AI FAQ 2025
M
Meeting 12/14/25
Presentation
Trello Card:
Recording
Code of Conduct / Anti Trust Policy
Join Slack https://join.slack.com/t/aifaqworkspace/shared_invite/zt-337k74jsl-tvH_4ct3zLj99dvZaf9nZw
Introduction
SNOWFLAKE MARKETPLACE UPDATE - Jayaram
Are we in the Marketplace????
https://drive.google.com/file/d/1q1JHLCZ2UMLGqQkye_z13uPy8ieqh76s/view (A.I.F.A.Q ?)
One Pager
https://www.canva.com/design/DAG5jOFYLkk/2d99ix93fV3ob6k9CxbRqw/edit?utm_content=DAG5jOFYLkk&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton Remove Marketing hype/ add features available now
https://mentorship.lfx.linuxfoundation.org/project/15e75e7b-fe14-44d4-9e08-cf19bf8d1e0a#Mentees
Completed
Available Now – Version 1
Read unstructured data directly from Snowflake stages: PDF, DOC/DOCX, CSV, TXT.
100% Snowflake-native — your data never leaves your account.
Enterprise-grade security, compliance, and auditability.
Role-based access and granular permissions.
Instant deployment via Snowflake Marketplace.
Zero infrastructure — no external systems required.
Instant answers using multi-agent Retrieval-Augmented Generation (RAG).
Coming Soon – Version 2
Iceberg-based connectivity.
Read structured & unstructured data from AWS S3, Azure Blob, Google Cloud Storage.
Large-scale ingestion for enterprise datasets.
Advanced multi-agent orchestration workflows.
Fast setup. Maximum security. Zero friction.
MULTI AGENT / Gitmesh -
https://docs.google.com/document/d/1v8G24uO9Zt-ym5SBx1ePquxPdVoyzeiU1jCilDphNos/edit?tab=t.0
https://docs.google.com/document/d/1zAKZcx08H9UnjxW_BS3MRVBNRyT7vVbZ-7B2JQnT9Og/edit?tab=t.0
Founders Institute
https://fi.co/prompts#revenue-business-models
Just a list of successful prompts to assist cohort members create materials and answer questions
INCUBATION
AIFAQ for Linux Foundation Incubation
Project Incubation Entry Considerations - Technical Advisory Council
Approved project proposals enter into Incubation. For new components and modules, a repository is created under the Hyperledger Github org . New features or capabilities must be handled through pull requests labeled with tags that identify the project and tag it as incubator. Pull requests ideally are capable of being enabled and disabled with feature-flags.
Projects in Incubation can overlap with one another. Entering Incubation is meant to be fairly easy to allow for community exploration of different ideas.
After a project qualifies to be declared Graduated, the project maintainers can then vote to request a graduation review by the TOC.
Entering Incubation does not guarantee that the project will eventually get to the Graduated state. Projects may never get to the Graduated state.
Projects seeking to graduate from Incubation must meet the criteria defined in the Incubation Exit Criteria document.
LINKEDIN POSTS
BM
Social Media Schedule
DEVIES “Innovator of the Year” description tailored for Gianluca Capuzzi
New features and code cleanup
COST ISSUES:
https://docs.google.com/spreadsheets/d/1RIhQTvsoyA9Ge4WWRxtjEKZGaiQFGcmktrQuVDCH1ps/edit?gid=0#gid=0
Project Incubation Entry Considerations - Technical Advisory Council
AIFAQ PROJECT 2025
ㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤ
✨ AIFAQ 2025 Goals
1. Overall Project Goals for AIFAQ (Organizational Level)
(To move from Sandbox to Incubation at LFDT and get early paying customers)
a. Technical and Community Goals (to qualify for Incubation at LFDT):
Expand Active Contributors: Grow to at least 5+ consistent contributors outside the founding team, documented in GitHub activity.
Establish Governance: Implement a clear Technical Steering Committee (TSC) charter and lightweight governance model aligned with LFDT best practices.
Production-Ready MVP: Deliver a production-quality MVP for the AIFAQ platform, showing enterprise-grade performance, security, and usability.
Document Use Cases: Publish 2–3 real-world case studies demonstrating AIFAQ solving enterprise FAQs or documentation automation.
Community Engagement: Host at least 2 public webinars, 1 workshop, and quarterly updates to the LFDT and open-source community.
Security and Compliance: Complete basic security audits and compliance documentation (e.g., data handling policies).
b. Business Goals (to build customer traction):
Land 2–3 Pilot Customers: Sign early-stage pilot programs with at least 2–3 enterprise customers (offering free or discounted pilots initially).
Launch Paid Offering: Define pricing tiers and launch a beta paid service (SaaS or managed service model) by Q4 2025.
Marketing & Sales Enablement:
Publish one white paper describing AIFAQ’s unique value proposition.
Create sales deck, website landing page, and downloadable 1-pager for outreach.
Investor and Grant Readiness: Prepare a pitch deck and start conversations with potential grants or impact investors aligned with AI/OSS innovation.
Mentorship Project Goals
A. Multi-Agent RAG (Retrieval-Augmented Generation) System
Technical Goals:
Multi-Agent Architecture: Build a working prototype of modular agents (retriever, router, summarizer, responder) communicating through an orchestrator.
Plugin Support: Enable pluggable data sources (e.g., Confluence, GitHub Wikis, custom PDFs).
Scalable RAG Pipeline: Implement efficient document chunking, embedding caching, and vector search optimizations.
Agent Collaboration Logic: Develop logic for agents to delegate subtasks among themselves dynamically (example: a retriever agent passes relevant docs to a summarizer agent).
Baseline Evaluation Metrics: Create benchmark tests to measure precision, latency, and accuracy improvements vs. a simple single-agent model.
Community Deliverables:
Public Demo Repository with a clean README and deployment instructions (Docker optional).
Technical Blog Post: Write a blog post showcasing how multi-agent RAG outperforms single-agent baseline.
B. Snowflake Connector for AIFAQ
Technical Goals:
Data Pipeline Integration: Build a robust connector to extract structured FAQ or knowledge base data from Snowflake tables.
Authentication & Security: Implement secure authentication (OAuth, token-based) for enterprise Snowflake accounts.
Connector API: Develop a simple API interface to trigger data pulls, syncs, and refreshes on-demand.
Auto-Sync and Scheduling: Allow scheduled pull jobs and delta updates (new or modified rows only).
Business/Strategic Deliverables:
Enterprise Use Case Demo: Showcase pulling an enterprise customer’s knowledge base into AIFAQ using Snowflake in a demo environment.
Documentation & SDK: Release an open-source SDK or connector package on GitHub with easy-to-follow examples.
Outbound Messaging: Write 1 LinkedIn article or case study targeting Snowflake’s huge enterprise customer base (joint marketing potential if successful).
MEETING NOTES
Meeting 10/08/25
Trello Card:
Recording
Code of Conduct / Anti Trust Policy
Join Slack https://join.slack.com/t/aifaqworkspace/shared_invite/zt-337k74jsl-tvH_4ct3zLj99dvZaf9nZw
Introduction
Welcome Back Tripur
MENTORSHIP
SNOWFLAKE MARKETPLACE UPDATE - Jayaram
https://mentorship.lfx.linuxfoundation.org/project/15e75e7b-fe14-44d4-9e08-cf19bf8d1e0a#Mentees
Completed
Are we in the Marketplace????
https://drive.google.com/file/d/1q1JHLCZ2UMLGqQkye_z13uPy8ieqh76s/view (A.I.F.A.Q ?)
One Pager
https://www.canva.com/design/DAG5jOFYLkk/2d99ix93fV3ob6k9CxbRqw/edit?utm_content=DAG5jOFYLkk&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton Remove Marketing hype/ add features available now
Available Now – Version 1
Read unstructured data directly from Snowflake stages: PDF, DOC/DOCX, CSV, TXT.
100% Snowflake-native — your data never leaves your account.
Enterprise-grade security, compliance, and auditability.
Role-based access and granular permissions.
Instant deployment via Snowflake Marketplace.
Zero infrastructure — no external systems required.
Instant answers using multi-agent Retrieval-Augmented Generation (RAG).
Coming Soon – Version 2
Iceberg-based connectivity.
Read structured & unstructured data from AWS S3, Azure Blob, Google Cloud Storage.
Large-scale ingestion for enterprise datasets.
Advanced multi-agent orchestration workflows.
Fast setup. Maximum security. Zero friction.
MULTI AGENT / Gitmesh -
https://docs.google.com/document/d/1v8G24uO9Zt-ym5SBx1ePquxPdVoyzeiU1jCilDphNos/edit?tab=t.0
https://docs.google.com/document/d/1zAKZcx08H9UnjxW_BS3MRVBNRyT7vVbZ-7B2JQnT9Og/edit?tab=t.0
INCUBATION
AIFAQ for Linux Foundation Incubation
Project Incubation Entry Considerations - Technical Advisory Council
Approved project proposals enter into Incubation. For new components and modules, a repository is created under the Hyperledger Github org . New features or capabilities must be handled through pull requests labeled with tags that identify the project and tag it as incubator. Pull requests ideally are capable of being enabled and disabled with feature-flags.
Projects in Incubation can overlap with one another. Entering Incubation is meant to be fairly easy to allow for community exploration of different ideas.
After a project qualifies to be declared Graduated, the project maintainers can then vote to request a graduation review by the TOC.
Entering Incubation does not guarantee that the project will eventually get to the Graduated state. Projects may never get to the Graduated state.
Projects seeking to graduate from Incubation must meet the criteria defined in the Incubation Exit Criteria document.
Mentorship Issues
SNOWFLAKE
MULTI AGENT
LINKEDIN POSTS
BM
Social Media Schedule
New features and code cleanup
COST ISSUES:
https://docs.google.com/spreadsheets/d/1RIhQTvsoyA9Ge4WWRxtjEKZGaiQFGcmktrQuVDCH1ps/edit?gid=0#gid=0
Project Incubation Entry Considerations - Technical Advisory Council AIFAQ PROJECT 2025
Quick Recap
The team discussed transitioning from a mentorship program to an incubation stage, with plans to present a proposal to the Linux Foundation Technical Steering Committee and review results from a 3-month discovery activity. They explored development and testing of a Snowflake-related project, including discussions about positioning, functionality, and potential features, while emphasizing the importance of user feedback and security concerns. The team focused on productizing their MVP by completing core features before adding new functionality, with plans to reach out to chapter leaders for introductions and testing purposes.
Next Steps
Bobbi: Send proposal and related documents to David (after review and completion)
Gianluca: Review and update the proposal and deliverables, including checking DCO sign-off and adding Tripur's name
Gianluca: Work on an easy installation version of the code (chatbot only, no analytics, no login) for community testing and notify Bobbi when ready
Gianluca: Contact Jerem/Jaren to request code, platform access, and current version/configuration for Snowflake project, and begin knowledge transfer/ownership
Bobbi: Send Snowflake code/test documentation to Gianluca
Bobbi: Set up Gianluca with Snowflake admin access and coordinate a session for him to log in and explore the Snowflake instance
Peter: Send email to Don (and CC team) to request introduction to additional Founders Institute chapter leaders for broader user testing
Bobbi: Follow up with Don regarding introductions to other chapter leaders
Gianluca: Send direct message to Jerem/Jaren on LinkedIn to check availability for knowledge transfer/demo of Snowflake work
Bobbi and Gianluca: Schedule and conduct a session for Gianluca to access and familiarize with the Snowflake application (pending Jerem's availability)
Team: Internally test Snowflake integration before considering external release
Gianluca: Continue discovery interviews with potential customers and advisors to gather feedback on features and market needs
Summary
Incubation Strategy and Project Updates:The team discussed transitioning from the mentorship program to an incubation stage, with two new projects: a Snowflake sub-project of AI FAQ and a Git mesh wrapper for GitHub. Bobbi explained their strategy for positioning the Snowflake project to target small and medium enterprises, while also working on getting the Founders Institute website to include their curriculum and mentor information in phase two. The team plans to present a proposal to the Linux Foundation Technical Steering Committee to move from labs to incubation, with Gianluca sharing that he has been reviewing results from a 3-month discovery activity.
Snowflake Project Review and Testing:The team discussed the development and testing of a Snowflake-related project, with Peter emphasizing the need for a review and demo process to clarify current features and functionality. They considered expanding access to more FI cohort leaders for free usage to gather broader feedback, while also addressing missing analytics functionality. Bobbi noted uncertainty about Snowflake approval status and mentioned pending assistance from JRAM to access and demonstrate the project's features in the Snowflake portal.
AI Product Development Strategy:The team discussed product development and market feedback for their AI project. Tripur emphasized the importance of having both unpaid and paid potential customers to validate the product's unique positioning and identify areas for improvement. They agreed on the need for regular user feedback and suggested conducting structured interviews to gather insights. Gianluca inquired about selecting "killer features" for future development, and Tripur advised focusing on recurring feedback from users and positioning the product to address specific market gaps in the AI space.
AIFAQ's Private Knowledge Base Strategy:The team discussed AIFAQ's positioning and potential features, with Bobbi emphasizing the importance of a private knowledge base as a key differentiator from public AI tools like ChatGPT. Peter agreed that ease of accessing private information remains a core value proposition, though security concerns need to be addressed. The team is considering Snowflake as a solution for security issues and is working on making the product easier to install and deploy.
AI FAQ Deployment Strategy Discussion:The team discussed deployment and installation options for their AI FAQ product. Gianluca suggested creating a downloadable version that users could run locally, allowing them to set up a knowledge base offline and maintain security. Bobbi and Peter emphasized the need to improve the overall user journey, from learning about the product to configuring and accessing it. They agreed to focus on basic functionality first, with plans to add more features and data sources later. The team also acknowledged that custom setup by Gianluca would likely be necessary for some time.
Data Sync and Roadmap Updates:The team discussed the need to sync data between the website and documents without manual uploads, with Peter suggesting leveraging Snowflake's functionality for role-based permissions. Bobbi confirmed that work was ongoing on the roadmap feature and offered to provide Gianluca with access to the Snowflake playground. Gianluca mentioned preparing a public roadmap for future features and working on an easy installation process for the chatbot, which would allow users to access it without analytics or login requirements.
Snowflake Project and Testing Discussion:Bobbi and Gianluca discussed the project deliverables and roadmap, including the need to add Gianluca's name to a worksheet. They also addressed the issue of testing the Snowflake marketplace content, with Bobbi explaining that they need to determine how to access and test it using JRM's data warehouses. Peter emphasized the need for Gianluca to take ownership of the Snowflake code and review it. Bobbi agreed to provide Gianluca with some code examples from the Snowflake account, and they discussed the limitations of Snowflake Cortex's availability in certain regions.
Core Functionality Prioritization Discussion:The team discussed the importance of testing and validating existing features before developing new ones, with Peter emphasizing the need to focus on core functionality such as role-based access, knowledge base integration, and easy setup. Gianluca raised concerns about the lack of customer requests for these features, prompting a debate about prioritizing customer feedback versus internal goals. The team agreed to concentrate on bringing the current functionality to market rather than pursuing new features, with Peter specifically highlighting the potential for Snowflake customers to pay for these features.
MVP Productization Strategy Discussion:The team discussed productizing their MVP, focusing on completing core features like role-based access and auto-update before adding new functionality. Bobbi and Peter agreed that getting the product to market is more important than developing additional features, while Tripur emphasized the need to improve the existing product's technology and foundation before considering differentiation features. The team decided to reach out to chapter leaders for introductions and to test Snowflake analytics, with Bobbi offering to help set up a meeting with Jerem to demonstrate Snowflake functionality.
Meeting 12/01/25
Presentation
Trello Card:
Recording
Code of Conduct / Anti Trust Policy
Join Slack https://join.slack.com/t/aifaqworkspace/shared_invite/zt-337k74jsl-tvH_4ct3zLj99dvZaf9nZw
Introduction
MENTORSHIP
https://mentorship.lfx.linuxfoundation.org/project/15e75e7b-fe14-44d4-9e08-cf19bf8d1e0a#Mentees
SNOWFLAKE ISSUES
Are we in the Marketplace????
https://drive.google.com/file/d/1q1JHLCZ2UMLGqQkye_z13uPy8ieqh76s/view
One Pager
Available Now – Version 1
Read unstructured data directly from Snowflake stages: PDF, DOC/DOCX, CSV, TXT.
100% Snowflake-native — your data never leaves your account.
Enterprise-grade security, compliance, and auditability.
Role-based access and granular permissions.
Instant deployment via Snowflake Marketplace.
Zero infrastructure — no external systems required.
Instant answers using multi-agent Retrieval-Augmented Generation (RAG).
Coming Soon – Version 2
Iceberg-based connectivity.
Read structured & unstructured data from AWS S3, Azure Blob, Google Cloud Storage.
Large-scale ingestion for enterprise datasets.
Advanced multi-agent orchestration workflows.
Fast setup. Maximum security. Zero friction.
MULTI AGENT / Gitmesh
https://docs.google.com/document/d/1v8G24uO9Zt-ym5SBx1ePquxPdVoyzeiU1jCilDphNos/edit?tab=t.0
https://docs.google.com/document/d/1zAKZcx08H9UnjxW_BS3MRVBNRyT7vVbZ-7B2JQnT9Og/edit?tab=t.0
https://www.alveoli.app/gitmesh_demo.mp4
INCUBATION
AIFAQ for Linux Foundation Incubation
Project Incubation Entry Considerations - Technical Advisory Council
Approved project proposals enter into Incubation. For new components and modules, a repository is created under the Hyperledger Github org . New features or capabilities must be handled through pull requests labeled with tags that identify the project and tag it as incubator. Pull requests ideally are capable of being enabled and disabled with feature-flags.
Projects in Incubation can overlap with one another. Entering Incubation is meant to be fairly easy to allow for community exploration of different ideas.
After a project qualifies to be declared Graduated, the project maintainers can then vote to request a graduation review by the TOC.
Entering Incubation does not guarantee that the project will eventually get to the Graduated state. Projects may never get to the Graduated state.
Projects seeking to graduate from Incubation must meet the criteria defined in the Incubation Exit Criteria document.
Mentorship Issues
SNOWFLAKE
MULTI AGENT
LINKEDIN POSTS
BM
Social Media Schedule
New features and code cleanup
COST ISSUES:
https://docs.google.com/spreadsheets/d/1RIhQTvsoyA9Ge4WWRxtjEKZGaiQFGcmktrQuVDCH1ps/edit?gid=0#gid=0
Dev Issues Test
LFDT
https://www.aifaq.pro/linux-foundation-dt.html
Quick Recap
The team discussed various project proposals including incubation efforts, TAC changes, and Gitmesh development, with specific focus on licensing requirements and naming conventions. They explored pricing models and technical specifications for their system, including AWS instance sizing and cost analysis for different user scenarios. The conversation ended with discussions about testing plans, user guides for Snowflake, and potential promotional opportunities for their open-source LLM project, along with plans for future meetings and reviews of proposals.
Next Steps
Bobbi Muscara: Send the incubation proposal to David and ask if more sponsors are needed; follow up with David about project naming/trademark requirements.
Bobbi Muscara: Investigate the AIFAQ-related mentorship project found on the Linux Foundation Decentralized Trust and determine if Arun can serve as an additional sponsor.
Bobbi Muscara: Schedule and send out meeting invite for Snowflake testing with JRAM and team for Thursday at 9:00.
Bobbi Muscara: Post link to the New York event in February in the chat and consider team attendance if project is ready.
Bobbi Muscara: Reach out to Founders Institute leadership (or coordinate with Don) to request introductions to other chapter leaders for AIFAQ rollout.
Gianluca: Check and update the license/notice file and ensure all dependencies are Apache 2.0 compatible, and that the project is not using non-Apache 2.0/GPR-licensed tools before applying for incubation.
Gianluca: Recalculate and document the actual costs for Mistral (splitting embedding and inference costs), including unit economics per query, and update the cost spreadsheet with formulas and data sources for review.
Gianluca: Check the current configuration for answer size (standard/verbose) in existing chatbot deployments and confirm the setting.
All team members: Review the proposal on the wiki page (or Google Drive when shared), especially items marked in red, and update/correct as needed before submission to TAC.
Bobbi Muscara: Share the proposal in Slack (and/or Google Drive) for collaborative editing and finalization before sending to TAC.
Bobbi Muscara: Consider discussing the new project proposal within the broader Trust Over IP Foundation community before TAC submission.
Bobbi Muscara: Postpone the regular advisor meeting by a week to allow for Snowflake testing and preparation of user guides, one-pager, and video for Tim.
Bobbi Muscara: Prepare user guides and one-pager for Snowflake demo and share with Tim at next meeting.
Gianluca: Conduct load testing or usage analysis to better estimate required AWS instance sizes for different numbers of users and update the calculator/POC guidance accordingly.
Gianluca: Double-check all infrastructure and API cost components and confirm there are no additional costs not already accounted for in the pricing model.
Summary
Incubation Project Alignment Discussion:Gianluca and Bobbi discussed their work on the incubation project, noting that their efforts were aligned as they had covered similar content in both slides and a wiki page. They agreed to proceed with presenting the slides, and Bobbi mentioned creating a formal proposal for the incubation project, which she would share on the wiki page. The conversation briefly touched on the weather, with Bobbi mentioning her upcoming trip to Florida and discussing the challenges of dealing with snow in the Northeast.
TAC Proposal and Sponsorship Discussion:Bobbi Muscara discussed a proposal for TAC changes, which involves formalizing five key aspects including the roadmap and codebase. She noted that while they have diverse maintainers, they may need to create additional pull requests to demonstrate more corporate diversity. Bobbi also mentioned the need for more project sponsors from different organizations, and she plans to reach out to Arun about potentially serving as an additional sponsor. The team discussed the legal implications of their project name, AIFAQ, and whether they might need to change it for open source purposes. Bobbi plans to consult with David about the proposal and timing, as it could either be the last action of the current board or the first of the new board.
Gitmesh Licensing and Proposal Review:Bobbi Muscara discussed a proposal for Gitmesh, including naming conventions for different editions and the need to comply with Apache 2 licensing requirements. Gianluca mentioned checking dependencies and filling out a notice file before applying for the license. Bobbi also mentioned being a member of the Trust Over IP Foundation and potentially discussing the proposal with them. The conversation ended with technical difficulties preventing Bobbi from sharing her screen to review the wiki page containing the proposal.
Snowflake User Guides Testing Plans:Bobbi Muscara discussed plans for testing and preparing user guides for Snowflake, with a meeting scheduled for Thursday at 9 AM. She proposed postponing the advisor meeting to align with a presentation to Tim next Tuesday, where they can showcase the completed materials. Bobbi also mentioned a potential event in New York for LLM Day in February and suggested exploring opportunities to promote their open-source LLM project. Peter agreed with Bobbi's approach and emphasized the need for both bottom-up and top-down strategies to roll out AIFAQ to other chapter leaders, suggesting Don could help with introductions. Bobbi agreed to follow up with Don via email and noted she would reach out to Founders Institute leadership as previously discussed.
Linux Foundation Support Strategy:The team discussed using more support from the Linux Foundation during the incubation phase, with Bobbi suggesting testing a developer tool on their website before promoting it through the TAC newsletter. Peter introduced the concept of the "Mom Test" book, which helps validate ideas by focusing on past behavior rather than hypothetical future actions, and recommended it for customer interviews. The conversation ended with a brief discussion about a calculator tool, though the details were not fully explored.
AWS Instance Sizing and Pricing:The team discussed AWS instance sizing and pricing for different user scenarios. Gianluca explained that for proof of concept, a T3 instance with 2GB of memory would be sufficient, costing approximately $55 per month. They clarified that each new customer would get their own AWS instance, with different instance types recommended based on the number of users. The discussion revealed that the current architecture uses siloed instances for each company, rather than a multi-tenant approach.
System Pricing and Performance Review:The team discussed pricing and technical details for a system. They agreed on a standard account cost of $55 per month, which includes 4GB of storage. Gianluca mentioned that they had tested the system with a medium machine and some users, finding good performance. Peter suggested doing load testing to better understand system capacity. They also discussed the cost per query for the system, with Gianluca explaining it was estimated based on their history. Peter requested more information on how the token prices were calculated to better understand the pricing model.
System Cost Analysis and Pricing:Peter and Gianluca discussed the cost analysis of their system, focusing on determining the unit economics per question. Peter requested Gianluca to recalculate the costs by breaking down the components, including embedding and inference, and to provide a clear formula for the calculations. They agreed to start fresh with the first question in December to accurately determine the number of tokens used for input and output. Peter emphasized the need to verify all components and ensure the pricing is accurate. Bobbi Muscara mentioned a meeting scheduled for Thursday morning regarding snowflake testing with JRAM and reminded everyone to review the wiki page containing the proposal and roadmap, noting that uncertain information is marked in red for verification.
Meeting 11/23/25
Presentation
Trello Card:
Recording
Code of Conduct / Anti Trust Policy
Join Slack https://join.slack.com/t/aifaqworkspace/shared_invite/zt-337k74jsl-tvH_4ct3zLj99dvZaf9nZw
Introduction
MENTORSHIP
SNOWFLAKE ISSUES
Are we in the Marketplace????
https://drive.google.com/file/d/1q1JHLCZ2UMLGqQkye_z13uPy8ieqh76s/view
One Pager
Available Now – Version 1
Read unstructured data directly from Snowflake stages: PDF, DOC/DOCX, CSV, TXT.
100% Snowflake-native — your data never leaves your account.
Enterprise-grade security, compliance, and auditability.
Role-based access and granular permissions.
Instant deployment via Snowflake Marketplace.
Zero infrastructure — no external systems required.
Instant answers using multi-agent Retrieval-Augmented Generation (RAG).
Coming Soon – Version 2
Iceberg-based connectivity.
Read structured & unstructured data from AWS S3, Azure Blob, Google Cloud Storage.
Large-scale ingestion for enterprise datasets.
Advanced multi-agent orchestration workflows.
Fast setup. Maximum security. Zero friction.
MULTI AGENT / Gitmesh
https://docs.google.com/document/d/1v8G24uO9Zt-ym5SBx1ePquxPdVoyzeiU1jCilDphNos/edit?tab=t.0
https://docs.google.com/document/d/1zAKZcx08H9UnjxW_BS3MRVBNRyT7vVbZ-7B2JQnT9Og/edit?tab=t.0
https://www.alveoli.app/gitmesh_demo.mp4
INCUBATION
AIFAQ for Linux Foundation Incubation
Project Incubation Entry Considerations - Technical Advisory Council
Approved project proposals enter into Incubation. For new components and modules, a repository is created under the Hyperledger Github org . New features or capabilities must be handled through pull requests labeled with tags that identify the project and tag it as incubator. Pull requests ideally are capable of being enabled and disabled with feature-flags.
Projects in Incubation can overlap with one another. Entering Incubation is meant to be fairly easy to allow for community exploration of different ideas.
After a project qualifies to be declared Graduated, the project maintainers can then vote to request a graduation review by the TOC.
Entering Incubation does not guarantee that the project will eventually get to the Graduated state. Projects may never get to the Graduated state.
Projects seeking to graduate from Incubation must meet the criteria defined in the Incubation Exit Criteria document.