AI Integration

The GitLab AI Integration Working Group aims to define, coordinate and ramp up integration of AI capabilities into all product areas


Property Value
Date Created 2022-03-23
Target End Date TBD
Slack #wg_ai_integration - Slack channel for the working group and the high level alignment on getting AI ready for Production
Slack #ai_integration_dev_lobby - Channel for all implementation related topics and discussions of actual AI features
Slack #g_ai_framework - Channel for the AI Framework Team which is building the base for all features (experimentation API, Abstraction Layer, Embeddings, etc.)
Slack #ai_strategy - Discussion on strategic and business initiatives surrounding AI/ML at GitLab.
Slack #ai-experiments-infra - Infrastructure/Platform support for AI integration. See also &969.
AI Architecture Documentation Doc
Google Doc Working Group Agenda
Feature Tracking Sheet
YouTube playlist Playlist on GitLab Unfiltered
Parent Epic Parent epic
Epic/Issue Working Group label wg-ai-integration issue board and wg-ai-integration epic search
Epic label for prioritized prototypes wg-ai-integration-prioritized-prototype
Issue Board for AI Framework group Issue board link using label group::ai framework
Overview & Status See Exit Criteria below
Meeting schedule Monday, Tuesday, and Wednesday at 8am Pacific and Thursdays at 1pm Pacific


The GitLab AI Integration Working Group aims to define, coordinate and ramp up integration of AI capabilities into all product areas


Corresponding Epic

We want to enable all product teams to be able to use advanced AI capabilities for improving and adding functionality to the product so users can be faster and more productive in their DevSecOps lifecycle. For product teams we want to establish a clear and fast way for going from idea, experiment to production when using AI functionality. Incorporating the services, models and knowhow that the MLOps group has built over time and can provide to the wider team.

The working group will facilitate fast experimentation and prototyping of AI capabilities. We will also advise on what must be considered (and in some cases, get explicit approval on) before moving to production, including legal approval, ethical use of AI, potential necessary changes to terms of service, performance implications, hosting cost implications, infrastructure readiness, security readiness, licensing of 3rd party software/services, appropriate GitLab licensing levels for features, value add in helping users achieve their goals and needs, etc.

More information on the effort and plans can be found in the internal handbook.


This is a list of topics that we want to discuss:

  • Classification of AI possibilities and complexities
  • Ideation and understanding how experimentation can be done
  • Rapid ML Prototyping
    • API’s and Framework for experimentation
    • Feature Flagged Prototypes
    • Determine how teams without Product Design representation can progress with validation
  • Groundwork
    • Base API’s for all teams
    • Infrastructure
    • Documentation
    • Understanding of Jobs and user needs to be affected
  • Existing ML features
  • Road to production for features
    • Different gates

Exit Criteria

The following criteria should be met for the group to disband:

  • Product teams have a clear method to build and integrate AI into GitLab product areas.
  • The integration platform should have a product group handling maintanence and feature development
  • We have a structured methodology for evaluating new AI models, adding them to the integration platform to allow them to be consumed by product teams.
  • We have a roadmap plan to achieve GA for our initial AI experiments.
  • Documented process for handling AI feature proposals as part of the prioritization framework
  • Move SAFE content from the internal handbook to the public handbook where appropriate and SAFE.
  • Develop an evaluation process of user experience between options to make more intelligent decisions on which engineering solution we recommend.


Deliver X experimental, Y beta, and Z GA AI features

Roles and Responsibilities

Working Group Role Username Person Title
Executive Stakeholder @hbenson Hillary Benson Senior Director, Product Management - Sec, Data Science & Monitor
Executive Stakeholder @timzallmann Tim Zallmann Senior Director of Engineering, Dev
Facilitator @tmccaslin Taylor McCaslin Group Manager, Product - Data Science
Facilitator @wayne Wayne Haber Director of Engineering
Functional Lead - AI Model Validation @mray Monmayuri Ray Engineering Manager AI Model Validation
Functional Lead - UX @jmandell Justin Mandell Product Design Manager: Analytics, Govern, ModelOps, and Secure
Functional Lead - UX @pedroms Pedro Moreira da Silva Staff Product Designer
Functional Lead - Legal @m_taylor Matthew Taylor Sr. Director of Legal
Pricing representative TBH TBH Principal Pricing Manager, Product
Product representative @mushakov Melissa Ushakov Group Manager, Product - Plan
Product representative @sarahwaldner Sarah Waldner Group Manager, Product - Create
Product representative @abellucci Alana Bellucci Senior Product Manager, Govern:Threat Insights
Product representative @joshlambert Joshua Lambert Director of Product, Enablement
Product representative @tlinz Torsten Linz PM, Source Code
Development representative @johnhope John Hope SEM, Plan
Development representative @andr3 André Luís FEM: Source Code
Development representative @cdu1 Chun Du Director of Engineering, Enablement
Development representative @igor.drozdov Igor Drozdov Staff Backend Engineer, Source Code
Development representative @jeromezng Jerome Ng Director of Engineering, Fulfillment
Development representative @pcalder Phil Calder Senior Engineering Manager, Anti-abuse, Govern, and Growth
Development representative @nmccorrison Neil McCorrison Engineering Manager, Govern: Threat Insights
Development representative @carlad-gl Carla Drago Senior Backend Engineer, Manage: Import & Integrate
Development representative @donaldcook Donald Cook EM, Project Management
Legal representative @jbackerman Jesse Backerman Managing Legal Counsel
Vulnerability Research Representative @idawson Isaac Dawson Staff Vulnerability Researcher
Vulnerability Research Representative @dbolkensteyn Dinesh Bolkensteyn Sr. Vulnerability Researcher
Third Party Security Risk Representative @tdilbeck Ty Dilbeck Security Risk Manager
Governance and Field Security Representative @jlongo_gitlab Joseph Longo Governance and Field Security Manager
Security Compliance Representative @kbray Ken Bray Sr. Security Compliance Engineer (Dedicated Markets)
Security Compliance Representative @lcoleman Liz Coleman Security Compliance Manager (Commercial)
Security Automation Representative @agroleau Alexander Groleau Senior Security Engineering Manager (Automation)
Security Automation Representative @imand3r Ian Anderson Staff Security Engineer (Automation)
Application Security Representative @greg Greg Myers Security Engineer (Application Security)
Solutions Architecture Representative / Rapid Prototyping Team Member @bartzhang Bart Zhang Channel Solutions Architect
Product Marketing Representative @laurenaalves Laurena Alves Senior Product Marketing Manager
Developer Relations Representative @johncoghlan John Coghlan Senior Manager, Developer Evangelism
Privacy Representative @emccrann Eugene McCrann Lead Legal Counsel, Privacy
Quality Engineering Representative @at.ramya Ramya Authappan Engineering Manager, Quality, Dev & Analytics Section
Infrastructure @lmcandrew Liam McAndrew Engineering Manager, Scalability Frameworks
Infrastructure @igorwwwwwwwwwwwwwwwwwwww Igor Wiedler Staff SRE, Scalability Frameworks
Infrastructure @mbursi Michele Bursi Engineering Manager, Delivery System
Support @ralfaro Ronnie Alphero Support Engineering Manager
Enablement Christopher Wang Sr. Manager, Enablement (Sales Development)

Engineering Groups

We currently have two core AI Development groups at GitLab: AI Framework group and AI Assisted group.

AI Model Validation group

The AI Model Validation group helps all product groups to match the right model(s) and AI/ML-based techniques to the user problem they must solve. They do that by evaluating, building, training, and tuning many of the models GitLab uses as well as by proactively sharing AI resources and experience. Today, they also directly build and maintain some user-facing AI features.

  • AI Model Validation group develops in-house AI features that run natively within GitLab infrastructure. The group develops these models to meet capability, quality, customizability, privacy, and cost requirements. These components include an inference engine, abstraction layer, and models.
  • The custom-built models help in use cases when there is a need to train models on customer-proprietary data (like all merge requests and commits for a customer) and when 3rd party models do meet our needs.
  • Their currently released features are code suggestions which is currently in customer beta and suggested reviewer which is GA.
  • This group can help evaluate models for functional correctness and model perplexity based on metrics and (often large) benchmark datasets, which is a more statistical evaluation than manual testing. This can help determine the most quality model for a feature’s use-case.
  • They work in many languages, including Ruby on Rails, Golang, Python (for machine learning and data science), and Typescript (for the VS Code Plugin). The group comprises of ML Scientistists, MLOps Engineers , ML Infrastructure Engineers, and Fullstack engineers.

You can contact this group via Slack in #g_ai_assisted. View their issue board here. To see who is engaged on this effort please see here.

AI Framework

AI Framework exposes AI services and the underlying models (third party or native GitLab models) to all product groups.

  • The AI Framework group enables the rest of the development department to build AI features through the abstraction layer.
  • The abstraction layer supports OpenAI and is being extended to support equivalent Google AI functionality. Other commercial, open-source, and GitLab custom-built models are also being considered.
  • This group empowers other groups to evaluate models via manual human testing, through the Experimentation API.
  • This group works with Ruby on Rails as they make it easy for the GitLab product to add AI functionality through the gitlab/gitlab-org repo.

You can contact this group via Slack in #g_ai_framework. View their issue board here.

AI Engineering Allocation

Because of the dynamic nature of the AI work and folks to be engaged, we are putting the AI work under an Engineering Allocation. This means that assignments may change rapidly as focus and priorities shift. Current focus is on Code Suggestions adoption.

Name Role Area of Work
Alexandru Croitor Senior Backend Engineer AI Enablement
Eulyeon Ko Backend Engineer AI Enablement
Nicolas Dular Senior Backend Engineer AI Enablement
Denys Mishunov Staff Frontend Engineer AI Enablement
Jan Provaznik Staff Backend Engineer AI Enablement
Mikołaj Wawrzyniak Staff Backend Engineer AI Enablement
Pavel Shutsin Senior Backend Engineer AI Enablement
Max Woolf Staff Backend Engineer AI Enablement
Tan Le Senior Fullstack Engineer AI Enablement
Andras Herczeg Backend Engineer AI Enablement
Sebastian Rehm Engineering Manager AI Enablement
Daniel Tian Senior Frontend Engineer Threat Insights
Gregory Havenga Backend Engineer Threat Insights
Kerri Miller Staff Backend Engineer Code Review
Stanislav Lashmanov Senior Frontend Engineer Code Review
Simon Knox Senior Frontend Engineer Plan:Project Management
Nikola Milojevic Senior Backend Engineer Application Performance
Aleksei Lipniagov Senior Backend Engineer Application Performance
Matthias Käppler Staff Backend Engineer Application Performance
Roy Zwambag Backend Engineer Application Performance
Paul Phillips Engineering Manager Application Performance
Igor Drozdov Staff Backend Engineer Source Code
Patrick Cyiza Backend Engineer Source Code
Natalia Radina Frontend Engineer Source Code
Alper Akgun Staff Fullstack Engineer VS Code Extension
Tomas Vik Staff Fullstack Engineer VS Code Extension
Enrique Staff Frontend Engineer VS Code Extension
André Luís Engineering Manager Editor Extensions
Mike Eddington Staff Backend Engineer Editor Extensions (Visual Studio)
Ross Fuhrman Senior Backend Engineer Editor Extensions (Visual Studio)
Gabriel Mazetto Senior Backend Engineer Editor Extensions (JetBrains)
Naman Gala Senior Backend Engineer Editor Extensions (JetBrains)
Marshall Cottrell Principal Editor Extensions (Code Suggestions/VS Code)
Illya Klymov Senior Frontend Engineer Editor Extensions (Code Suggestions/VS Code)
Lena Horal-Koretska Senior Frontend Engineer Editor Extensions (Language Server)
Erran Carey Staff Incubation Engineer Editor Extensions (Neovim)
Ash McKenzie Staff Backend Engineer Editor Extensions (Neovim)
Jay Swain Engineering Manager Model Evaluation
Andrei Zubov Senior Frontend Engineer Deploy:Environments, Model Evaluation
Allison Browne Senior Backend Engineer Model Evaluation, Verify:Pipeline Execution
Dylan Bernardi Associate Backend Engineer Model Evaluation
Stephan Rayner Senior Backend Engineer Model Evaluation
Igor Wiedler Staff Site Reliability Engineer Model Evaluation
Alejandro Pineda Staff Site Reliability Engineer Model Evaluation
Last modified September 1, 2023: Mark all active working groups (e749da39)