feedback and community manager can apply feedback to model so that model will learn 
 <hr>
 
 # fAIr Architecture
 ![fAIr2](https://github.com/hotosm/fAIr/assets/97789856/63394f65-ce0d-4a3d-8683-7455f14fb366)
 
-1. Third party extensions are sent to fAIr backend which then generates data for OSM raw data API , osmconflator and geoson2osm xml .
-2. Data from fAIr backend is sent to fAIr utilities . The backend is using s separate library we call it fAIr-utilities to handle:
+  The backend is using library we call it [fAIr utilities](https://github.com/hotosm/fAIr-utilities) to handle:
 
      1. Data preparation for the models
-     2. models trainings
-     3. inference process
-     4. post processing (converting the predicted features to geo data)
-3. The public API is then sent to the fAIr frontend.
-
+     2. Models trainings
+     3. Inference process
+     4. Post processing (converting the predicted features to geo data)
 
 
 
+## Local Installation [DEV]
 
+Checkout Docker Installation [docs](./docs/Docker-installation.md)