Decipher AI

What do they actually do

Decipher AI runs QA agents for web apps that learn how your product actually works by watching real user sessions or recordings, then generate end‑to‑end tests from those flows or from short natural‑language descriptions. The agents keep tests working as the UI changes by updating selectors and steps automatically, reducing the need to rewrite brittle tests by hand getdecipher.com docs.getdecipher.com.

Teams can run the generated tests in CI/CD or inside Decipher. In production, Decipher monitors real sessions to detect failures, quantify impact, attach reproducible context (including session links), and open prefilled issues in tools like GitHub or Linear; it can also pass structured context to coding agents (e.g., Claude Code, Cursor) to help draft fixes or PRs. The product plugs into common developer workflows (CI/CD, GitHub, issue trackers, Sentry) and offers enterprise controls such as PII masking and SSO; they also list a public “Pro” plan with monthly pricing and run/session limits getdecipher.com docs.getdecipher.com getdecipher.com/pricing Y Combinator.

Who are their target customer(s)

• Fast‑shipping product engineering teams (startups and feature teams): They push changes quickly and often break user flows, but don’t want to slow down to write or continually fix fragile E2E tests. Decipher auto‑generates and self‑heals tests to reduce that drag getdecipher.com Y Combinator.
• Small engineering teams without dedicated QA: They lack headcount for manual testing or test engineers, so regressions slip to production or releases get delayed due to hand testing. Decipher generates runnable tests from recordings or short descriptions docs.getdecipher.com.
• QA leads at mid‑sized companies: Maintaining flaky, selector‑dependent E2E suites consumes time and hides real issues in noise. Decipher updates selectors/steps when the UI changes to reduce maintenance burden getdecipher.com.
• Product/Support/Incident owners who triage customer bugs: Reproducing production failures, measuring user impact, and prioritizing fixes is slow when context is scattered. Decipher monitors sessions, quantifies impact, and attaches repro links to issues getdecipher.com.
• Security/compliance and enterprise IT teams: They need controls for sensitive data, access, and retention when a tool records sessions and touches CI/issue trackers. Decipher offers PII masking, SAML/SSO, and enterprise controls getdecipher.com.

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

• First 10: Run hands‑on pilots with YC founders, case‑study references, and warm intros; provide concierge onboarding that turns session replays into runnable tests and fixes early integration pain getdecipher.com Y Combinator.
• First 50: Lean into product‑led demos, publish short case studies and step‑by‑step docs, host live demos in startup communities/engineering Slack groups, and ship frictionless CI/GitHub/issue‑tracker integrations to show value quickly docs.getdecipher.com getdecipher.com.
• First 100: Add a targeted mid‑market motion (SDR/AE) offering paid pilots bundled with enterprise controls (SSO, PII masking, retention) to ease procurement; form channel partnerships with observability and coding‑agent vendors getdecipher.com.

What is the rough total addressable market

Top-down context:

Decipher sits across automated testing, AI test automation, and session replay. Reports place automation testing in the tens of billions today Grand View Research Future Market Insights, AI test automation at ~$8.8B in 2025 with rapid growth MarketsandMarkets, and session replay around low single‑digit billions Dataintelo; broader observability is also tens of billions Research Nester Elastic.

Bottom-up calculation:

Assume ~100k mid‑market/enterprise web product teams worldwide and ~150k SMB teams that actively maintain E2E tests or buy session replay; if 25% adopt an AI‑driven QA platform with a blended ACV of $20k (SMB)–$70k (mid‑market/enterprise), that yields roughly 62.5k customers and ~$3–5B in near‑term SAM, with penetration toward 50% and higher enterprise ACVs pushing the serviceable TAM toward ~$8–12B over the next several years.

Assumptions:

• Population: ~250k web product teams globally across SMB to enterprise that use CI/CD or session replay.
• Adoption: 25% near‑term rising toward 50% as AI test automation and production monitoring converge.
• Blended ACV: ~$20k (SMB) to ~$70k+ (mid‑market/enterprise), combining testing and replay budgets.

Who are some of their notable competitors

• mabl: Low‑code, intelligent test automation platform with auto‑healing and CI/CD integrations; widely used by teams seeking to reduce test maintenance.
• Tricentis Testim: AI‑assisted test authoring and self‑healing within the Tricentis suite, targeted at larger organizations standardizing on end‑to‑end testing.
• Functionize: ML‑based test automation that emphasizes self‑healing and cross‑browser coverage for web apps.
• testRigor: Plain‑English test creation with autonomous maintenance aimed at reducing flaky selector‑based tests.
• FullStory: Session replay and product analytics to reproduce user issues and quantify impact; while not a test tool, it competes for the session‑replay/monitoring budget Decipher targets.