---
title: "GitLab's Strategies for Building Reliable Conversational Analytics"
url: "https://atlan.com/regovern-watch-center/gitlab-conversational-analytics-strategies/"
description: "Amie Bright of GitLab, explains how they're building Account IQ and Customer 360 by combining structured data, vectorized unstructured data, and a context layer."
keywords: "atlan, data governance, regovern 2025, regovern watch center"
---

> Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/

GitLab's Strategies for Building Reliable Conversational Analytics is a highlight clip from Re:Govern 2025, published in Atlan's Re:Govern Watch Center. Amie Bright of GitLab, explains how they're building Account IQ and Customer 360 by combining structured data, vectorized unstructured data, and a context layer.

Watch it at https://atlan.com/regovern-watch-center/gitlab-conversational-analytics-strategies/ (free; the page asks for an email after two minutes of viewing).

## Speakers

- **Amie Bright**, VP of Enterprise Data & Insights, GitLab. Amie is driving GitLab toward "AI-ready data," with governance at the center. She believes BI is on its way out and is testing AI chatbots while tackling hallucinations and missing context. Her mission: prepare GitLab's data for an AI-first future where traditional BI tools fade.

## Companies

- **GitLab**: GitLab is the world's largest all-remote public company. With distributed teams across the globe, GitLab is testing AI chatbots and building AI-ready data products where governance replaces centralized control with transparency and context.

## What's inside

GitLab's conversational AI needs to generate executive briefing docs and account plans on demand - requiring more than just structured data. Amie shares their three-part architecture: structured data from warehouses (the foundation they've had for years), vectorized unstructured data that marries seamlessly with structured sources, and context pulled from semantic layers, dbt docs, metadata, and business glossaries.

Key takeaways:

- Conversational AI for enterprise use cases requires three components working together, not just one data source
- Structured data from warehouses provides the foundation - the single source of truth companies have built for years
- Unstructured data must be vectorized to combine seamlessly with structured sources for comprehensive AI responses
- Context comes from multiple places: semantic layers (dbt docs), metadata repositories, and business glossary definitions
- Account IQ and Customer 360 use cases demand this full stack to generate executive briefings and account plans reliably

## Related

- [How GitLab is Moving From BI to AI with AI-ready Data Products](https://atlan.com/regovern-watch-center/how-gitlab-is-moving-from-bi-to-ai/)
- [GitLab's Learnings from its First Experiment with Conversational AI](https://atlan.com/regovern-watch-center/gitlab-conversational-ai-learnings/)
- [All Re:Govern 2025 sessions and highlights: the Re:Govern Watch Center](https://atlan.com/regovern-watch-center/)
- [Book a demo of Atlan](https://atlan.com/forms/talk-to-sales-contact/)