You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This is a list of questions that need to be answered by the contributor in order to allow a new project to pass to the approval stage of onboarding.
Introduction
Moody’s has been building a Generative AI platform since 2023 to empower use cases across the organization. The platform covers use case development, distribution, and integration with all the governance and model management features needed in the financial industry.
This platform has allowed Moody’s to produce hundreds of use cases and tools both internal and external, and leverage GenAI capabilities across the board. Moody’s has seen a need in the market for companies to move from a “Proof of Concept” into production, or to deploy GenAI solutions in a compliant and secure way. And none of the solutions in the market offer this today for the Financial Industry.
Business Problem
Generative AI provides a very quick and effortless way to create Proofs of Concept of use cases that can generate extensive value to the organization. However, moving those PoCs into production is usually a big challenge for enterprises, specifically in a regulated industry such as finance.
Building a robust and scalable orchestration platform to experiment, deliver use cases, manage AI models, apply the appropriate guard rails, content filters and logging mechanisms, build agents, etc. And at the same time keep the pace of how the technology evolves is proving very difficult for most companies.
Using an Open-Source solution that provides full transparency of the code, extendibility, and customization, as well as deployment on the company’s private cloud can reduce the time to delivery, increase security and reduce cost on any company AI journey.
Proposed Solution
The Moody’s GenAI platform has enabled Moody’s to create, experiment and deploy hundreds of GenAI use cases in production. It has provided the right scaffolding for any team to add their own data / skills, combine them with others and provide solutions to users in the form of chat boxes / bots, assistants, agents, background processes, conversational tools, writing tools, correction features, etc.
We are offering our knowledge and experience to create a similar platform for the community so that anybody can benefit. It is an opensource platform built using Moody’s learnings from working with GenAI. We believe this can save thousands of development hours on deploying GenAI use cases in production.
This platform will be the perfect boilerplate to create new uses cases or apply new frameworks and technologies. For example, adding an “Agentic Framework” or a “GraphRAG” based technology are some of the projects of what we would like to add to the platform to make it even more complete.
Goals
The goal of this project is to provide a GenAI platform for the financial industry that supports full ingestion of various document types and integrates with Active Directory entitlements for data access and permissioning on entitlements.
It is a platform that empowers:
Reusability: Easier creation and deployment of new GenAI uses cases in finance. Empower FIs to increase speed to market, enable productivity, and reduce technical debt.
Data Interoperability: Content / inference across data silos. User’s data, external datasets and ingested documents based on the user behavior and interactions. Discoverability of relevant and contextual insights. With entity extraction and recognition.
Future proofing: Future proofing products with new developments on AI.
Distribution: Enabling distribution to 3rd party platforms in a transparent and cost efficient manner.
How it would work
Install the full platform in your own Private Azure instance. Access to the code through GitHub (Azure now, any cloud provider in next version)
Add your own skills / data access. Improve the platform.
Leverage the already existing skills / datasets and apply them to your assistants.
Use your own managed or self-hosted LLMs and Guardrails
Continuous development on new features (i.e. Agents framework)
Product Definition and Features
The platform is built for developers and AI engineers that allows quick use case generation. It supports a full ingestion of PDFs, Word, PPT, txt, html, mp3, mp4, and integrates with Active Directory for data entitlements for RAG use cases. It also integrates an “agentic” system (Build Your Own Assistant) that can connect skills, both in the platform or developed by the user.
The platform will be available through comprehensive APIs and web. It allows users to create custom use cases and share them across the organization.
Full Open Source (License TBD)
Based on Langchain
Deployable in any cloud (Azure for now)
Integrable with SSO and Active Directory.
Content filtering and tracing
Model agnostic with Model Gateway for balancing
Compatible with benchmarking tools (Langsmith)
Document ingestion and assistant creator
Pluggable and extendable
API based
Audits and logs
Tentative Roadmap
Move existing code from internal repository to FINOS
Current State
The platform is currently being built following the current high level architecture diagram:
Existing Materials
If materials already exist, provide a link to them that Foundation staff can access - if it's in a private GitHub.com repositories, you should invite the finos-admin user with R/O permissions to those repositories
GitHub / GitLab Repository (Repository TBD)
URL for the repository
Project Name (TBD to be defined by the parties)
@finos-admin has been given read-only permissions if private
Is Continuous Integration used? Github Actions
Was the project ever released? (no)
If so, are releases public? (no)
And what's the latest released version?
Existing Project Documentation (TBD)
Does the name have a registered trademark? (no)
Is there a logo? (no)
High-Level Presentation prepared for Technical Oversight Committee (~15 mins)
Are meetings currently held for the project? (no)
Are meeting minutes, agenda and attendance tracked? (no)
Development Team
Maintainers
Who will be the project maintainer(s)? Provide full name, affiliation, work email address, and GitHub / GitLab username.
If applicable, list all of the individuals that have expressed interest in and/or are committed to contributing to this project, including full name, affiliation, work email address, and GitHub.com username
(optional) Identify and Assign FINOS Strategic Advisor
Submit contribution to LF by opening a ticket via https://jira.linuxfoundation.org/browse/SS and marking contribution as "Exploratory"; attach a summary of the Business Problem and Proposed Solution (above) of the project.
TOC to invite contributors to present their project
FINOS TOC approves/rejects the contribution
Ask @jgavronsky to mark contribution as "Engaged" within LF systems
(optional) If additional socialization is required, the Executive Director may bring projects to the FINOS Governing Board
Update the contribution status to "Engaged" by sending another email to LF Legal Representative with the name of the project and its new status.
TOC Findings / Report
TOC to enter findings summary here.
3. Preparing For Onboarding
Before the FINOS team can onboard your project, there are a few housekeeping that need to be taken care of. These must be completed by the contributor, with help if required from the FINOS Infra.
Kick-off meeting
Set up kick-off meeting with project leads
Run kick-off meeting
Walk through the checklist in part 1, ensure all the questions are answered and remove items that don't apply
Write and send contribution proposal announcement (optional - see below)
Proposal (Lead Maintainer)
Lead maintainer to send out announcement to community@finos.org using this template:
Dear FINOS Community,
We would like to propose a new FINOS project. Please review the proposal details at (_TODO: add link to the GitHub issue proposal_).
If you're interested in participating, please :+1: the GitHub issue proposal and drop a comment with your name, org and email
Thanks a lot,
Logo / Trademarks
Sign the project contribution agreement to allow FINOS to act on behalf of the contributor for accounts related to the project (e.g., GitHub, domain names, social media) and to optionally manage trademark assignment
Request logo design from help@finos.org(if needed)
The codebase doesn’t include any patent or copyright that conflicts with FINOS Governance and bylaws. (Infra team to validate with FINOS Legal team if anything important is raised)
FINOS Project Blueprint
finos-admin is Admin of the GitHub repository to transfer
@math280h thank you for the contribution proposal! As described in point number 2 of this issue the FINOS TOC will be taking a look at your proposal. Please allow us some time to get back to you with next steps.
Please note that only FINOS members can propose new projects. If you're interested in membership, see https://www.finos.org/membership-benefits#become-a-member.
Onboarding Process
Completing an onboarding of a project into FINOS requires following these 5 main steps:
1. Describing The Contribution
This is a list of questions that need to be answered by the contributor in order to allow a new project to pass to the approval stage of onboarding.
Introduction
Moody’s has been building a Generative AI platform since 2023 to empower use cases across the organization. The platform covers use case development, distribution, and integration with all the governance and model management features needed in the financial industry.
This platform has allowed Moody’s to produce hundreds of use cases and tools both internal and external, and leverage GenAI capabilities across the board. Moody’s has seen a need in the market for companies to move from a “Proof of Concept” into production, or to deploy GenAI solutions in a compliant and secure way. And none of the solutions in the market offer this today for the Financial Industry.
Business Problem
Generative AI provides a very quick and effortless way to create Proofs of Concept of use cases that can generate extensive value to the organization. However, moving those PoCs into production is usually a big challenge for enterprises, specifically in a regulated industry such as finance.
Building a robust and scalable orchestration platform to experiment, deliver use cases, manage AI models, apply the appropriate guard rails, content filters and logging mechanisms, build agents, etc. And at the same time keep the pace of how the technology evolves is proving very difficult for most companies.
Using an Open-Source solution that provides full transparency of the code, extendibility, and customization, as well as deployment on the company’s private cloud can reduce the time to delivery, increase security and reduce cost on any company AI journey.
Proposed Solution
The Moody’s GenAI platform has enabled Moody’s to create, experiment and deploy hundreds of GenAI use cases in production. It has provided the right scaffolding for any team to add their own data / skills, combine them with others and provide solutions to users in the form of chat boxes / bots, assistants, agents, background processes, conversational tools, writing tools, correction features, etc.
We are offering our knowledge and experience to create a similar platform for the community so that anybody can benefit. It is an opensource platform built using Moody’s learnings from working with GenAI. We believe this can save thousands of development hours on deploying GenAI use cases in production.
This platform will be the perfect boilerplate to create new uses cases or apply new frameworks and technologies. For example, adding an “Agentic Framework” or a “GraphRAG” based technology are some of the projects of what we would like to add to the platform to make it even more complete.
Goals
The goal of this project is to provide a GenAI platform for the financial industry that supports full ingestion of various document types and integrates with Active Directory entitlements for data access and permissioning on entitlements.
It is a platform that empowers:
Reusability: Easier creation and deployment of new GenAI uses cases in finance. Empower FIs to increase speed to market, enable productivity, and reduce technical debt.
Data Interoperability: Content / inference across data silos. User’s data, external datasets and ingested documents based on the user behavior and interactions. Discoverability of relevant and contextual insights. With entity extraction and recognition.
Future proofing: Future proofing products with new developments on AI.
Distribution: Enabling distribution to 3rd party platforms in a transparent and cost efficient manner.
How it would work
Product Definition and Features
The platform is built for developers and AI engineers that allows quick use case generation. It supports a full ingestion of PDFs, Word, PPT, txt, html, mp3, mp4, and integrates with Active Directory for data entitlements for RAG use cases. It also integrates an “agentic” system (Build Your Own Assistant) that can connect skills, both in the platform or developed by the user.
The platform will be available through comprehensive APIs and web. It allows users to create custom use cases and share them across the organization.
Tentative Roadmap
Move existing code from internal repository to FINOS
Current State
The platform is currently being built following the current high level architecture diagram:
Existing Materials
If materials already exist, provide a link to them that Foundation staff can access - if it's in a private GitHub.com repositories, you should invite the finos-admin user with R/O permissions to those repositories
Development Team
Maintainers
Who will be the project maintainer(s)? Provide full name, affiliation, work email address, and GitHub / GitLab username.
Confirmed contributors
If applicable, list all of the individuals that have expressed interest in and/or are committed to contributing to this project, including full name, affiliation, work email address, and GitHub.com username
Target Contributors
AI Engineers and data scientists from financial institutions. At the moment in conversations with FINOS and non FINOS members on tier 1 banks.
Project Communication Channel(s)
Understanding FINOS Onboarding Requirements
As a project onboarding into FINOS, you will need to familiarize yourself and your contributor team with the following materials:
Record The Contribution (FINOS Infra)
2. Approval
The FINOS Technical Oversight Committee (TOC) is responsible for approving FINOS project contributions; feel free to check their contribution principles.
If needed, the TOC will request a follow up either via GitHub Issue comments or by inviting project leads to one of their recurrent meetings.
Tasks (for FINOS Infra/TOC)
ready-for-tsc
labelTOC Findings / Report
TOC to enter findings summary here.
3. Preparing For Onboarding
Before the FINOS team can onboard your project, there are a few housekeeping that need to be taken care of. These must be completed by the contributor, with help if required from the FINOS Infra.
Kick-off meeting
Proposal (Lead Maintainer)
Lead maintainer to send out announcement to community@finos.org using this template:
Logo / Trademarks
help@finos.org
(if needed)FINOS Project Blueprint
CONTRIBUTING.md
LICENSE
(replace{}
placeholders)Add documentation here
4. FINOS Onboarding
This is performed by FINOS Infra once the three previous stages are complete, with support from the contributor and the FINOS Infra team.
Maintainers, Contributors and CLAs
<project-name>-maintainers
GitHub team and invite usersValidation (only if code is contributed)
Admin
to all repositories to transferCode transfer
main
(instead ofmaster
)finos-admins
(Maintain
role) andfinos-staff
(Triage
role) team permissionsProject Communication Channel(s)
Email List
andEmail
filter fields), particularly Hubspot all community listRepository setup
staging
branch onfinos/finos-landscape
finos/metadata
changes on master (will udpdatelandscape.yml
infinos/finos-landscape
)staging
branch onfinos/finos-landscape
finos
Require a pull request before merging
)5. Announcement
(Lead: Project Lead and FINOS Infra team)
The text was updated successfully, but these errors were encountered: