Cohere Applications

Browse applications built on Cohere technology. Explore PoC and MVP applications created by our community and discover innovative use cases for Cohere technology.

Business Llama

📣 Exciting News from Business Llama! 📈 🚀 We're thrilled to introduce "Business Llama: Optimized for Social Engagement," our latest project that's set to transform the way you approach business planning and go-to-market (GTM) strategies. 🌟 🤖 With the power of advanced, fine-tuned models, driven by the renowned Clarifai platform, we're taking your business strategies to the next level. Here's what you can expect: 🎯 Enhanced Decision-Making: Make smarter, data-driven decisions that lead to business success. 📊 Improved Business Plans: Develop robust and realistic plans backed by deep insights. 🌐 Optimized Go-to-Market Strategies: Reach your target audience more effectively than ever before. 🏆 Competitive Advantage: Stay ahead in the market by adapting quickly to changing conditions. 💰 Resource Efficiency: Maximize resource allocation and reduce costs. 🤝 Personalization: Tailor your offerings to individual customer preferences. ⚙️ Scalability: Apply successful strategies across various products and markets. 🛡️ Risk Mitigation: Identify and address potential risks proactively. 🔄 Continuous Improvement: Keep your strategies aligned with evolving market conditions. Join us on this journey to elevate your business game! 🚀 Stay tuned for updates and exciting insights. The future of business planning and GTM strategies is here, and it's more engaging than ever. 🌐💼 #BusinessLlama #SocialEngagement #DataDrivenDecisions #Clarifai #GTMStrategies

Team Tonic
ClarifaiLlama 2OpenAIVercelCohere

Optibuild

Visual Q&A for Monday.com is an innovative AI-powered tool designed to elevate project management and team productivity by analyzing task-related images and providing visual question answering. It integrates seamlessly with Monday.com, adding an extra layer of intelligence to this already robust platform. Harnessing the capabilities of Salesforce's state-of-the-art visual question-answering model, Blip, this tool allows teams to ask specific, targeted questions about visual content related to their tasks and get instant, automated responses based on an AI-powered analysis. For instance, in a construction project, photos documenting progress are often critical. With Visual Q&A, project managers can automatically analyze these images by asking questions like "Is the wall painted evenly?" or "Are the tiles laid correctly?" The AI model then scans the uploaded images and provides an answer, flagging potential issues and allowing teams to address them promptly. This drastically reduces the time required for manual checking and provides an efficient tool for quality control. In a marketing context, the tool becomes even more powerful. It can analyze design drafts, advertisement images, or campaign graphics and answer queries about specific elements. Questions like "Is the company logo clearly visible in the advertisement?" or "Does the advertisement contain any text in red?" can be answered rapidly, making it a valuable tool for ensuring brand consistency and meeting design specifications. But the applications of Visual Q&A for Monday.com extend far beyond these examples. Whether it's a retail company needing to confirm if their products are displayed correctly in a store, a manufacturer checking if a machine part has been installed correctly through images, or a software company verifying the presence of specific elements in their UI/UX, Visual Q&A can provide quick and reliable answers, streamlining the task management process.

Change Makers
CohereAnthropic ClaudeOpenAILangChainMonday AI Assistant

MindSpeak - Visualizing Mental Health Support

MindSpeak is a groundbreaking project that leverages cutting-edge technologies to revolutionize mental health support. Mental health disorders are prevalent in our society, but due to stigmatization and lack of accessible information, many individuals face challenges in seeking help and finding accurate resources. The project combines the power of artificial intelligence, advanced embedding techniques, and immersive multimedia to offer an engaging and interactive platform for mental health support. The process begins with Chroma, an innovative tool that converts uploaded PDF files into vector representations. By employing advanced embedding techniques, Chroma ensures that the information is accurately captured and transformed into a format suitable for further processing. To enhance the quality of vectorization and improve the overall representation, Cohere comes into play. Cohere facilitates the embedding process, utilizing sophisticated algorithms to refine and enhance the vectorized data. This step ensures that the generated vectors are of high quality and accurately capture the nuances of the original content. One of the key features of MindSpeak is Stable Diffusion, a technology that enables the generation of coherent and visually appealing images based on the text generated by the model. By analyzing the textual information, Stable Diffusion generates images that align with and enhance the provided content. To further enhance the user experience and accessibility, MindSpeak incorporates Elevenlabs, a powerful tool that converts text into speech. This feature allows the generated content to be conveyed audibly, adding an immersive audio component to the multimedia animations.

Cookies
Streamlit
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ChromaCohereCohere EmbedStable Diffusion

Vocava

AI-powered Personal Tutor: Vocava leverages state-of-the-art Large Language Models to act as a personalized language tutor. This AI can adjust its teaching strategy according to user's fluency and interests, making each learning session tailored and efficient. - Immersive Learning: Unlike traditional language apps that focus on vocabulary and grammar, Vocava focuses on creating immersive, context-based learning experiences. This mimics how people naturally acquire languages, making learning more intuitive and enjoyable. - Language Translation and Conversation Practice: Vocava offers a translation module with added features like part-of-speech tagging and explanations. Moreover, users can engage in conversation with the AI tutor in the Chatterbox module, practicing their speaking and listening skills. - Storytelling and Reading Comprehension: The Storytime module presents learners with stories in their target language and offers comprehension questions, reinforcing understanding in an entertaining way. - Culture Corner: Vocava goes beyond language learning, offering insights into the culture and traditions of different regions. This helps users understand the context of the language and adds richness to the learning experience. - Learning Through Games: Vocava's Arcade module presents a series of games that teach language in a fun and engaging manner. From Pictionary and MadLibs to Jeopardy, learning becomes a delightful activity rather than a tedious chore. - Dynamic Vocabulary Learning: The Playground module allows learners to generate new vocabulary and phrases, save known phrases, and review them. All these phrases are embedded in a vector database for future reference. - Analytics Dashboard: Vocava offers a comprehensive dashboard to track learner's progress over time, making it easy to see improvements and identify areas for focus. - Newsfeed: Users can access real-time content in their target language, practicing their skills with actual, relevant information.

The Irrelevant Elephant
Streamlit
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CohereChromaOpenAIDALL-E-2WhisperAnthropic Claude

Maverick AI

Maverick REACT offers artificial intelligence integration for emergency situations. Our service uses AI with the necessary event information provided by government officials and acts as an assistant to provide key protocols and information to citizens. The AI service is accessed via SMS or web portal, offering a solution without internet. How does our service work? When an emergency situation occurs, such as a flood, fire or earthquake, our service sends an SMS message or makes a voice call to numbers registered in a database or the citizen can contact a number provided by the authorities. The message or call contains information about the type and severity of the emergency, preventive measures that should be taken and resources available in the area. The user can respond to the message or call with specific questions about their personal situation or request additional help. Our service uses AI algorithms to process responses and offer personalized and updated advice. REACT has several advantages over traditional emergency alert and response systems. Firstly, it does not depend on the internet, which means it can function even when there are power outages or problems with mobile networks. Secondly, REACT service is interactive and adaptable to the individual needs of each user. Thirdly, it uses reliable and verified sources of information provided by the government or other authorized organizations. And finally REACT is fast and efficient in sending and receiving large-scale messages or calls. Our goal is to contribute to creating a safer and more resilient world in the face of emergency situations through innovative and intelligent use of technology. We believe that our service can save lives and reduce suffering caused by disasters. If you want to know more about our service or how to register for it, contact us. We are Maverick AI.

MaverickAI
replit
application badge
CohereQdrant

Fetcher the work sidekick

In today's increasingly remote working style, organization’s messaging system, whether it's email or chat, contains lots of invaluable institutional knowledge. However, because these data are often unstructured and scattered, they are usually buried in the organization’s data ecosystem and are hard to search and extract value. Fetcher is a chatbot that integrates into popular chat platforms such as Discord and Slack to seamlessly help users find relevant people and documents to save them from endless frustrating search. It does this by semantically searching chat messages to find the most relevant results and help to deliver actions that leads to a peace of mind. Fetcher differs from traditional keyword search engines in that it searches by the meaning of the query, not just by keywords. It also enables multi lingual search, so that global teams can more quickly find important information even when language is a barrier. Since Fetcher searches in the embedding space, this search engine can extend to multi modal modes that includes audio and images. Fetcher works by collecting a chat channel’s history and embedding them using Cohere’s Embed API, then saving the embeddings to Qdrant’s vector search engine. When a new query comes in, Fetcher embeds the query and searches against the vector database to find the most relevant results, which can then feed into Cohere’s Generate API to summarize the message thread to kick start new conversations. Fetcher offers 3 commands, /fetch, using vector similarities search to find relevant chat messages. /discuss, summarize a message thread, and kick start a conversation with a channel number. /revise, a sentence correction tool similar to Grammarly, allows user to send professional sounding messages.

Fetch
CohereCohere GenerateCohere EmbedQdrant

Language Matchmaker

Language Matchmaker is an app that aims to help language learners find conversation partners who share their interests and language proficiency levels. The app will use Cohere's multilingual semantic search technology to analyze user profiles and match them with other users who have similar interests and language skills. Users will create a profile on the app where they can specify their native language, target language(s), and interests. The app will then use Cohere's technology to identify commonalities between users and present them with a list of potential conversation partners. The Qdrant technology will be used to rank the matches based on similarity and provide recommendations for the best matches. Once a match is made, users can schedule a virtual conversation through the app and practice their language skills with their partner. The app will also provide conversation prompts based on the users' interests to facilitate the conversation. We highlighted the unique features of the app, such as the use of Cohere and Qdrant technologies to match users based on shared interests and language proficiency, and the ability to schedule virtual conversations through the app. You can also discuss the potential use cases of the app, such as for language learning, cultural exchange, or making new friends from different parts of the world. For the app development, we will use programming languages like Python that integrates the Cohere and Qdrant APIs.

Polyglot Searchers
Vercel
application badge
QdrantCohere

LegalFruit

Our project is aimed at developing a comprehensive legal document search system that makes use of advanced technologies to retrieve relevant legal documents that can be relied upon in court. The system utilizes Cohere's multilingual embedding and Qdrant vector database to provide fast and efficient search results. The use of multilingual embedding ensures that the system is capable of searching through legal documents written in various languages, making it suitable for use in multilingual environments. Qdrant vector database, on the other hand, allows for fast and efficient indexing of large volumes of legal documents, thus reducing search time. Our legal document search system is particularly useful for law firms, legal practitioners, and businesses that require access to legal documents for various purposes, including legal research, contract negotiations, and dispute resolution. With our system, users can easily retrieve legal documents that have been signed by mutual assent, thus ensuring that they are reliable and admissible in court. In addition to the legal document search functionality, we have also implemented a question answering system using Cohere's generate endpoint. This feature enables users to ask specific questions related to the legal documents they have retrieved and receive accurate and relevant answers. The question answering system is particularly useful for legal practitioners who require quick access to specific information in legal documents. Overall, our legal document search system provides an efficient and reliable solution for users who require access to legal documents. By leveraging advanced technologies such as Cohere's multilingual embedding and Qdrant vector database, we have developed a powerful search system that can save time and improve productivity for legal practitioners and businesses alike.

The Meowsterminds
Streamlit
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CohereCohere GenerateCohere EmbedQdrant

Heuristic AI

Heuristic AI brings browsing your Slack chat histories into a new dimension. Fueled by Qdrant vector search engine and the Generative model of Cohere, Heuristic.ai extracts the context from your question and matches it with your chat messages to elaborate the answer. Forget keywords and chats scrolling. We give you the answer and the source message in seconds! Vision: to enable people to find answers to any questions in their digital experience. Mission: to bring browsing chat histories to a new dimension How it works: 1. The user write normal query with the structure we have “hai, setup” or “hai, question” 2. Ngrok forward queries from slack_api to the Amazon server 3. Here, we evaluate the query to take action: - Setup from the sentence <hai, setup> or a sentence which contains hai and setup - Search: from the sentence that contains only the word hai - None, if the message sent in slack is a normal message 4. here we have two scenarios: - in the case of the setup action, we retrieve all the messages from all the channels, then encode them using co.embed prepare to be ready to store in Qdrant vector database - in the case of the search action, we encode the user query to retrieve the first 5 relevant messages from the conversations, then extract the answer to the user query from the first message retrieved using co.generate 5. Qdrant is the vectors search engine that allows us to store our vectors and to search on them. 6. Then lastly, the extracted answer is sent to the user.

Heuristic AI
QdrantCohereCohere Generate

Project Peace

Project Peace is a Multilingual Text Detoxifier. It is an innovative solution to identify and neutralize toxic or harmful language in written text. It utilizes advanced AI algorithms powered by Cohere’s multilingual models to understand and analyze text across multiple languages, and flag potentially toxic language, including the ability to convert that toxic language into neutral and non-toxic one. Project Peace’s ability to process text in multiple languages, allows it to address the problem of toxic language on a global scale. Project Peace can be integrated into online platforms, such as social media websites, online forums, and online communities, to help prevent the spread of toxic language and promote a safer online environment. It can be used by businesses and organizations to monitor and control the language used on their website and even in their customer care services. It can also be used by governments and public institutions to monitor and control the language used in online communication channels and to promote social harmony and inclusion. It can be used by educators and schools to help prevent bullying and toxic language in online learning environments, ensuring that students have a safe and supportive learning environment. private individuals as well who want to promote a safer and more inclusive online environment, or who want to ensure that the language they use online is respectful and non-toxic. Project Peace has an appealing future by its scalability and customization. By integrating it with the existing social platforms, it can be made accessible to a wide range of users. Moreover, it has the potential to become an industry standard for detecting and detoxifying toxic texts. The goal of the project remains to create a safer online community by reducing the spread of hate speech, cyberbullying, and other forms of harmful language.

Project Peace
Streamlit
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CohereCohere ClassifyCohere Generate

FINXAI

Usually firms have excess liquidity from their operations and people have savings that want to invest so that they can protect themselves from inflation or to generate a passive income. The problem is that in order to invest in the financial markets they either have to hire an specialist or they need to manage their investments themselves and this can expose them to high risks since they aren't usually experts on the field. There have been cases of companies even going into bankruptcy for preciselly investing in the financial markets with poor management or little understanding of the complexity of the markets. Here's where we come in, our solution powered by AI enalbes firms and people to invest in the financial markets without having to hire an expensive investment manager or having to learn themselves. It is a virtual portfolio manager. First the user has to provide financial information and a news or article related to him, with this an ML classifier using the Cohere API determines the risk profile of the user. With this information the platform generates a tailored portfolio, out of a selected financial asset universe specific to each risk profile. The available assets vary between different types of US stocks, FOREX pairs and even cryptocurrencies for the more risk taker profiles. Once the portfolio has been created it reports an overview of its composition as well as a backtest of its performance on the market. At this point the user has the option to decide whether to pursue a passive portfolio management strategy or an active one with just the click of a button. If he prefers a passive strategy the platform will take positions for a classic buy and hold strategy of the selected assets. If, in contrast, the user selects the use of trading bots then he will opt for an active portfolio management and trained DL bots will be buying and selling the assets at convenience for a better portfolio performance.

FINXAI
CohereCohere Classify

Project Eval

Eval aims to address the problem of subjectively evaluating test answers. Traditionally, this task has been carried out manually by human graders, which can be time-consuming and prone to bias. To address this issue, the project utilizes Cohere powered APIs to automate the evaluation process. The use of Cohere APIs allows for the integration of advanced natural language processing techniques, enabling the system to accurately understand and analyze the content of test answers. The custom model built upon these APIs then scores the answers based on suitable metrics, which can be tailored to the specific requirements of the test or assessment. One potential application of this technology is in the field of education, where it could be used to grade assignments or exams in a more efficient and unbiased manner. It could also be utilized in professional settings for evaluating job applications or performance evaluations. In addition to increasing efficiency and reducing bias, the use of automated evaluation techniques has the potential to provide more consistent and reliable scoring. This can help to ensure that test-takers receive fair and accurate assessments of their knowledge and skills. The model for the same was evaluated based on 4 major metrics: - Semantic Search: this is the primary scoring strategy of Eval. It is used to semantically understand the answer given and evaluate based on content rather than simply scoring based on textual similarities. Cohere Embed was used to generate embeddings for 5 suggested answers for the question and the answer to be checked. Then we find the distance from the nearest neighbor out of the 5 suggestions and the answer. This distance is used to grade the answer. - Duplication Check: partially correct answers with duplication of text tended to get higher similarity scores compared to the ones without duplication. To stop students from using this exploit to gain extra marks, a duplication checker was implemented based on Jaccard-Similarity between sentences within the answer. - Grammar Check: this strategy aims to check the grammar of the answer and assign a score based on the number of grammatical errors. We used Cohere Generate endpoint to generate a grammatically correct version of the answer, then check for cosine similarity of the generated version with original version to check if the original version was grammatically correct. - Toxicity Check: this aims to detect for toxic content in the answer and penalize an answer if it is toxic. We trained a custom classification model on Cohere using the Social Media Toxicity Dataset by SurgeAI which gave a 98% precision on the test split. We also implemented a Custom Checks which allows users to give different weights to each of the three different metrics based on how important they are for the evaluation of the answer. This allows for a more personalized evaluation of the answer. We built our custom model into a Flask-based REST API server deployed on Replit to streamline usage and allow people to access the full-functionality of the model. We also built a highly interactive UI that allows for users to easily interact with the API and evaluate their answers as well as submit questions.

chAI
replit
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Cohere

I Rene

I-Rene Provides CBT for the lovely user using Cohere Conversant AI tool. The NFT is minted for free by the lovely user to track the CBT sessions and be proud of their healing process. It is a free, open-source, specific & effective mental health therapy needed by everyone, anywhere at anytime. The mental health AI chatbot will be developed as a standalone application that users can download and install on their mobile devices or access through a web-based platform. The chatbot will collect and use user feedback to improve its performance and effectiveness. This will involve monitoring user interactions and responses, and using machine learning algorithms to continuously adapt and improve the chatbot's responses and support. The mental health AI chatbot will use sentiment analysis to understand the emotional state of users and react accordingly. For example, if a user is feeling sad or anxious, the chatbot can provide appropriate support and resources to help the user manage their emotions and feelings. The mental health AI chatbot will use entity extraction to provide context-dependent answers and support, rather than just generic responses. This will involve analyzing the user's messages and extracting relevant entities and information, such as the user's goals, concerns, and challenges. The chatbot can then use this information to provide personalized and tailored support. The mental health AI chatbot will be integrated with a decentralized autonomous organization (DAO) and the Metaverse, which is a virtual shared space for communities and organizations. This will enable the chatbot to access and use decentralized resources and data to provide more accurate, relevant, and engaging support to users.

Mental Health AI
medal
Streamlit
application badge
Cohere

AI Chatbot

Chatbots in the healthcare field are providing patient assistance and care. AI-powered medical assistant can book appointment, monitor a patient health status and perform other time-intensive responsibilities such as inventory, billing and claims management . There are three key limitations: 1)Explainability 2)Datarequirement 3)Transferability STATEMENT: To be able to enter a prescription with structured data in a software system, within a comparable time to hand written prescription. IDEA: 1) Automation of handwritten and digital prescriptions to reduce entry time. 2) Improve the effectiveness of customer service terms. 3) Reduce the potential for human error. 4)Collect candid and meaningful customer feedback. 5) Guide customers along the path to purchase. 6) Build stronger customer relationships Chatbots reside in the most commonly used apps in the form of assistants on various websites where they can converse with the users. With advanced machine learning algorithms and natural language processing methods enabled , these chatbots can create maps linking symptoms and diseases. Chatbots in the industry for medical care ,ask some standard questions and help create a profile based on age ,sex, and medical history . They can record the users history and analyze symptoms based on users inputs. They can also use image and voice processing to record and match symptoms against the database using the gathered information it automatically print handwritten and digital prescriptions. Natural language processing Deep learning Context aware processing Intelligent robots Neural networks Fuzzy logic Support vector machines Genetic algorithms Hybrid system

AiDemanica
Cohere

mEYE Buddy App

mEYE Buddy app is an application whose main role is to play part as your very own PERSONAL assistant who will make the world a little bit more accessible for anyone who is in any way visually impaired and requires assistance. The BEST SIDES OF THIS APP are that IT is highly affordable, with a premium version and is based on a loyalty program (business model). With mEYE buddy, not only would the life of blind people be much easier, but they could have the privilege of INDEPENDENCE - they would not need to rely on their caretaker or service dog to help them withq everyday chores.The state of the art tool for assistance to visually impaired persons! This state of the art AI technology will assist you in your everyday life. How app works: For first login you would have to register, and you'd do that using your fingerprint. Everytime the app is opened, the voice will tell you where everything is located on the screen. The design was made simple and the buttons were made big for easy access. The user can always press the icon in the middle of the screen for a reminder of the locations of the buttons. The voice assistant is the main function of the app. It will activate the AI, which will use a connected camera to describe the surroundings and warn the user of hazards. The key places tab will take the user to a tab where they can activate a guide to registered locations that are important to the user. (workplace, supermarket, hospital, home, coffee shop, etc.) They can also register new locations and remove old ones. The hazards that were noted by the ai could also be registered here. The devices tab is simple. It is used to connect the device to an external camera, preferably one on the glasses of the user or attached to their body. The user can also connect to their smart watch for easy access. The settings tab is pretty self-explanatory. However, besides the settings, it will also contain emergency information about the user, in case they need help from someone. The “mEYE Buddy” app will be connected to a camera on the glasses or on your body, and will describe everything important going in front of you - every peculiar movement that triggers its sensors or warn you against any potential hazards that may come in your way. The app can also be asked to describe something specific in more detail. Our Buddy also stores any hazard or newly recognized item in its already vast and enriched database. The app can also be told where certain points of interest are located (workplace, supermarket, hospital, home, coffee shop, etc.) for easier access to it later. The app comes with a simple UI with big buttons for input, and can be instructed through the AI voice as well. Also comes with a 3x3 keyboard for simpler accessibility.

Aurora
GPT-3Cohere

BridgeDoc

https://krusnabalar-bridgedoc-frontendsrcmydoc-jwe73t.streamlitapp.com/ INSPIRATION: When was the last time you had an uncomfortable sensation that you didn’t know how to describe? You look it up on google, WebMD and ask onreddit, but find yourself just as uninformed and even more stressed than before. You end up calling a clinic or hospital to speak with a doctor, and find yourself having to wait a week until the next available appointment. The general public is not trained to be aware of and describe the symptoms they might be going through. The way we describe our sensations can vary enormously, often we use idioms and other figures of speech. The stress and struggle of being unable to understand our body’s pain can be frustrating. Even in speaking with doctors, there’s often misunderstandings, and a lot of back and forth, until the doctor can finally understand the patient’s symptoms. In the field of medicine and healthcare, that kind of subjectivity and unpredictability can be dangerous, inefficient, and costly. That got our team to wonder: What if there’s a way to effectively predict a patient’s symptoms based on their own description in a fraction of the time at no cost to the patient? BMJ Journal published a study which performed research on clinical text to extract mental health symptoms and using a classification NLP model, citing the automatability of the symptom detection process as being a credible way to approach this issue. SOLUTION: Introducting BridgeDoc, the tool that doctors and the general public can use to understand and identify their symptoms and diseases. BridgeDoc will use classification tools provided by co:here to detect and identify the specific symptoms, for the knowledge of the doctor, and possible disease diagnoses, for the knowledge of the doctor and the patient. It will allow an ease of communication between a doctor, clinic, or hospital with the patients by using a model trained with colloquial descriptions of symptoms to identify the likelihood of the patient’s symptoms. COMPETITIVE ADVANTAGE: Companies like WebMD, Mayo Clinic, DearDoc, Mercury Healthcare all lack a way to enhance user inquiry and streamline the communication between doctors and patients. BridgeDoc equips users and medical businesses to prevent the struggle and misunderstandings involved in translating a patient’s description of their issue and the doctor’s knowledge of what exactly those symptoms are, helping them efficiently pin point the most likely solutions. This has not been used in a professional medical sense for patients and can help edge over competitors in a significant way with a high quality symptom prediction model. REVENUE AND EXPENSES: There are two B2B (doctors, hospitals) and B2C (online website clients) solution that BridgeDoc provides. B2B solutions will be provided initially per user at a contract price based on the user report and needs of the doctor/hospital. B2C users will have free access. Expenses will be website hosting when moved to a different website that is more customized, as well as to access data that is more reliable by finding methods to get a secure access to it. Research expenses for improving model prediction will also be there. KEY METRICS: We will track the following key metrics Growth: number of doctor contracts acquired per month, number of users accessing website Engagement: use tools like Hotjar to track website interaction (user remains anonymous), track number of searches made per user Marketing: to measure success for our marketing efforts, we will evaluate cost per acquisition, cost per clicks, and understanding the trends in impression to generate better marketing assets iteratively. We will also research marketing tacics of companies like Zocdoc who created an industry tool used by doctors. Product: we will track and receive product feedback dynamically using tools like hotjar to understand user engagement with the product (what parts of the website are being used, how long they spend on the website, etc. HOW WE BUILT IT: We built everything with python. Using jupyter notebooks we tested out co:here’s endpoints, integrated them into the boilerplate provided by LabLab and co:here. The app is deployed using streamlit. Data was collected from various tools such as reddit and google search scrapers, forums. To reach co:here’s requirement for a minimum of 250 examples to train the classify model, due to scarcity of data, we used co:here’s generate tool to build more examples by fine-tuning the model and using specific input phrases that generate reliable results. WHAT'S NEXT FOR BRIDGEDOC We would love to see BridgeDoc to be a standalone tool that can be integrated with online tools for clinics and doctors with private practices, as well as hospital which often deal with issues of patient capacity limits, to automate the report creation by listing possible symptoms and diagnoses automatically. This would require adding a co:here-trained chat-bot that can extract the information from the user in a friendly, secure, and reliable way, similar to how a doctor might on the phone, and produce a report based on the user’s profile (gender, age, previous conditions, etc.) to improve the symptom and disease prediction. Additionally, we leveraged insights from our mentor Ervin, and the “How to get funding from your startup” workshop by Pawel Czech and Mathias Asberg to understand business needs and identify the gaps in what’s currently being offered in our product. It’s important to focus our efforts to specific medical domains to improve accuracy, user retention, and help market and get clients. And the user base can expand by changing the architecture of our model to have a bigger tool that operates like the following: Take input from user and classify what medical domain the query is in. Based on the prediction confidence levels, use the top medical domains and send the user input to specialized classify models trained for the specific domain. Get top predictions from those results of the symptoms as well as the disease. This does not narrow down the user base, and more importantly, it provides improved symptom prediction and more reliable results.

Voyagers
Cohere