Executive Summary

Teams that write and operate selection predicates ("revsets") for CI, codeowners, release gates and automation routinely hit a wall when a rule matches unexpectedly: it’s opaque which sub-expression, change, or historical refactor caused the hit, and debugging can consume hours per incident across large orgs. This problem is most acute for release engineers, CI/automation owners, and platform teams in enterprises and monorepos where a single predicate can affect hundreds of pipelines and millions of files and where false positives cost compute, review time, and developer trust. The product would be an interactive visualization and provenance engine that traces predicate evaluation across commits and file changes, showing which subclauses matched, the diff context that triggered a match, and a compact natural-language explanation for each verdict; it would integrate with Git/GitHub/GitLab/Mercurial and CI systems and produce auditable logs for policy compliance. Key technical ingredients are a symbolic evaluator that runs efficiently over large change sets, a time-travel UI to step through the evaluation, and an LLM-assisted explanation layer that maps low-level logic to human-readable reasons while surfacing uncertainty and counterexamples. This is an attractive window: developer tools is an $18.0B market (25M professional developers × ~$720/year on tooling), AI-assisted explainability is now expected in developer workflows, and the rise of monorepos and heavy automation has increased demand for traceable selection logic. Enterprises increasingly require auditable, debuggable automation—making investable early customers available and willing to pay for reduced CI waste and faster incident resolution. To stand out you should combine rigorous, testable predicate semantics (so explanations are provably accurate) with pragmatic UX for non-experts and tight integrations into existing CI/VCS workflows; the defensible core is the symbolic provenance engine plus curated explanation templates rather than an LLM-only black box. Challenges are real: scaling to very large histories, handling refactor-induced matches, and ensuring explanations remain trustworthy—start by focusing on a few high-value enterprise customers with monorepos to validate accuracy and ROI before broadening.

Market Opportunity

Trace why a revset matches: visualize predicate evaluation across changes targets a $18.0B = 25M professional developers x $720/year avg spend on developer tooling (IDEs, CI, analysis, code-intel) total addressable market with medium saturation and a year-over-year growth rate of 12% (developer-tools & devops toolchain growth).

Key trends driving demand: AI-assisted developer tooling -- LLMs make explainability and natural-language reasoning about code feasible and expected in tools.; Monorepos & large-scale version control -- large codebases increase the need for precise, explainable selection/predicate tooling.; Observability for developer workflows -- teams want traceable, auditable reasoning behind automation and selection rules.; Shift to self-hosted enterprise tooling -- security concerns drive demand for on-prem or VPC-deployable developer tools that can use private telemetry..

Key competitors include Sourcegraph, GitHub Pull Requests + Code Search / GitHub Advanced Security, CodeScene (Empear), Internal scripts / ad-hoc tooling (workaround).

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Trace why a revset matches: visualize predicate evaluation across changes

Executive Summary

Market Validation

Market Opportunity

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