diff --git a/main.bib b/main.bib
index 79ca985..ab06879 100644
--- a/main.bib
+++ b/main.bib
@@ -4,8 +4,15 @@
 	year	={2023},
 	url	={https://cloud.google.com/vision?hl=en}
 }
-
-@article{amazon-rekognition,
+@misc{amazon-rekognition,
+	title = {{What Is Amazon Rekognition? (1:42)}},
+	journal = {Amazon Web Services, Inc},
+	year = {2023},
+	month = dec,
+	note = {[Online; accessed 18. Dec. 2023]},
+	url = {https://aws.amazon.com/rekognition}
+}
+@article{,
 	author	={Amazon},
 	title	={Image Recognition Software - {ML} Image \& Video Analysis - Amazon Rekognition - AWS},
 	year	={2023},
@@ -109,3 +116,18 @@ author={
 	note = {[Online; accessed 5. Nov. 2023]},
 	url = {https://www.loginradius.com/blog/engineering/a-journey-from-node-to-golang}
 }
+@misc{amazon-machine-learning,
+	title = {{An overview of AI and machine learning services from AWS}},
+	journal = {Amazon Web Services, Inc},
+	year = {2023},
+	month = dec,
+	note = {[Online; accessed 18. Dec. 2023]},
+	url = {https://aws.amazon.com/machine-learning}
+}
+@misc{amazon-rekognition-custom-labels,
+	title = {{What is Amazon Rekognition Custom Labels? - Rekognition}},
+	year = {2023},
+	month = dec,
+	note = {[Online; accessed 18. Dec. 2023]},
+	url = {https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/what-is.html?pg=ln&sec=ft}
+}
diff --git a/report/report.tex b/report/report.tex
index 6ae674f..03c572b 100644
--- a/report/report.tex
+++ b/report/report.tex
@@ -79,9 +79,27 @@
             \item Create a system to automatically to merge modules to increase efficiency.
             \item Create a system to distribute the load of training the model's among  multiple services.
         \end{itemize}
+
     \section{Literature and Techincal Review}
     % 1 page of background and literature review. Here you will need to references things. Gamal et al.~\cite{gamal} introduce the concept of \ldots
 
+		\subsection{Intruduction}
+			This section reviews current existing thechnologies in the market that do image classification. It also reviews current image classification technologies, and which meats the requirements fot the project. This review also analysis methods that are use to distrubute the learning between various machines, and how to spread the load so miminum reloading of the models is required when running the model.
+
+		\subsection{Current existing classification platforms}
+			There are currently some existing software as a service(SaaS) platfomrs that do provide similar services to the ones this will project will be providing.
+
+			%Amazon provides bespoque machine learning services that if were contacted would be able to provide image classification services. Amazon provides general machine learning services \cite{amazon-machine-learning}.
+
+			Amazon provides an image classification service called rekognition \cite{amazon-rekognition}. This services provides multiple services from face regonition, celebrity regonition, object regonition and others. One of this services is called custom labels \cite{amazon-rekognition-custom-labels} which provides the most similiar service, to the one this project is about. The custom labels service allows the users to provide custom datasets and labels and using AutoML the rekognition service would generate a model that allows the users to classify images acording to the generated model. 
+
+			%https://aws.amazon.com/machine-learning/ml-use-cases/
+
+			%https://aws.amazon.com/rekognition/image-features/
+
+			
+			
+
     	\subsection{Alternatives to my Project}
     		There currently exist systems that do image classification, like Google Vision AI \cite{google-vision-api}, and Amazon's Rekoginition \cite{amazon-rekognition}. 
     		Their tools, while providing similar services to what my project is supposed to do, it mostly focusses on general image classification rather than specific image classification, i.e. Car vs Boat, vs, Car model X vs Car model Y.