AIMate: A companion in productivity

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Ahan M R
Project Owner

AIMate: A companion in productivity

Funding Requested

$80,000 USD

Expert Review
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Overview

AIMate is an AI-powered productivity app designed to enhance personal efficiency. It offers features like multi-chat management, task automation, in-depth research tools, and document handling. Users can customize AI responses, integrate with email and calendars, and access a platform for developing and sharing extensions. Aimed at improving daily workflows through smart, adaptive assistance, AIMate promises to be an essential tool for personal and professional growth.

Proposal Description

Company Name (if applicable)

Zenith.ai

How our project will contribute to the growth of the decentralized AI platform

(i) Innovation in AI Interaction: pushes the boundaries of how users interact with AI by integrating various modes of communication and task management.

(ii) User-Centric Design: By focusing on customizable workflows and user-driven development, AIMate encourages more personalized AI experiences.

(iii) Integration of AI in Everyday Tools: AIMate integration with personal data sources like emails and calendars promotes the seamless adoption of AI in managing personal and professional lives.

The core problem we are aiming to solve

Problem Identification: A significant issue users face in general is the fragmentation of tools, as users often navigate multiple apps for different purposes, leading to inefficiencies. AIMate proposes to centralize task management, document storage, and LLM research functionalities into a single platform, reducing the need for context switching. Traditional task management tools, which are manual and error-prone, are streamlined through AIMate's assistant that automates task creation and management thereby improving the efficiency in performing daily tasks and information management.

Solution: AIMate provides a unified AI-powered platform integrating communication, task management, document handling, and research functionalities.

  • Core Challenge: High cognitive load and decreased productivity due to frequent context and tool switching.
  • Automation: Automates routine tasks and manages communications to enhance efficiency.
  • Unified Interface: Reduces the need for multiple apps by consolidating essential functions into one platform.

Our specific solution to this problem

Local LLM Integration:

  • Compatibility: Supports various local large language models (LLMs) like ooba, llama.cpp, mistral-7b, falcon-7b, and ollama, making it versatile and adaptable to different AI capabilities.
  • Connection: Connects to any OpenAI-compatible application, facilitating easy integration with existing AI infrastructures.
  • Architecture: Utilizes a job queue system allowing for simultaneous handling of multiple requests. Configurable to single or multiple workers depending on the user’s setup.

Main Features:

  • Assistant Mode: Executes a variety of tasks such as web searches, document creation, and to-do list management. Easily expandable through modular functions.
  • Stream: Displays a history of AI-generated entries with capabilities to replay single-message assistant and chat interactions.
  • Docs: Supports uploading and editing PDFs and web pages converted to markdown. Facilitates creation and management of markdown documents within the app.
  • RAG (Retrieval-Augmented Generation): Enhances chat interactions by referencing uploaded documents, enabling the AI to provide more contextually relevant responses.

Technical Infrastructure:

  • Data Storage: Utilizes lightweight databases such as chromadb and sqlite, chosen for their simplicity and lack of requirement for multiple server setups.
  • File Management: Handles document uploads, with a current limit of 3-5 MB per file.
  • Scalability: Designed to scale with user needs, from a single local worker to multiple workers through external APIs.

Project details

Project Overview

AIMate is a multi-functional AI productivity application designed to centralize various digital tasks into one streamlined platform. The app integrates with local and cloud-based LLMs, leveraging AI to enhance user efficiency in handling communications, document management, and task automation.

Large Language Models (LLMs) Used

  • ooba: A proprietary model known for its adaptability to small-scale enterprises.
  • llama.cpp: C++ implementation of a popular model, optimized for performance.
  • mistral-7b: A robust model with capabilities in handling complex query understanding.
  • falcon-7b: Similar to mistral-7b but fine-tuned for quicker response times.
  • ollama: Known for its deep learning efficacy in language understanding.

These models are integrated into AIMate via APIs that are compatible with OpenAI’s standards, ensuring a broad range of AI capabilities and the flexibility to switch or upgrade as new models emerge.

Technical Implementation Details of AIMate

1. Architecture

  • Modular Design: AIMate is engineered with a modular architecture, allowing seamless integration and flexibility in adding or modifying features. This design facilitates the easy plug-in of various Large Language Models (LLMs) and other productivity tools, accommodating evolving user needs and technological advancements.
  • Customization and Scalability: Users can customize their setup by choosing which modules or features to activate based on their specific requirements. The modular nature also supports scalability, allowing the system to expand its capabilities without significant restructuring.

2. Document Handling

  • File Upload and Management: Users can upload documents, primarily PDFs, which are currently limited to a size of 3-5 MB to ensure optimal processing speed and system stability. The application then converts these documents into markdown format, facilitating easier manipulation and integration within the app.
  • In-App Editing: After conversion, documents can be edited directly within AIMate. This feature is particularly useful for users who need to quickly modify documents or extract information without the need for external software.
  • Integration with RAG: The document handling system is closely integrated with the Retrieval-Augmented Generation (RAG) feature, allowing the AI to utilize stored documents as a reference for generating more accurate and contextually relevant responses during user interactions.

Hardware Requirements

  • Minimum Setup: Capable of running on standard desktop hardware with at least 8 GB of RAM and a modern multi-core processor.
  • Recommended Setup: A high-performance server with at least 16 GB of RAM and multiple GPUs, especially if running multiple LLMs or handling high volumes of requests.

Retrieval-Augmented Generation (RAG) in AIMate

Functionality

  • Contextual Data Retrieval: RAG enhances the AI’s capability by integrating a retrieval system that fetches information from a database of uploaded documents. This allows the AI to leverage previously stored data to provide responses that are contextually relevant and informed by past inputs and documents.
  • Dynamic Information Integration: When a user interacts with AIMate, the RAG component dynamically searches the document database for relevant information that can augment the AI's response. This ensures that the AI's answers are not only based on its intrinsic understanding from training but are also enriched with specific details from the user's own documents.

Advantages of RAG

  • Enhanced Accuracy and Detail: By referencing stored material, RAG enables the AI to produce more precise and detailed responses. This is particularly useful in scenarios where the user is seeking in-depth information on previously discussed topics or documents.
  • Improved User Experience: Users receive responses that feel more tailored and relevant to their specific context. This customization enhances user engagement and satisfaction as interactions are more aligned with their individual needs and historical interactions.
  • Continual Learning and Adaptation: As more documents are added and more interactions occur, the RAG system continually improves its ability to fetch and utilize relevant information, effectively learning from ongoing usage to improve its performance over time.

Building the Prototype for AIMate

Initial Phase

  • Core Functionality Integration: The first phase of development focuses on integrating a single Large Language Model (LLM) to establish the foundational chat functionality and document handling capabilities. This phase ensures that the basic operational framework is robust and functional.
  • Selection of LLM: The chosen LLM should be versatile and capable of handling a variety of tasks, from processing natural language queries to generating text-based responses. This LLM serves as the backbone for the subsequent addition of more complex features and additional models.
  • Document Handling Setup: Implement the ability to upload, convert (to markdown), and edit documents within the app. This feature is essential for testing the interaction between the AI's text processing capabilities and user-generated content.

Development Tools

  • Backend Services: Utilize Node.js for the backend. This JavaScript runtime is well-suited for building fast, scalable network applications. Node.js is particularly effective for handling I/O-bound tasks, real-time operations, and data-intensive workloads, all of which are crucial for AIMate.
  • Frontend Development: Adopt React for the frontend development to create a dynamic and responsive user interface. React’s component-based architecture makes it ideal for developing complex user interfaces with efficient updates and manageable state handling.
  • API Integration: Develop RESTful APIs to facilitate communication between the frontend, backend, and the LLM. These APIs will handle tasks such as sending user inputs to the LLM and retrieving responses, managing document uploads, and performing other user actions.

The competition and our USPs

Yes

Describe how your solution distinguishes itself from other solutions (if exist) and how it will succeed in the market.

1. Customizable and Scalable AI Integration

Support for Various LLMs: AIMate allows users to connect to a wide range of Large Language Models (LLMs), offering unparalleled customization and flexibility. Users can choose the model that best fits their needs, whether for simple tasks or complex, specialized requests. This adaptability not only caters to a broader user base but also future-proofs the platform against rapid advancements in AI technologies.

2. Enhanced Document Interaction with RAG

Retrieval-Augmented Generation: AIMate utilizes RAG to enhance its AI responses with information retrieved from user-uploaded documents. This capability ensures that interactions are not only based on generic AI knowledge but are enriched with specific, context-relevant content, providing more accurate and personalized assistance.

Our team

  1. Ahan M R - Applied Scientist, Amazon (5+ years experience in building Machine Learning/Deep Learning models)
    • Building LLM Stack, Feature addition related to modularity of LLM support, Integration with RAG, Addition and integration of models from SNET.
  2. Agrima Rai - Full Stack Developer, RNSIT
    • Dealing with frontend and backend implementation and web design of AIMate.
  3. Karthik C - Blockchain and Data Engineering, UT Austin/ Rivian
    • Integration with SNET, integration of plugins and wallet
  4. 2 Interns (TBD)
View Team

What we still need besides budget?

No

Existing resources we will leverage for this project

Yes

Open Source Licensing

Apache

AI services (New or Existing)

Generative Language Models

How it will be used

Generative Language Models

Proposal Video

Placeholder for Spotlight Day Pitch-presentations. Video's will be added by the DF team when available.

  • Total Milestones

    6

  • Total Budget

    $80,000 USD

  • Last Updated

    20 May 2024

Milestone 1 - API Calls & Hostings

Description

This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.

Deliverables

You can use this amount for payment of API calls on our platform. Use it to call other services or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.

Budget

$20,000 USD

Milestone 2 - Project Kickoff: Design (HLD LLD) & Architecture

Description

Objective: Define the overall system architecture and the interaction between major components. Tasks: Design a solid foundation for AIMate through high-level design (HLD) low-level design (LLD) and architectural planning. Ensuring that all technical and functional requirements are carefully mapped out leading to a scalable tool. Budget: 10000 Expected Completion: 3 weeks Head Count: 1 Applied Scientist 1 Research Engineer 2 Interns

Deliverables

AIMate Design documents are split into 3-4 parts with two key modules being: (i) The LLM Integration Module will be designed to support the seamless incorporation of multiple Large Language Models (LLMs) allowing users to choose from a variety of AI models depending on their specific needs and preferences. This module manages the setup configuration and communication with different LLMs via a unified interface ensuring that users can easily switch between models without compatibility issues. It also handles load distribution among various models to optimize performance and response times (ii) The RAG Module design is important since it utilizes advanced search algorithms to quickly retrieve information that can augment the AI's pre-trained knowledge providing responses that are not only accurate but also tailored to the specific context of previous interactions or stored content.

Budget

$10,000 USD

Milestone 3 - LLM Features and RAG Integration

Description

Objective: Implement advanced features such as the multi-LLM support the Retrieval-Augmented Generation (RAG) system and chat integration by adding tasks. Tasks: Develop and integrate the job system to enable support for multiple LLMs and incorporate RAG to enhance AI responses with document-derived data. Budget: 25000 Expected Completion: 1-1.5 months Head Count: 1 Applied Scientist 1 Research Engineer 1 Web Developer 2 Interns

Deliverables

1. Multi-LLM Support - Description: Extension of AIMate's architecture to support multiple LLMs allowing users to choose from various AI models depending on their specific needs such as language fluency technical expertise or industry-specific knowledge. A. Features: Modular LLM integration framework to easily plug in and configure new LLMs. User interface for selecting preferred LLMs based on task or preference. Compatibility checks to ensure seamless operation of different LLMs within the platform. B. Technology Stack: RESTful APIs for seamless LLM communication and integration with backend adjustments for managing multiple AI model responses. 2. Retrieval-Augmented Generation (RAG) System - Description: Integration of a RAG system that uses document-derived data to enhance AI responses making them more accurate and contextually relevant based on previously stored or currently relevant documents. A. Features: Retrieval system to fetch pertinent information from a document database. Integration with the AI response system to augment the generative capabilities of LLMs with retrieved data. Dynamic update and indexing system to keep the document data relevant and easily accessible. B. Technology Stack: Combination of Node.js and additional indexing/search software for managing document queries and retrieval integrated with the AI response handling system.

Budget

$25,000 USD

Milestone 4 - Core Development and Integration

Description

Objective: Develop the core framework integrate a single LLM and establish basic chat functionality and document handling. Tasks: Set up backend with Node.js integrate RESTful APIs and implement basic LLM functionality and document upload/conversion features. Budget: 15000 Expected Completion: 1-1.5 months Head Count: 1 Web Developer 1 Blockchain Developer 2 Interns

Deliverables

Set up backend with Node.js integrate RESTful APIs and implement basic LLM functionality and document upload/conversion features. (i) Integrate RESTful APIs: Design and implement RESTful APIs to facilitate seamless communication between the frontend, backend, and the LLM, ensuring efficient data exchange and system functionality. (ii) Develop the functionality to allow users to upload documents in various formats and convert them into a standardized format (e.g., markdown) for easier handling and interaction within the app. (iii) Integrate a single LLM to provide AI-driven responses. This involves configuring the LLM for initial use, establishing communication protocols, and ensuring that the model can receive input and deliver responses accurately. The allocation is structured to ensure that all aspects of this milestone are adequately funded, ensuring high-quality outcomes.

Budget

$15,000 USD

Milestone 5 - Quality Assurance Testing for AIMate

Description

Objective: Conduct comprehensive testing across all components—unit integration and system testing. Tasks: (i) Perform detailed testing to ensure functionality reliability and user experience are up to the standards. Address any bugs or issues that arise. (ii) Final preparations for launch including documentation and last-minute optimizations. Budget: 5000 Expected Completion: 3 weeks

Deliverables

Conduct comprehensive testing across all components—unit integration and system testing. Prepare for and execute the initial public release of AIMate. Begin marketing efforts to promote the tool.

Budget

$5,000 USD

Milestone 6 - Launch/Initial Marketing

Description

Prepare for and execute the initial public release of AIMate. Begin marketing efforts to promote the tool. Budget: 5000 Expected Completion: 3 weeks

Deliverables

Implement initial marketing strategies such as online ads tech blog posts and partnership announcements.

Budget

$5,000 USD

Join the Discussion (5)

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5 Comments
  • 0
    commentator-avatar
    HenriqC
    May 18, 2024 | 11:19 AM

    Pretty straightforward plan and clear idea how to use the SNET marketplace. New kinds of AIs are probably pretty nicely integrable when they keep appearing on the marketplace.    I don’t have too much experience of the corresponding services but still my view is that it is quite competed and rapidly evolving space. So I was just wondering if you have been thinking about the project’s long-term sustainability and ways to keep yourself well-resourced to constantly improve and work on the service? I have concerns regarding the ultimate user adoption which makes this look a bit risky investment from the business perspective.   

  • 0
    commentator-avatar
    Cardano4Seniors
    May 2, 2024 | 12:09 AM

    Before I go deeply and actually review and rate I have two questions for you: 1. How does Zenith.ai figure into this proposal or team? and, 2. Is this planned to be an Open Source project?  

    • 0
      commentator-avatar
      Ahan M R
      May 2, 2024 | 7:13 AM

      Hey! Yes, answering the second part, the plan is to open-source this completely, and should be available for usage by everyone. For usage of LLMs, we shall allocate credits for usage of open-source models, but for OpenAI models like GPT-4, for the open-source version, I'll create a placeholder for user to use their API token.  For the first part, Zenith.ai is a team of 4 currently, its still in a very nascent phase right now, but during the course of this project, you can expect that our team will be more public (Website, blog, linkedin presence) for better publicity. But it wouldn’t hamper the open-source nature of this project in any manner. I'd see Zenith.ai just as banner for a charter of various AI based projects open to public for usage. Will need to be how our team can officially associate with singularity.net with this milestone. Does that answer your question?

    • 0
      commentator-avatar
      Ahan M R
      May 2, 2024 | 7:16 AM

      Just adding to it, I will be refining the proposal a bit more in a few days (currently working on a paper), so I'll be adding a doc that will be addressing the timelines and implementation details in form of low-level design (LLD)

      • 0
        commentator-avatar
        Cardano4Seniors
        May 9, 2024 | 2:17 AM

        Thank you.  

Reviews & Rating

Sort by

12 ratings
  • 0
    user-icon
    mivh1892
    May 14, 2024 | 11:19 AM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    AIMate: A Promising AI Productivity Application

    This application has the potential to address real user problems, meet market needs, and provide a positive user experience. However, the success of AIMate will depend on the effective implementation of marketing, product development, and branding strategies.

    Feasibility:

    • Technology: AIMate is built on modern and readily available Large Language Models (LLMs), indicating technical feasibility.
    • Resources: The development team has the necessary experience and expertise.
    • Business Model: AIMate can be offered as a subscription service, generating potential revenue streams.

    Sustainability:

    • Market Demand: The demand for AI productivity tools is growing, indicating a potential market for AIMate.
    • Competition: AIMate competes with other productivity applications but has a competitive advantage due to its integrated AI capabilities and high customizability.
    • Barriers to Entry: Developing a similar AI application requires significant resources and expertise, creating a barrier to market entry.

    Desirability:

    • Problem-Solving: AIMate addresses the problem of tool fragmentation and high cognitive load, appealing to users seeking more efficient solutions.
    • Features: AIMate offers a range of attractive features such as task automation, document processing, and high customizability.
    • User Experience: AIMate aims to provide an intuitive and user-friendly experience.

    Usefulness:

    • Enhanced Efficiency: AIMate helps users save time and complete tasks more effectively.
    • Improved Productivity: AIMate automates mundane tasks and allows users to focus on more important work.
    • Empowered Creativity: AIMate provides tools for research and document processing, enabling users to be more creative.

  • 0
    user-icon
    GhostlyGaze
    May 6, 2024 | 7:44 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    A Robust AI-Powered Productivity App

    AIMate stands out as a robust AI-powered productivity app designed to elevate personal efficiency, earning a strong four-star rating. Its comprehensive features such as multi-chat management, task automation, research tools, and document handling offer users a wide range of functionalities to streamline their daily workflows effectively.

    One of the app's key strengths is its customization options, allowing users to tailor AI responses to their specific needs. Integration with email and calendars further enhances its utility by centralizing important communication and scheduling tasks within a single platform.

    The inclusion of a platform for developing and sharing extensions adds an extra layer of versatility, enabling users to expand AIMate's functionality according to their evolving requirements. This adaptability makes AIMate not just a productivity tool but also a platform for continuous improvement and innovation.

    While the app's feature set is impressive, ensuring a seamless user experience and addressing any potential usability issues would further enhance its appeal. Additionally, ongoing updates and enhancements to keep pace with evolving productivity needs would solidify AIMate's position as an essential tool for personal and professional growth.

  • 0
    user-icon
    TrucTrixie
    May 6, 2024 | 5:06 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Further analysis of the most important milestones

    The milestones (4 milestones) are thoroughly presented. Among these milestones, which one do you think is the most important? I think it is milestone number 2 because it integrates many high technologies - LLM features and RAG integration, but this is still my personal subjective opinion. The team needs to focus a lot of resources on this milestone 2 and asking for $25,000 is worth it.

  • 0
    user-icon
    BlackCoffee
    May 4, 2024 | 1:17 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Professional qualifications in info technology, AI

    I don't have many opinions on this proposal, overall it's quite complete from a preparation perspective. What I want is for the team to be more specific in affirming the expertise of the members because this is a proposal that requires a diverse amount of knowledge about technology, AI, applications and blockhain.

  • 0
    user-icon
    Viclex Ad
    May 3, 2024 | 1:00 PM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    AIMATE Productivity

    AIMate has the potential to revolutionize personal efficiency through AI, but more information and careful planning will increase the likelihood that it will be delivered successfully.

    Feasibility:

    AIMate's concept, which takes advantage of scalable infrastructures and advances in AI, is technically and theoretically possible. Reliability is increased by the integration with other LLMs, which shows an understanding of existing AI capabilities.


    Viability:

    A thorough feasibility assessment requires more information on team experience, relationships, and commercial models, even though the project has a solid technical foundation. The budgetary allotment appears rational for the suggested benchmarks, augmenting the likelihood of feasibility.

    Desirability:

    AIMate provides a single AI-powered solution to the widespread problem of dispersed tools. Users find it more appealing because of its emphasis on user-centric design and interaction with commonplace tools like calendars and email.

    Usefulness:

    The value of AIMate is found in its capacity to greatly increase productivity via automation and AI-powered support. 

    The following are critical success factors:
    • Technical Expertise:

    Make sure the team has a variety of skills in blockchain, artificial intelligence, and app development.

    • User Feedback:

    Regularly collect user feedback to improve features and usability.

    • Strategic Partnerships:

    Form alliances with AI researchers and industry players to pursue growth prospects.

    • Marketing Strategy:

    Create a strong marketing plan to reach target users and highlight AIMate's advantages.

    Suggestions:
    • Team Information:

    Give each member of the team a thorough profile that highlights their relevant experience.

    • Business Model:

    Clearly define possible market segmentation, pricing schemes, and revenue sources.

    • User Experience:

    Make sure AIMate is simple to use and intuitive by conducting extensive UX testing.

    • Describe strategies for growing infrastructure and managing higher user demand in the scalability plan.

  • 0
    user-icon
    Akshara Manjunath
    May 2, 2024 | 8:42 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Game Changer in the AI Productivity Domain

    AIMate's proposal is impressive, offering a deep understanding of productivity challenges and a compelling solution with clear market advantages.

    The integration with everyday tools is a standout feature, expanding its usability and enhancing user-friendliness.

    This proposal not only addresses productivity hurdles but also delivers an innovative and competitive solution, setting a high standard in the AI productivity domain

  • 0
    user-icon
    Max1524
    May 2, 2024 | 1:17 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    The project creates quite high trust

    The seriousness of proposal presentation is a significant advantage that needs to be mentioned. Overall, it's specific and detailed.
    The presentation of milestones is impressive in its meticulousness. It would be even better if I could see a timeline associated with a specific date.

    user-icon
    Ahan M R
    May 2, 2024 | 7:18 AM
    Project Owner

    Thank you so much for the review, Max. I will be refining the proposal a bit more in a few days (currently working on a paper), so I'll be adding a doc that will be addressing the timelines and implementation details in form of low-level design (LLD). I submitted this initial draft to get comments from the community to see if I missed anything in terms of the "big picture". Cheers.

     

  • 0
    user-icon
    Joseph Gastoni
    Apr 30, 2024 | 2:58 PM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    It is a feasible and potentially useful project

    AIMate is a feasible and potentially useful project. However, the competitive landscape and long-term viability require careful consideration.

    Feasibility

    • The project seems technically feasible. The chosen technologies (Node.js, React) are widely used and well-suited for the project's goals.
    • Integrating multiple LLMs might require additional development effort compared to using a single model.
    • The initial development phase with a single LLM seems achievable.

    Viability

    • The project's long-term viability depends on user adoption and a sustainable revenue model.
    • Competition from existing productivity apps and AI assistants is high.

    Desirability

    • The concept of a unified AI productivity platform is desirable, especially if it offers customization and efficient task management.
    • The focus on user-centric design and customizable AI interactions is a plus.

    Usefulness

    • AIMate has the potential to improve user efficiency by automating tasks and centralizing information.
    • The RAG feature can enhance the accuracy and relevance of AI responses.

    Besides, the project should consider:

    • Focus on a strong initial value proposition:
      • Clearly define the target user group and tailor features to their specific needs.
      • Highlight the unique benefits of AIMate compared to existing solutions (e.g., LLM flexibility, RAG integration).
    • Develop a freemium model:
      • Offer a basic version with core features for free to attract users.
      • Introduce premium features (additional LLM access, increased storage) for paying users.
    • Build a strong community:
      • Encourage user feedback and suggestions to improve the platform and identify new features.
    • Phased Development:
      • Prioritize the development of core functionalities (chat, document handling) before adding complex features like multiple LLM integration.

    By addressing these points and focusing on a clear value proposition, AIMate can position itself for success in the competitive productivity app market.

  • 0
    user-icon
    Victor2815
    Apr 30, 2024 | 9:16 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    The project has potential and carefully presented

    It can be seen that you have prepared your project proposal very well, you have provided detailed information. Existing problems have also been clearly stated by the proponent. I love it. The problem solving solutions proposed by the team are very thorough step by step, which makes me believe that the project can run well and effectively. The important milestones and goals have been presented publicly and transparently, but I have a question: this will be a large project, in the future there may be many complex and difficult factors that arise different complexities, so do you have a plan to prepare for it? Will the project expand and expand in the future? I believe that with a careful and skilled team like you, there will not be too many difficulties when operating this project. We hope the project is successful.

    user-icon
    Ahan M R
    May 2, 2024 | 7:24 AM
    Project Owner

    Hey Victor, Thank you so much for your feedback, appreciate it. Regarding the complexities, yes, I have been thinking about it. The plan is get my low-level design (LLD) reviewed by a few more scientists which will be acting as mentors for this project. Some of the key takeaways from my previous experiences in terms of building LLM based solutions:

    1. Modular Architecture: Ensuring that the platform’s architecture is modular will be crucial. This approach will allow for updates, testing, and scaling of individual features without impacting the entire system.

    2. Continuous testing and Risk Management: I shall be ensuring to add a member to the team once the project is in good shape to help me with Continuous testing and Risk Management (Estimating biases in the model/ benchmarking the models so that, only 2-3 models can be maintained without reduction in quality of responses of the tool).

    Expansion Plans:

    1. Incremental Feature Rollout: Gradually we will be introducing new features to allow the team to manage growth sustainably. This strategy helps in dealing with complexities in smaller, manageable parts and ensures that each component is stable before expanding further (which is one of the biggest challenges if not done correctly).

     

  • 0
    user-icon
    adarshanand
    Apr 30, 2024 | 9:09 AM

    Overall

    5

    • Feasibility 4
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Much needed productivity tool using LLMs

    A single platform for managing daily tasks is really a much needed intervention in our lives. Solution proposed says that as far as problem is solvable in the daily tasks, LLM will help solve the task in a timely fashion. But in case when it is unclear or difficult to solve, LLM will help prepare milestones or launch plans for finishing the task which is amazing. Feasibility is possible since AI Agents can be used to create automation in LLMs as mentioned in the proposal. I hope this project does come into life in the near future. Good to highlight how the problem uses SNET integration or what SNET tools will be used in the software. 

    user-icon
    Ahan M R
    May 2, 2024 | 7:28 AM
    Project Owner

    Thanks for the review, Adarsh! I'm trying to find a good vector DB to use in the project to improve the latency for common questions/queries across users which will help in querying from cache itself. Yes, we will be using generation models from SNET as well, and will ensure to integrate atleast one more model into SNET as part of AIMate (mostly from our RAG toolkit), a good open-source alternative for quick prototying for your reference will be: https://github.com/bclavie/RAGatouille/tree/main

  • 0
    user-icon
    Aokishi
    Apr 30, 2024 | 8:13 AM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    A project can replace existing virtual assistants

    AIMate positions itself as an essential tool for personal and professional development in future. This is an interesting project that can improve daily workflow through the integration of AI into everyday tools. A special feature of AIMate is probably its support for many LLMs, so users can customize and personalize the experience. Additionally, the ability to retrieve information from documents gives AIMate the potential to become a new-generation intelligent virtual assistant. The budget required by the project is quite small compared to the scale of the project. We can compare based on the size of the budget that Bigtechs spend to develop virtual assistant applications. Although AIMate is proving to be an investment bargain, the project\'s timeline is proving too ambitious and optimistic. Risks during implementation and budget shortfalls need to be considered to set a more careful timeline for the project.

    user-icon
    Ahan M R
    May 2, 2024 | 7:31 AM
    Project Owner

    While AIMate's budget is relatively small compared to similar projects by larger tech companies, I'm hoping that you'll see this both as a strength and a weakness. A smaller budget requires more efficient use of resources but can limit the scope and speed of development, but I shall never compromise on the quality, so we should be good to go. This will also ensure that if really successful and good outreach for our tool, AIMate can hopefully put in a proposal in the future rounds of grants as well. 

  • 0
    user-icon
    Wei Dong
    Apr 29, 2024 | 10:40 PM

    Overall

    5

    • Feasibility 4
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Great use-case with innovative solution with SNET

    Pros

    • The approach to integrating multiple AI functionalities into a single platform is highly desirable. The ability to choose between different LLMs based on task specificity and the enhancement of AI responses through RAG makes it extremely attractive.
    • By combining regular chat, document handling, a stream/feed feature, and advanced AI capabilities in one tool, it addresses a significant market need for integrated productivity solutions.
    • The project appears economically viable with a well-thought-out budget that covers all necessary aspects from development to deployment.

    Cons

    • The only concern might be the ongoing costs associated with maintaining multiple LLMs and the necessary infrastructure, which could impact long-term sustainability without adequate financial planning.
    • The ambitious timeline for implementing these advanced features within three months might be slightly optimistic, considering the potential complexities involved in integration and testing. 

    Suggestion

    • Try to narrow down the scope of the project and reduce the timeline to 8-10 weeks (Focus on multiple LLM integration, Feature additions and productivity ideas) and cut down the development work related to build a software/tool, and have a good working MVP/MLP as part of this round of funding. If you're really able to get a working tool with all enhancements, that would actually be amazing. 

Summary

Overall Community

4.7

from 12 reviews
  • 5
    8
  • 4
    4
  • 3
    0
  • 2
    0
  • 1
    0

Feasibility

4.5

from 12 reviews

Viability

4.3

from 12 reviews

Desirabilty

4.7

from 12 reviews

Usefulness

4.8

from 12 reviews