Synthetic Society

What do they actually do

Synthetic Society runs AI “synthetic users” that click through an app’s UI to exercise end‑to‑end journeys and surface bugs, broken flows, and UX friction before real users encounter them. The product records each step and hesitation, highlights drop‑offs via a “Friction Finder,” and provides real‑time analytics on failures and problem spots. They position this as a “confidence layer” over product changes with self‑updating end‑to‑end flows syntheticsociety.ai.

Today the product is invite‑only and founder‑led (team size 2) with a “Request Access” flow rather than open signup; onboarding appears to happen through demos and pilots rather than self‑serve. There is no public pricing, documentation, or case studies published yet syntheticsociety.ai Y Combinator.

The intended workflow is to connect a web or mobile app, let synthetic agents explore or follow seeded flows, then use the product’s friction reports and analytics to fix issues before release. Public materials highlight integration into the development loop but do not disclose technical details (e.g., SDK vs. browser automation, CI hooks) syntheticsociety.ai.

Who are their target customer(s)

• Product managers at mid‑size web or mobile apps: Frequent UX changes make manual checks slow and incomplete; they need a quick way to confirm critical flows (signup, onboarding, checkout) still work after each release.
• QA leads and test engineers at e‑commerce or marketplaces: Scripted tests are brittle and high‑maintenance; they want broader edge‑case coverage and fewer flaky failures that risk revenue during checkout or onboarding.
• Growth/experimentation teams running A/B tests: Experiments can introduce hidden friction; they need continuous, automated validation that variants don’t quietly break steps or reduce conversion.
• DevOps/release engineers owning CI/CD pipelines: End‑to‑end tests are often slow and flaky; they want faster, more reliable synthetic runs that catch regressions earlier so deployments aren’t blocked.
• Small engineering teams without dedicated QA: They can’t staff manual QA; they need a hands‑off way to detect broken flows before users hit them to avoid support load and bad first impressions.

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

• First 10: Founder‑led, concierge pilots via the invite/demo funnel and YC network; offer short, hands‑on pilots focused on high‑impact flows to generate concrete bug/regression wins to reference next syntheticsociety.ai Y Combinator.
• First 50: Turn early pilots into 2–4 one‑page case studies and short videos; run targeted LinkedIn/email outreach to similar PM/QA buyers with a fixed 30‑minute pilot offer and a repeatable onboarding playbook, plus referrals from pilot customers.
• First 100: Launch clear docs, pricing/trial, and automated onboarding for self‑serve; add light SDR follow‑up on inbound from content/ads/marketplaces and partner with CI/CD, QA vendors, and select consultancies to resell pilots.

What is the rough total addressable market

Top-down context:

The software testing market was about $55.8B in 2024, with application testing roughly 54% (~$30B) GMI Insights. A narrower adjacent segment, synthetic monitoring, was about $1.42B in 2024 IMARC Group.

Bottom-up calculation:

Conservatively, assume ~50,000 mid‑size+ web/mobile teams could adopt synthetic user testing at $20k–$50k per year, implying ~$1–$2.5B for the narrow product. If adoption expands toward hundreds of thousands of teams and $50k–$100k ACV for broader application‑testing coverage, the spend aligns with the ~$30B application‑testing market GMI Insights.

Assumptions:

• Counts refer to teams with budget authority for QA/tools, not total developers or all companies.
• ACVs reflect tool spend (not services) and include multi‑environment usage and team seats.
• Adoption starts in synthetic monitoring‑like use cases and expands into broader application testing over time.

Who are some of their notable competitors

• Datadog Synthetic Monitoring: Runs browser and API checks as part of a larger observability platform; widely adopted by SRE/DevOps teams for proactive journey and uptime testing.
• Checkly: Developer‑focused synthetic monitoring built on Playwright for browser flows and APIs; strong CI/CD integrations and code‑centric workflow.
• mabl: Low‑code, AI‑assisted test automation for web apps with auto‑healing and CI/CD integration aimed at reducing test maintenance.
• QA Wolf: Managed Playwright end‑to‑end testing; the vendor builds and maintains tests and reports failures, reducing in‑house maintenance burden.
• ProdPerfect: Generates and maintains automated tests from real user behavior data to focus coverage on the most critical and frequently used flows.