Leaping

What do they actually do

Leaping is building software that uses AI to find the root cause of software bugs and generate tested code fixes. The goal is to reduce the manual work engineers spend triaging, reproducing, and patching issues after releases, especially in production environments (YC profile, CB Insights).

Based on public descriptions, Leaping is early-stage and focused narrowly on automated diagnosis and code changes, not replacing monitoring or full observability stacks. In practice, it aims to sit alongside tools teams already use and shorten time-to-fix by proposing safe, testable patches for real defects (YC profile).

Who are their target customer(s)

• Mid-size SaaS engineering teams responsible for production services: They lose engineering time to triage and fix bugs after releases, which slows feature delivery and risks customer churn.
• On-call SRE/DevOps teams running 24/7 services: They face long mean-time-to-repair and incident fatigue because reproducing root causes under pressure is slow and manual.
• Frontend/web and mobile engineering teams: They spend outsized time on hard-to-reproduce client-side issues that hurt conversion and user experience.
• Small engineering teams / startups with limited headcount: Engineers get pulled into maintenance instead of building product, causing velocity and roadmap slips.
• QA / testing teams at larger organizations: Creating, triaging, and validating fixes is manual and error-prone; reducing this overhead would speed releases and improve quality.

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

• First 10: Run hands-on pilots with warm contacts (e.g., YC and existing networks), set up integrations, and personally help reproduce and fix a handful of live bugs in exchange for candid feedback and case studies.
• First 50: Use early case studies to power targeted outbound to similar teams and add one-click integrations with popular error/monitoring tools so trials can self-validate; support with technical webinars and engagement in engineering communities.
• First 100: Launch a paid self-serve tier and marketplace listings while hiring a small sales team for mid-market deals and bespoke onboarding; add partner channels with monitoring vendors and select consulting partners, using measured outcomes to shorten sales cycles.

What is the rough total addressable market

Top-down context:

Leaping touches adjacent budgets in observability and software testing. Observability is estimated in the tens of billions (e.g., Gartner/others cite ~$51–62B range for cloud observability mid‑2020s), while software testing is projected around $54B in 2026 and growing double digits (Sergeycyw citing Gartner/DA Davidson, Mordor Intelligence – Software Testing). Error monitoring specifically is a smaller subsegment, with some reports placing it in the low hundreds of millions today (Research Nester).

Bottom-up calculation:

Initial beachhead TAM: assume ~20,000 mid-market software teams (SaaS, SRE/DevOps, frontend/mobile) are viable buyers for automated bug resolution at an average $20,000 annual contract. That implies an initial serviceable TAM of roughly $400M, with upside as the product expands to larger enterprises and broader use cases.

Assumptions:

• Focus on mid-market teams actively investing in error tracking/observability and QA tooling (~20k teams).
• Average ACV of ~$20k for automated bug resolution that delivers measurable MTTR and engineering-time savings.
• Does not include long-term expansion into large enterprise or adjacent categories (e.g., observability, broader testing).

Who are some of their notable competitors

• Sentry: Error monitoring that captures exceptions, links issues to code and commits, and provides session replay/context to help engineers find where a bug happened. Widely adopted for client and server error tracking (Sentry).
• Rollbar: Real-time error tracking that groups similar errors, reduces alert noise, and routes issues via automation rules/workflows to speed response (Rollbar).
• Bugsnag: Stability/error monitoring that highlights which errors affect users most to help teams prioritize and triage effectively (Bugsnag).
• Rookout: Live debugging for production services using non-breaking breakpoints to inspect variables and state without redeploying—useful for root-cause investigation (Rookout docs).
• Datadog: A broad observability platform (metrics, logs, traces) with error tracking and a live debugger/trace tooling to find root causes across distributed systems (Datadog APM, Live Debugger docs).