Janet AI

What do they actually do

Janet AI is a web-based layer that sits on top of Jira and other company data sources to automatically triage and organize tickets. It monitors new issues, finds related or duplicate tickets, and applies structure like type and relationships; the team describes this as an always-on auto-triage agent that builds a knowledge graph of tickets and their links tryjanet.ai (YC profile).

It enriches tickets with context pulled from tools like Slack, Google Drive, Google Calendar, and Discord, so issues carry relevant notes and links without manual copy-paste. Teams use a dashboard (app.tryjanet.ai) plus chat and report pages to ask questions about scope and impact (e.g., which customers or versions are affected) and to search structured insights across the ticket graph (docs, homepage).

Today they target mid-to-large engineering and incident-management teams. Public materials say they process thousands of Jira tickets and offer demos and free proofs of concept on customer data; Janet is a YC Summer 2025 company with a small founding team (YC launch, YC profile, LinkedIn).

Who are their target customer(s)

• Engineering triage lead at a mid-to-large product team: Spends hours grouping, deduplicating, and labeling incoming tickets. Lacks enough context to see duplicates, revenue blockers, or priority order quickly.
• Product manager responsible for roadmap and customer impact: Cannot reliably tell which bugs hurt key customers or which releases need fixes first. Pulling customer, incident, and ticket context together is slow and incomplete.
• Site reliability / incident response engineer: During incidents, wastes time stitching together alerts, Jira tickets, chat logs, and notes to understand scope and root cause. This slows MTTR and makes follow-ups noisy.
• Customer support / success manager who escalates issues: Has to manually collect logs, customer details, and prior tickets before handing off to engineering, causing long handoffs and frustrated customers. Lacks clear reports tying tickets to impacted customers.
• Enterprise IT or platform lead improving developer productivity: Struggles to measure and reduce rework because ticket systems are noisy and disconnected from other company data. Needs a single, searchable view linking tickets to meetings, docs, and chats.

How would they acquire their first 10, 50, and 100 customers

• First 10: Direct, POC-driven outreach via YC intros, LinkedIn, and targeted emails to triage leads and SREs; run a free POC on their Jira data and deliver a one-page ROI summary to create an internal champion.
• First 50: Publish short case studies and POC results, list in Jira/Atlassian channels and partner with consultancies, run targeted demos/webinars, and offer a guided self-serve trial to convert small teams.
• First 100: Add sales and customer success for multi-week enterprise pilots with security/procurement, ship enterprise controls (SSO, audit logs, retention), and use a repeatable POC→paid playbook plus referrals and partnerships.

What is the rough total addressable market

Top-down context:

Hundreds of thousands of organizations use ticketing systems; Atlassian alone serves 300k+ customers Atlassian. The broader IT service-management market is measured around $9B (2022) with steady growth SDI/Grand View Research.

Bottom-up calculation:

Focus on mid-to-large engineering orgs (subset of ~377k companies with 250+ employees) Statista. If 50,000 targetable orgs exist and average ACV is ~$40k for an intelligence add-on, implied TAM is about $2B, with upside in high-ACV enterprise deals also using ServiceNow/Jira ServiceNow context.

Assumptions:

• Targetable mid-to-large orgs ≈ 50,000 globally from the broader 250+ employee pool.
• Average ACV for ticket-intelligence layer ≈ $25k–$100k; modeled at ~$40k for TAM calc.
• Adoption focuses on engineering/incident-heavy teams rather than the entire Atlassian base.

Who are some of their notable competitors

• Atlassian (Jira / Atlassian Intelligence / Rovo): Jira’s native AI can summarize issues, suggest priorities/request types, and provide a triage assistant within Jira/JSM—reducing the need for an external layer for some teams (AI in Jira issues, Rovo triage assistant).
• ServiceNow: Enterprise ITSM platform with incident management, AI agents, automated triage/routing, and strong governance/scale features—an alternative for organizations wanting intelligence plus controls in one stack (Incident Management, AI agents for ITSM).
• BigPanda: Ops/incident management focused on correlating alerts, surfacing similar incidents, and automating triage and root-cause analysis—overlaps with Janet’s incident-triage use case (product overview).
• incident.io: Slack-centric incident management with “AI SRE” features that triage alerts, investigate root causes, and draft next steps—competes in on-call/SRE workflows (AI SRE).
• DevRev: AI-first product and support platform linking customer signals and support cases to engineering work and business outcomes—overlaps with Janet’s longer-term goal of tying tickets to impact (Jira alternatives).