## Inspiration

As the world grapples with challenges like climate change, resource depletion, and social inequality, it has become imperative for organizations to not only understand their environmental, social, and governance (ESG) impacts but also to benchmark and improve upon them. However, one of the most significant hurdles in this endeavor is the complexity and inaccessibility of sustainability data, which is often buried in lengthy official reports and varied formats, making it challenging for stakeholders to extract actionable insights.

Recognizing the potential of AI to transform this landscape, we envision Oasis as a solution to democratize access to sustainability data, enabling more informed decision-making and fostering a culture of continuous improvement toward global sustainability goals. By conversing with AI agents, companies are able to collaborate in real-time to gain deeper insights and work towards solutions.

## What it does

Oasis is a groundbreaking platform that leverages AI agents to streamline the parsing, indexing, and analysis of sustainability data from official government and corporate ESG reports. It provides an interface for companies to assess their records and converse with an AI agent that has access to their sustainability data. The agent helps them benchmark their practices against practices of similar companies and narrow down ways that they can improve through conversation.

Companies can effortlessly benchmark their current sustainability practices, assess their current standings, and receive tailored suggestions for enhancing their sustainability efforts. Whether it's identifying areas for improvement, tracking progress over time, or comparing practices against industry standards, Oasis offers a comprehensive suite of features to empower organizations in their sustainability journey.

## How we built it

Oasis uses a sophisticated blend of the following:

1. LLM (LLaMA 2) parsing to parse data from complex reports.
We fine-tuned an instance of `meta-llama/Llama-2-7b-chat-hf` on the HuggingFace dataset [Government Report Summarization](https://huggingface.co/datasets/ccdv/govreport-summarization) using MonsterAPI. We use this model to parse data points from ESG PDF text, since these documents are in a non-standard format, into a JSON format. LLMs are incredibly powerful at extracting key information and summarization, which is why we see such a strong use case here.
2. Open-source text embedding model (SentenceTransformers) to index data including metrics and data points within a vector database.
LLM-parsed data points contain key descriptors. We use an embedding model to index these descriptors in semantic space, allowing us to compare similar metrics across companies. Two key points may not have the same descriptions, but are semantically similar, which is why indexing with embeddings is beneficial. We use the SentenceTransformer model `msmarco-bert-base-dot-v5` for text embeddings.
We also use the InterSystems IRIS Data Platform to store embedding vectors, on top of the LangChain framework. This is useful for finding similar metrics across different companies and also for RAG, as discussed next.
3. Retrieval augmented generation (RAG) to incorporate relevant metrics and data points into conversation
To enable users to converse with the agent and inspect and make decisions based on real data, we use RAG integrated with our IRIS vector database, running on the LangChain framework. We have a frontend UI for interacting with our agent in real time.
4. Embedding similarity to semantically align data points for benchmarking across companies
Our frontend UI also presents key metrics for benchmarking a user’s company. It uses embedding similarity to find company metrics and relevant metrics from other companies.

## Challenges we ran into

One of the most challenging parts of the project was prompting the LLM and running numerous experiments until the LLM output matched what was expected. Since LLMs are non deterministic in nature and we required outputs in a consistent JSON form (for parsed results), we needed to prompt the LLM and reinforce the constraints multiple times. This was a valuable lesson that helped us learn how to leverage LLMs in intricate ways for niche applications.

## Accomplishments that we're proud of

We are incredibly proud of developing a platform that not only addresses a critical global challenge but does so with a level of sophistication and accessibility that sets a new standard in the field. Successfully training AI models to navigate the complexities of ESG reports marks a significant technical achievement. The ability to turn dense reports into clear, actionable insights represents a leap forward in sustainability practice.

## What we learned

Throughout the process of building Oasis, we learned the importance of interdisciplinary collaboration in tackling complex problems. Combining AI and sustainability expertise was crucial in understanding both the technical and domain-specific challenges. We also gained insights into the practical applications of AI in real-world scenarios, particularly in how NLP and machine learning can be leveraged to extract and analyze data from unstructured sources. The iterative process of testing and feedback was invaluable, teaching us that user experience is as important as the underlying technology in creating impactful solutions.

## What's next for Oasis

The journey for Oasis is just beginning. Our next steps involve expanding the corpus of sustainability reports to cover a broader range of industries and geographies, enhancing the platform's global applicability. We are also exploring the integration of predictive analytics to offer forward-looking insights, enabling users to not just assess their current practices but also to anticipate future trends and challenges. Collaborating with sustainability experts and organizations will remain a priority, as their insights will help refine our models and ensure that Oasis continues to meet the evolving needs of its users. Ultimately, we aim to make Oasis a cornerstone in the global effort towards more sustainable practices, driving change through data-driven insights and recommendations.
## Inspiration

We were inspired to create our detector by a desire to gain understanding of red team tactics and testing methodologies.

## What it does

The detector looks for running processes that match the names of known sniffing software, captures the information, and creates a hit on the security dashboard provided by Howler. the same process is repeated if a suspicious looking command is executed. Solution adheres to the detection recommendations outlined in the MITRE analysis of the network sniffing technique.

<https://attack.mitre.org/techniques/T1040/>

## How we built it

The detector is built using a continually running python script. The python script uses subprocesses and system utilities to access data on running processes and executed commands, and checks these against a list of known potentially malicious events. The demo and testing is done through Atomic Red Team test cases

<https://atomicredteam.io/discovery/T1040/>

## Challenges we ran into

The atomic testing framework required modification to run on the host system, getting logs of commands executed is difficult.

## Accomplishments that we're proud of

We are pleased that the script runs as expected and was able to detect both command execution and process creation.

## What we learned

* Containerized red team testing using atomic red team and Docker
* Analysis of system status using logging and process monitoring.
## What's next for Network Sniffing Detector - MacOS
Increasing the robustness of the program with more research into possible malicious processes.
* Using regex to allow for better classification of processes and commands.
## Inspiration

People are increasingly aware of climate change but lack actionable steps. Everything in life has a carbon cost, but it's difficult to understand, measure, and mitigate. Information about carbon footprints of products is often inaccessible for the average consumer, and alternatives are time consuming to research and find.

## What it does

With GreenWise, you can link email or upload receipts to analyze your purchases and suggest products with lower carbon footprints. By tracking your carbon usage, it helps you understand and improve your environmental impact. It provides detailed insights, recommends sustainable alternatives, and facilitates informed choices.

## How we built it

We started by building a tool that utilizes computer vision to read information off of a receipt, an API to gather information about the products, and finally ChatGPT API to categorize each of the products. We also set up an alternative form of gathering information in which the user forwards digital receipts to a unique email.

Once we finished the process of getting information into storage, we built a web scraper to gather the carbon footprints of thousands of items for sale in American stores, and built a database that contains these, along with AI-vectorized form of the product's description.

Vectorizing the product titles allowed us to quickly judge the linguistic similarity of two products by doing a quick mathematical operation. We utilized this to make the application compare each product against the database, identifying products that are highly similar with a reduced carbon output.

This web application was built with a Python Flask backend and Bootstrap for the frontend, and we utilize ChromaDB, a vector database that allowed us to efficiently query through vectorized data.

## Accomplishments that we're proud of

In 24 hours, we built a fully functional web application that uses real data to provide real actionable insights that allow users to reduce their carbon footprint

## What's next for GreenWise

We'll be expanding e-receipt integration to support more payment processors, making the app seamless for everyone, and forging partnerships with companies to promote eco-friendly products and services to our consumers

[Join the waitlist for GreenWise!](https://dea15e7b.sibforms.com/serve/MUIFAK0jCI1y3xTZjQJtHyTwScsgr4HDzPffD9ChU5vseLTmKcygfzpBHo9k0w0nmwJUdzVs7lLEamSJw6p1ACs1ShDU0u4BFVHjriKyheBu65k_ruajP85fpkxSqlBW2LqXqlPr24Cr0s3sVzB2yVPzClq3PoTVAhh_V3I28BIZslZRP-piPn0LD8yqMpB6nAsXhuHSOXt8qRQY)