Turn any change you'd make by hand into a playbook that runs across every repo.
Show Kaydence a few example pull requests. It mines the pattern, extracts the repeatable rules, and verifies them on sample repos. Kaydence then runs the change across your GitHub organization. Every change is a pull request, validated against your own CI, reviewed by a human before it merges. No auto-merge, ever.
Book a 20-minute demo See how it works ↓Free trial available: 5 pull requests and 3 playbook builds, no card required.
Playbooks aren't written. They're mined.
Every other tool ships you a fixed menu. Dependabot bumps dependencies. OpenRewrite recipes are hand-authored by people who have never seen your codebase. But most of your tech debt isn't a generic migration. It's your naming convention, your logging pattern, your way of handling errors, repeated across four hundred repositories. Kaydence learns those from your own pull requests.
Give it examples
Point Kaydence at a few pull requests where you already made the change by hand. Or just describe the task in plain English.
It mines the pattern
Kaydence finds similar pull requests across public repositories, compares them against yours, and extracts the rules that actually repeat, separating the mechanical change from the one-off judgement calls.
It verifies before it touches your code
The playbook runs against sample repositories first. You see what it produces before you ever point it at your estate.
Every workflow in the catalogue below was built this way: fifteen playbooks, each one producing a reviewable pull request.
Tech debt is not a "later" problem.
Engineers spend between 25% and 42% of their time on activities caused by technical debt: rework, workarounds, slow builds, debugging legacy code, and managing fragile dependencies. For a 50-person engineering team, that's over $2.5 million in annual payroll consumed by debt before you account for incident costs.
Security debt compounds it. The average data breach in 2024 cost $4.88 million. GDPR fines reach €20 million or 4% of global turnover. HIPAA violations carry penalties up to $1.5 million per category, per year. Companies subject to SOC 2, ISO 27001, HIPAA, or GDPR face mandated patching windows that don't move.
Yet the remediation work, language migrations, dependency upgrades, code standardization, is manual, repetitive, and routinely deferred in favor of feature work. Debt compounds. Compliance risk accumulates. Velocity suffers.
Connect. Scan. Run. Review.
Kaydence is built around a single magic moment, the gap between authenticating GitHub and seeing exactly which repos in your estate would benefit from which automated workflows, with engineering time savings quantified per workflow.
Connect
Authenticate GitHub. Choose repo-level or org-level access. Done in two clicks.
Scan
Kaydence scans your repositories, parsing build files, dependencies, and test imports and identifies which repos are eligible for which workflows. Typically completes in under 5 minutes for 50 repositories.
See your opportunities
The Workflow Opportunities dashboard shows: this many repos eligible for Java 17 migration, this many with vulnerable dependencies, this many with Enzyme test suites each paired with an estimated PR volume and engineering hours saved.
Run a campaign
Pick a workflow. Pick the repos. Dry-run to preview the changes. Click execute. Kaydence opens pull requests with descriptive context, risk levels, and rationale.
Review and merge
Every PR is human-reviewed, no auto-merge, ever. LLM-assisted transforms validate against your own CI before opening as ready-for-review. Closed PRs feed back into the system to make future runs smarter.
Nine proven workflows out of the box. Plus whatever you build.
These ship pre-built and tested. They cover the highest-volume debt across Java, JavaScript/TypeScript, and Python: the mechanical, repetitive work that never wins a sprint planning argument. Everything else, you build.
Security Patching
CVE dependency patching
Vulnerability scanning across npm, pip, Maven, and Gradle. Auto-generated pull requests to bump vulnerable packages to safe versions, with CVSS thresholds and grouped patching.
Log4j → SLF4J
Migrate off Log4j to the SLF4J facade. Removes the dependency at the root of the most expensive vulnerability class of the last decade.
Language & Framework Migrations
Java 8/11 → Java 17/21
API removals, type inference changes, build file updates.
Spring Boot 2 → 3
Jakarta EE namespace migration, Java 17 baseline, config property updates.
javax → jakarta
The namespace migration underneath Spring Boot 3. Run it standalone, or as the step before.
JUnit 4 → JUnit 5
Annotation, import, and assertion updates across test classes.
AWS SDK for Java v1 → v2
v1 reached end of support in December 2025. Client construction, async patterns, and dependency updates.
Enzyme → React Testing Library
Behaviour-based test rewrites for React component suites. Enzyme has no React 18 adapter.
Code Standardization
Common static analysis fixes
A bundle of 50+ low-risk fixes: null checks, unused imports, empty blocks, redundant casts, all sourced from SonarQube and Checkstyle-style scans.
Custom Rules
Define your own find/replace or regex-based transformations for naming conventions, import patterns, or any team-specific standard.
Also proven and available on request: Guava → JDK equivalents, Joda-Time → java.time, Moment.js → date-fns, Python 2 → 3, Python f-strings, and JavaScript var → const/let with ESM imports.
Built for engineering leads, not just engineers.
Existing tools handle slices of the problem. Dependabot bumps dependencies. Moderne and OpenRewrite do precise Java refactors from hand-authored recipes. None of them let you point at three pull requests and get a playbook, and none give you a campaign layer, a debt dashboard, or a self-serve way in.
| Dependabot | Moderne / OpenRewrite | Kaydence | |
|---|---|---|---|
| Build your own playbook from example PRs | — | hand-authored recipes | ✓ |
| Language migrations | — | ✓ Java-first | ✓ Java, JavaScript/TypeScript, Python |
| Security patching | ✓ deps only | partial | ✓ with campaign management |
| Code standardization | — | ✓ | ✓ static analysis + custom rules |
| Magic-moment scan + opportunity dashboard | — | — | ✓ |
| Engineering lead dashboard | — | — | ✓ |
| Campaign management | — | — | ✓ |
| Transformation engine | N/A | AST | Hybrid: AST + LLM |
Start with one playbook.
The trial gives you three playbook builds and five pull requests, enough to take one real, annoying, repetitive change your team keeps deferring, and watch it run. If you would rather see it on your own estate with someone walking you through it, book twenty minutes.
Book a demo Or email us