## Inspiration

The inspiration for Leyline came from [The Interaction Company of California's challenge](https://interaction.co/calhacks) to design an AI agent that proactively handles emails. While Google's AI summarization features in their email app were a good start, we felt they lacked the comprehensive functionality users need. We envisioned an agent that could handle more complex tasks like filling out forms and booking flights, truly taking care of the busy work in your inbox.

## What it does

Leyline connects to your Gmail account and parses incoming emails. It uses AI models to analyze the subject, body text, and attachments to determine the best course of action for each email. For example, if there's a form attached with instructions to fill it out, Leyline completes the form for you using information from our database.

## How we built it

We developed Leyline as a Next.js app with Supabase as the backend. We use Google Cloud Pub/Sub to connect to Gmail accounts. A custom webhook receives emails from the Pub/Sub topic. The webhook first summarizes the email, then decides if there's a possible tool it could use. We implemented Groq's tool-calling models to determine appropriate actions, such as filling out attached forms using user data from our database. The frontend was built with Tailwind CSS and shadcn UI components.

## Challenges we ran into

1. Processing Gmail messages correctly was challenging due to conflicting documentation and inconsistent data formats. We had to implement workarounds and additional API calls to fetch all the necessary information.
2. Getting the tool-calling workflow to function properly was difficult, as it sometimes failed to call tools or missed steps in the process.

## Accomplishments that we're proud of

* We created an intuitive and visually appealing user interface.
* We implemented real-time email support, allowing emails to appear instantly and their processing status to update live.
* We developed a functional tool workflow for handling various email tasks.

## What we learned

* This was our first time using Google Cloud Pub/Sub, which taught us about the unique characteristics of cloud provider APIs compared to consumer APIs.
* We gained experience with tool-calling in AI models and learned about its intricacies.

## What's next for Leyline

We plan to expand Leyline's capabilities by:

* Adding more tools to handle a wider variety of email-related tasks.
* Implementing a containerized browser to perform tasks like flight check-ins.
* Supporting additional email providers beyond Gmail.
* Developing more specialized tools for processing different types of emails and requests.
## Inspiration

Companies lack insight into their users, audiences, and marketing funnel.

This is an issue I've run into on many separate occasions. Specifically,

* while doing cold marketing outbound, need better insight onto key variables of successful outreach
* while writing a blog, I have no idea who reads it
* while triaging inbound, which users do I prioritize

Given a list of user emails, Cognito scrapes the internet finding public information about users and the companies they work at. With this corpus of unstructured data, Cognito allows you to extract any relevant piece of information across users. An unordered collection of text and images becomes structured data relevant to you.

## A Few Example Use Cases

* Startups going to market need to identify where their power users are and their defining attributes. We allow them to ask questions about their users, helping them define their niche and better focus outbound marketing.
* SaaS platforms such as Modal have trouble with abuse. They want to ensure people joining are not going to abuse it. We provide more data points to make better judgments such as taking into account how senior of a developer a user is and the types of companies they used to work at.
* VCs such as YC have emails from a bunch of prospective founders and highly talented individuals. Cognito would allow them to ask key questions such as what companies are people flocking to work at and who are the highest potential people in my network.
* Content creators such as authors on Substack looking to monetize their work have a much more compelling case when coming to advertisers with a good grasp on who their audience is.

## What it does

Given a list of user emails, we crawl the web, gather a corpus of relevant text data, and allow companies/creators/influencers/marketers to ask any question about their users/audience.

We store these data points and allow for advanced querying in natural language.

[video demo](https://www.loom.com/share/1c13be37e0f8419c81aa731c7b3085f0)

## How we built it

we orchestrated 3 ML models across 7 different tasks in 30 hours

* search results person info extraction
* custom field generation from scraped data
* company website details extraction
* facial recognition for age and gender
* NoSQL query generation from natural language
* crunchbase company summary extraction
* email extraction

This culminated in a full-stack web app with batch processing via async pubsub messaging. Deployed on GCP using Cloud Run, Cloud Functions, Cloud Storage, PubSub, Programmable Search, and Cloud Build.

## What we learned

* how to be really creative about scraping
* batch processing paradigms
* prompt engineering techniques

## What's next for Cognito

1. predictive modeling and classification using scraped data points
2. scrape more data
3. more advanced queries
4. proactive alerts

[video demo](https://www.loom.com/share/1c13be37e0f8419c81aa731c7b3085f0)
## Inspiration

Have you ever attended a networking event and felt overwhelmed by the sheer number of people, unsure of how to find the right connections? We've all been there, wishing for a more streamlined way to meet individuals who align with our goals and values. The inspiration for Aligned.ai comes from this common challenge—finding people who will empower you to do your life's best work. Our goal was to create a system that helps individuals build sustainable and long-lasting relationships in the startup space, whether it’s founder-to-founder or founder-to-VC.

## What it does

Aligned.ai is a matchmaking platform designed to connect individuals in the startup ecosystem based on deep personality and goal alignment. Users engage in live, in-depth conversations with Aligned Voice, an AI-powered by Groq, which simulates a real human interaction. The system then generates a unique personality embedding using Cohere, which is stored in Chroma DB. Using powerful vector similarity algorithms, Aligned.ai ranks potential connections, presenting users with the best matches first. This way, users can find those who are not only aligned with their professional aspirations but also resonate with their personal values.

## How we built it

Aligned.ai was developed with a focus on seamless integration across multiple technologies:

![Alt text](https://d112y698adiu2z.cloudfront.net/photos/production/software_photos/003/026/013/datas/original.png)

* Frontend: Built using Next.js, our frontend integrates Auth0 for tokenization and secure login.
* Data Collection: Once logged in, users create their matchmaking profiles, including data from their Web Summit profile, LinkedIn, GitHub, and more.
* Conversation with Aligned Voice: This **core feature** was powered by Groq, users engage in a live conversation that feels as natural as talking to a real person. We used Groq's integrations with Whisper for speech to text - then sent this info to our backend server for stateless requests to Groq's blazing fast llama 3 models. **We experimented with various platforms but found Groq to give us the latency necessary for our needs**
* Personality Embedding: This was the **bread and butter** of our app. The conversation data is processed by Cohere's Embed API to generate a personality embedding, which is stored in Chroma DB vector database.
* Matchmaking: Users can then search for others with similar profiles using vector similarity algorithms. This was all ran over Chroma DB's seamless and powerful interface with multiple integrations. Summaries of similarities are hosted using Groq and Cohere. The system ranks matches from most to least aligned, providing a curated list of potential connections.
* Reach Out: Once a match is found, users can reach out directly using the embedded social information.

## Challenges we ran into

* One of the main challenges we faced was prompt engineering—ensuring that the AI could generate meaningful and accurate personality embeddings from the conversations.
* Additionally, integrating the full tech stack from frontend to backend, while maintaining real-time AI performance, posed significant difficulties.
* Balancing the scale of integration with providing a clean and intuitive user experience was another key challenge we successfully navigated.

## Accomplishments that we're proud of

* MVP Completion: We successfully built and deployed a minimum viable product that effectively demonstrates the core functionality of Aligned.ai.
* Contribution to Open Source: We made a PR to improve one of the technologies we worked with, contributing back to the community.
* User-Centric Design: We invested significant time in UI/UX design to ensure that the user experience is as intuitive and enjoyable as possible.

## What we learned

Through this project, we learned the immense potential of AI-driven systems to facilitate meaningful connections between individuals. The ability of autonomous agents to understand and simulate human interaction signals a new paradigm in networking and relationship-building. We also gained valuable insights into prompt engineering, real-time AI processing, and the importance of seamless integration across the tech stack.

## What's next for Aligned.ai

The potential for Aligned.ai is vast. Moving forward, we plan to expand the action space of personality embedding and vector based search, integrating more powerful features to enhance the matchmaking process. We aim to refine the AI's ability to simulate even more nuanced human interactions and to improve the accuracy of our personality embeddings. As we continue to develop Aligned.ai, our goal is to make it the go-to platform for building meaningful, long-term relationships in the startup ecosystem.