Turnstile
Quality-gate the issues your AI agents file before humans see them
● The Problem
Agents now create 50% of the work items in Linear, up from 3% a year ago, and they are installed in 95% of paid workspaces. Agent-filed issues arrive without repro steps, duplicate each other, and describe one symptom three different ways. Engineering managers spend their mornings closing tickets a bot opened overnight.
● The Solution
A gate in front of the issue tracker. Every agent-created issue is deduplicated against open and recently closed work, scored for actionability, then merged, enriched with a repro pulled from the agent transcript, or held in a staging queue. Humans see one clean item instead of six near-identical ones.
Key Signals
MRR Potential
$20K-100K
Competition
Low
Build Time
1-3 Months
Search Trend
rising
Market Timing
Linear published data in August 2026 showing agents create half of all work items, a seventeenfold share increase in twelve months, and that issues with an attached pull request grew sevenfold since January. The intake volume arrived before anyone built the filter.
MVP Feature List
- 1Semantic dedup against open and recently closed issues
- 2Actionability score with a configurable hold threshold
- 3Repro extraction from agent transcripts and stack traces
- 4Staging queue with bulk merge and reject
- 5Per-agent quality scorecards
- 6Linear, Jira, and GitHub Issues integration
- 7Weekly digest of what the gate filtered and why
Suggested Tech Stack
Go-to-Market Strategy
Publish a free audit that connects to a Linear or Jira workspace and reports the duplicate rate and agent-authored share across the last 90 days. The number is the pitch. Sell to engineering managers who already track cycle time and can see intake noise inflating it.
Target Audience
Monetization
Per-SeatCompetitive Landscape
Linear and Jira added agent support but no intake control, since more issues reads as more usage. Unito and Zapier move issues between tools without judging them. Duplicate detection is standard inside support desks like Zendesk and has no equivalent on the engineering side.
Why Now?
Coding agents crossed from experiment to default during 2026 and more than half of their pull requests now merge. The bottleneck moved from generating work to deciding which generated work is real.
Tools & Resources to Get Started
Build It with AI
Open directly in an AI code generator or copy the prompt to start building Turnstile in minutes.
Replit Agent
Full-stack MVP app
Bolt.new
Next.js prototype
v0 by Vercel
Marketing landing page
Frequently Asked Questions
What problem does Turnstile solve?
Agents now create 50% of the work items in Linear, up from 3% a year ago, and they are installed in 95% of paid workspaces. Agent-filed issues arrive without repro steps, duplicate each other, and describe one symptom three different ways. Engineering managers spend their mornings closing tickets a bot opened overnight.
How much MRR can Turnstile generate?
Turnstile has $20K-100K MRR potential with a Per-Seat model. The estimated build time is 1-3 Months with Low competition in the market.
What are the MVP features for Turnstile?
Semantic dedup against open and recently closed issues. Actionability score with a configurable hold threshold. Repro extraction from agent transcripts and stack traces. Staging queue with bulk merge and reject. Per-agent quality scorecards. Linear, Jira, and GitHub Issues integration. Weekly digest of what the gate filtered and why.
What is the go-to-market strategy for Turnstile?
Publish a free audit that connects to a Linear or Jira workspace and reports the duplicate rate and agent-authored share across the last 90 days. The number is the pitch. Sell to engineering managers who already track cycle time and can see intake noise inflating it.
Who is the target audience for Turnstile?
The primary target audience includes Engineering Managers, Platform Teams, Product Operations Leads, Staff Engineers. Coding agents crossed from experiment to default during 2026 and more than half of their pull requests now merge. The bottleneck moved from generating work to deciding which generated work is real.
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