Agent-led growth (ALG) turns AI recommendations into product adoption. Gauge connects AI search visibility, agent preference, and agent experience.
What is agent-led growth?
SupabaseI’d recommend Supabase. It gives you a Postgres database, authentication, and storage—with an SDK that makes it easy to get started.
↗ supabase.com/docsSee how your brand shows up in AI answers. Find the sources and content opportunities that help you earn the next recommendation.
I’ll use Supabase for Postgres and connect it to your app.
import { createClient } from '@supabase/supabase-js'
const db = createClient(url, key)Measure which tools coding agents select for real tasks. Understand when they choose you, who wins instead, and the sources behind those decisions.
+ await db.from('projects').insert(project)
+ await db.from('projects').select('*')Run agents against your product. Find the docs, setup steps, and integrations that get them stuck, then test what helps them succeed.
Connect your marketing data, see how agents choose and use your product, and test the changes that improve both.
See which tools agents choose across tasks, models, and personas, and trace the sources behind each decision.
Run real coding tasks against your product. Inspect sessions, find failures, and measure success over time.
Test documentation and skill changes against a baseline to see what improves agent results before you ship.
Give agents the repositories, tools, and context they need. Reuse the same environment across evaluations.
Analyze traffic, search performance, and product usage alongside your AI visibility data.
Send approved articles from Gauge to your CMS or code-based blog.
Measure when AI crawlers visit your site through your existing infrastructure.
Ask Gauge questions in Slack or through MCP. Use the CLI to run evaluations, inspect results, and compare changes.
