Introduction
Project Name: Sia Knowledge-Base System
Name of the Organization Submitting the Proposal: Daltonic LLC
Describe Your Project:
Sia Knowledge-Base System is an innovative platform that leverages artificial intelligence to enhance customer support and knowledge management. By integrating with popular communication channels like Discord and Telegram, it provides instant, accurate responses to user inquiries, improving satisfaction and operational efficiency. The system automatically scrapes and updates its knowledge base from diverse sources, ensuring that information remains current and readily accessible. For Sia, this project will deploy a customized version of the system, integrating Sia’s resources, documentation and community FAQs to support its Discord community with 24/7 automated assistance.
Video Demo
See how the AI Scraper processes queries and delivers real-time answers.
How It Works: Simplified Architecture
The AI Scraper operates in five simple steps:
- Request Received: A query comes through web, bot, or API.
- Authentication: The system verifies the request securely.
- Data Retrieval: Fetches relevant info using RAG technology.
- Response Generation: AI crafts an accurate answer with an LLM.
- Delivery: Sends the response back instantly.
Who Benefits Your Project:
The Sia ecosystem and its community are the primary beneficiaries of this project:
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Sia Users: Individuals using Sia’s decentralized storage network will receive instant answers to technical and operational questions, improving their experience and engagement.
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Sia Developers and Contributors: Those building on or supporting Sia’s platform will gain quick access to documentation and troubleshooting resources via Discord.
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Sia Foundation and Team: The system reduces the burden on human moderators and support staff, allowing them to focus on strategic initiatives while fostering a more self-sufficient community.
How Does the Project Serve the Foundation’s Mission of User-Owned Data:
This project supports the Sia Foundation’s mission by giving users direct, immediate access to essential information without relying on centralized intermediaries. By automating responses drawn from Sia’s own resources, it strengthens user autonomy, enhances community participation, and promotes a decentralized, user-driven support model within the Sia ecosystem.
Jurisdiction Compliance:
- Are you a resident of any jurisdiction on the list? No
- Will your payment bank account be located in any jurisdiction on the list? No
Grant Specifics
Project budget:
We requests the budget of $25,000 over the period of 4 months to develop, customize, and deploy the AI Scraper Knowledge-Base System for Sia. This will cover for:
- Salary for a UI/UX designer, Frontend, and backend developer.
- Upgrade of the MVP to a customized system with Sia’s data sources
- Deployment of the customized system with Sia’s data sources (e.g., online resources, docs, etc).
- Basic Discord bot setup and a simple web interface.
- Branding, UI Customizations, and backend enhancements.
- Minor bot behavior adjustments for Sia-specific queries.
- Integration with N8N workflows system monitoring, alerts, and automation.
- Includes domain and server hosting for the first full year post-deployment.
- Initial test, deployment, and scaling optimizations.
Timeline with Measurable Objectives and Goals:
The project spans 4 months with clear milestones:
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Milestone 1:
- Initiation of UI/UX development and branding.
- Integration of an open-source search engine for Sia online data sources.
- Enhancing MVP to gather Sia data sources from inputs and the web.
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Milestone 2:
- Customize Frontend with new UI/UX designs and additional features
- Perform Backend upgrades to reflect additional UI/UX features
- Integrate the Backend upgrades with the Frontend
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Milestone 3:
- Improve and Deploy Discord bot with enhanced behaviors
- Optimize backend to handle higher concurrent connections
- Addition of chat memory for recollections and context awareness.
- Incorporate high concurrency mitigation strategy (Caching, Rate Limiting, Parallel processing, etc.)
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Milestone 4:
- Integrate N8N workflow to ensure system monitoring, alerts, and automation
- Test, gather feedback, and deploy live system
- Test with multiple Open-source LLMs for the most suitable on the host machine specs
- Perform system adjustments based on feedback
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Deliverable: Fully functional AI Scraper Knowledge-base for Sia’s community
Potential risks that will affect the outcome of the project:
Risk: Resource Limitations Impacting Performance and AI Accuracy
The system may face challenges due to insufficient hardware resources—such as CPU, RAM, or storage—that can affect both performance and the accuracy of AI responses. Under high concurrent load, or when processing complex queries with advanced LLMs, limited computational power can lead to slow responses, timeouts, or system crashes. Additionally, inadequate resources may hinder the ability to run high-parameter models at optimal performance levels, potentially reducing AI accuracy below 100%.
Mitigation Measures:
- Minimum Hardware Requirements:
Ensure the host machine has at least 10 vCPU cores, 32GB of RAM, and 250GB of NVMe storage to support moderate to high traffic and advanced AI computations. - Caching:
Implement caching for frequently accessed data to alleviate database strain and improve response times. - Rate Limiting:
Limit the number of requests per user over a short period to prevent abuse and manage load effectively. - Asynchronous Processing:
Offload non-urgent tasks to background processes, freeing up resources for immediate query handling. - Monitoring and Alerts:
Utilize real-time monitoring tools and set up alerts to proactively manage resource usage, allowing for timely scaling or performance optimization.
Future Plans: Agentization and Expanding Capabilities
As part of our long-term vision for the Sia Knowledge-Base System, we aim to incorporate intelligent AI Agents to handle a wider array of tasks beyond simple question-answering. These agents will function as specialized, autonomous workers embedded within the system, capable of executing advanced workflows to further enhance user experience and community management.
What is Agentization?
Agentization refers to the transformation of the knowledge-base system into a multi-agent ecosystem, where dedicated AI Agents manage distinct responsibilities, each with its own specialized logic and action triggers. These agents will communicate with each other, the backend, and external platforms to independently complete tasks based on user input or system events.
Planned Agents and Capabilities
| Agent Name | Description |
|---|---|
| Onboarding Agent | Guides new users through Sia’s ecosystem, explaining key concepts, features, and how to get started. It will personalize the onboarding flow based on user type (developer, storage user, etc.). |
| Announcement Agent | Automatically drafts and posts important announcements, updates, and reminders across Discord, Telegram, and other linked platforms. |
| Booking Agent | Enables users to book sessions for scheduled Sia community events, such as AMA sessions, developer office hours, or technical workshops. |
| Web Search Agent | Performs real-time internet browsing to fetch up-to-the-minute data when requested information is not available within Sia’s internal knowledge base. This ensures the system remains current, even when new information emerges outside predefined sources. |
| Moderation Agent | Assists community moderators by flagging inappropriate content, escalating disputes, or automatically answering frequently asked compliance or security-related questions. |
| Chat Widget Agent | Provides an interactive chat interface directly embedded in Sia’s website or portal, offering instant answers, resource links, and the ability to escalate complex inquiries to human support beyond the discord server. |
Benefits of Agentization
Automation at Scale: Agents handle repetitive tasks autonomously, reducing manual workload for community managers and support teams.
Proactive User Support: Agents anticipate user needs based on real-time activity, offering help before users even ask.
Real-Time Data Fusion: By browsing the web and integrating external data sources, agents can enrich Sia’s internal knowledge with external insights when necessary.
Development Information
Will All of Your Project’s Code Be Open-Source?
Yes, all project code will be open-source, ensuring transparency and community access.
Leave a Link Where Code Will Be Accessible for Review:
GitHub Link
Do You Agree to Submit Monthly Progress Reports?
Yes, we agree to submit monthly progress reports on the Sia Grants Forum.
Contact Info
Email: [email protected]
Website: https://daltonicllc.com