Jazzberry

What do they actually do

Jazzberry is a GitHub App that runs an AI-driven tester on every pull request. When a PR opens, it clones the repo into a short‑lived, isolated sandbox (microVM), where an agent can run shell commands, install dependencies, execute tests, and try targeted checks to make the code fail. If it finds issues, it posts a markdown report back to the PR with the failure and concrete inputs to reproduce it (GitHub App, site/how it works, docs, about).

The system is language‑agnostic because it executes the repository in a real runtime rather than only doing static analysis. Each run is ephemeral and isolated, with data encrypted in transit and at rest. The product is sold as a per‑developer SaaS with a 7‑day free trial and a published Pro price of $20/month per developer (docs, about, prices, GitHub App).

Who are their target customer(s)

• Early-stage startup engineering teams shipping fast with small squads: They worry runtime bugs slip into production and lack QA capacity to catch them. They want failing cases and reproduction steps surfaced directly in PRs to avoid manual debugging.
• Mid-size SaaS product teams with frequent deploys: They deal with flaky behavior and logical errors that unit tests and static analyzers miss. They need quick, actionable PR feedback with reproducible inputs to speed triage.
• Security/DevSecOps teams preventing runtime vulnerabilities: Static scanners can miss exploitable runtime issues. They need evidence that checks ran in an isolated, auditable environment with minimal data exposure.
• Platform/CI engineers maintaining build pipelines and tooling: They need tools that scale across repos, integrate cleanly with PR/CI workflows, and provide controls for sandboxing and access to satisfy org policies.
• QA and reliability engineers focused on reproducing and closing bugs: They lose time recreating intermittent failures and need concrete inputs or tests they can run locally to reduce back‑and‑forth with developers.

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

• First 10: Run concierge pilots via YC and personal networks: help teams install the GitHub App, run on a few PRs, and document real bug reports as case studies to validate value quickly (YC, GitHub App).
• First 50: Make onboarding fully self‑serve through the GitHub App and docs; drive installs via developer channels (HN/Reddit, newsletters, blog posts with reproducible bugs) while converting trials with short, hands‑on support (docs, blog).
• First 100: Target platform and security teams with paid, time‑boxed pilots emphasizing sandboxing/privacy guarantees; expand reach via CI/tooling integrations and publish technical how‑tos and security reports to prove safety and ROI (about/security, docs).

What is the rough total addressable market

Top-down context:

List price is $20 per developer per month ($240/year). Applied to global developer populations (20.8M–47.2M depending on source), the theoretical TAM is on the order of $5B–$11B/year at full penetration (prices, JetBrains, Evans, SlashData).

Bottom-up calculation:

Using $240 per developer per year and focusing on 5%–20% of professional developers as likely buyers yields a near‑term SAM of roughly $250M–$1.3B ARR (e.g., 20.8M×5%×$240 to 27M×20%×$240) (JetBrains, Evans, prices).

Assumptions:

• Paying users are a subset (5%–20%) of professional developers using PR/CI workflows and Git hosting compatible with a GitHub App.
• Revenue scales linearly with paid developer seats at $240/year each; no enterprise uplift is included.
• Security/compliance needs do not materially restrict adoption beyond assumed percentages.

Who are some of their notable competitors

• Snyk: PR/CI security scanner focused on vulnerable libraries and code patterns via static analysis. Overlaps on pre‑merge scanning for security‑minded teams but does not execute repos in a sandbox to reproduce runtime failures.
• GitHub CodeQL: Queryable static analysis engine used in GitHub Actions to detect security issues. Competes for automated PR scanning but analyzes source with queries rather than running code in an isolated VM.
• Diffblue Cover: AI tool that generates unit tests (primarily for Java) to boost coverage and catch regressions. Focuses on test generation, while Jazzberry executes repos to discover failing runtime behavior and returns reproduction inputs in PRs.
• Testim: AI test automation for end‑to‑end and regression testing integrated with CI. Competes for QA budgets but centers on UI/functional flows, not exploratory runtime bug hunting inside a repo sandbox.
• EvoSuite: Automated test‑generation research tool that executes code to produce unit tests. Often language‑specific and less focused on PR/CI and enterprise controls compared to an LLM‑driven, PR‑commenting agent approach.