Internal AI platform
Built and led an internal AI platform that gives teams across Adly's portfolio per-brand context, connected to the systems they already use.
Adly, company-wide, May 2026 to now. I built it and lead it. Updated .
Context
Adly runs a portfolio of internet businesses. Each brand's truth lives in different places: P&Ls, Asana, GA4, Search Console, GitHub, ad accounts and Slack. Generic AI tools know none of it.
The problem
Teams wanted to work AI-first, but an assistant without company context gives generic answers, and one with too much access is a risk. The company needed AI that knows each brand's numbers, respects who can see what, and works inside the tools people already use.
What I built
- Brand systems: P&Ls, GA4, Search Console, ads, GitHub, Asana, Slack
- Per-brand context and access rules
- Ask in Slack or the app
- An answer, a scheduled brief, or a draft pull request
An engineer reviews and ships the features teammates ask for.
The platform
I started it in May 2026 and built most of it myself: 423 of 482 Linear issues and 271 of 323 merged pull requests, backed by 7,576 automated tests. Every brand gets its own context and access.
Scheduled intelligence
Reports post to Slack on a schedule, led by a daily leadership brief for one of the brands.
Community-built features
Proven end to end in Jul 2026: a teammate asks for a feature, the platform opens a draft pull request, and an engineer approves it.
The habits around it
I started AI Office Hours (8 sessions, 30 to 35 invitees each, including leadership), co-lead the AI-first rollout, and administer the company's AI vendor stack.
Engineering automation
An automated PR review bot on every pull request, and an agent that turns production errors into deduplicated tickets on its own.
Results
| Measure | Value |
|---|---|
| Linear issues | 482 (423 mine) |
| Merged pull requests | 323 (271 mine) |
| Automated tests | 7,576 |
| AI Office Hours sessions | 8 |
| Invitees per session | 30 to 35 |
- May 2026 to Sep 2026.