Today, code is cheap, but understanding is expensive. Ninchi is a lightweight, gamified accountability layer that integrates into your Git workflow to ensure you and your team can explain, own, and maintain every line of AI-generated code.
Free to start · No credit card required
Keep your skills sharp. Prevent yourself from becoming cognitively dependent on AI. Prove to yourself and your clients that you are the expert in the driver's seat.
Eliminate 'rubber-stamp' PR merges. Protect your codebase from long-term maintainability collapse and CI churn without slowing down velocity.
Install Ninchi through GitHub Marketplace, or procure Enterprise via AWS Marketplace.
GitLab
Vanta
SOC 2 Type 1Ninchi ships official GitHub and GitLab integrations, live today. We partner with Vanta and Drata through their official partner programs, and Ninchi has completed a SOC 2 Type 1 attestation.
Teams and organizations using Ninchi today.
Organizations are increasingly shipping AI-assisted code, AI-generated reports, and AI-assisted workflows — without clear verification of who reviewed what, who understood what, or who approved what.
“When something goes wrong, companies need to know who knew what, and when.”
Ninchi Creates Verifiable Human Accountability
Connect a repository once. Ninchi turns each change into a focused challenge, records the result, and rolls the evidence into team views.
Install Ninchi for selected repositories through the GitHub App. GitLab is supported too; this clip shows the GitHub flow.
When a challenge is triggered, Ninchi reads the changed code and repository context to generate an artifact-specific question and hidden rubric.
A pull-request comment opens one focused browser challenge, designed to take less than 60 seconds. Ninchi records the timestamped result as evidence in personal and organization views.
Recorded challenge events roll into organization analytics, knowledge maps, and accountability views. These are deterministic projections of stored evidence — not predictions of ability.
Ninchi combines repository facts with recorded challenge evidence to show which areas matter most and where your team has evidence on record. Every view comes from stored events and deterministic rollups; none predicts ability.
Available on the Enterprise plan.
Ninchi scans a repository and organises it into key areas ranked by size and recent activity. It also shows the repository's language mix and refreshes as the codebase changes.
Each key area shows how many contributors have qualifying evidence: passing challenge answers tied to that part of the repository. Ninchi weights each area's coverage classification by importance, so higher-importance areas contribute more to the overall figure. Areas with one qualifying contributor are labelled single-contributor.
Understanding Debt is the share of key-area importance with no verified-understanding coverage. Ninchi calculates it from recorded challenge evidence.
Org admins can inspect one developer's recorded evidence by repository area. The view separates evidence inferred from PR challenge history from evidence gathered through targeted assessments. It reports coverage and does not rank people.
Add lightweight verification without replacing the review process your team already uses.
Tie each recorded check to the person, artifact, and point-in-time decision it covers.
Detailed records of human understanding and approval at every step.
Review aggregate evidence patterns across teams, repositories, and knowledge-map areas.
Every verification records the question, answer, score, difficulty, artifact context, and timestamp.
“Keep AI-assisted delivery fast and human ownership visible.”
Available now is supported today. In development is active work not yet available. Exploring is directional and not committed.
GitHub and GitLab integrations create challenges from code changes inside the review cycle.
Create a challenge from submitted code or content without connecting a repository.
Canvas and Moodle integrations for assignments and education cohorts.
Potential challenge-and-evidence flows for AI-assisted drafts, memos, and reports.
The Ninchi Score is a transparent, difficulty-weighted evidence ratio over verified-understanding events. It records what a developer or team has demonstrated in Ninchi challenges, with artifact context and timestamps, so the conversation stays grounded in inspectable evidence.
“Demonstrated understanding and accountability, made visible as evidence you can inspect.”
Unlimited members on every plan. Start with the core challenge loop, then add team analytics, stricter verification, and enterprise controls.
Swipe to compare all four plans.
| Compare plans | Enterprise Custom tailored to your organization Organization-wide evidence, exports, knowledge maps, and support. Contact Us | |||
|---|---|---|---|---|
| Core workflow | ||||
| Scored PRs | 5 / month (soft cap) | Unlimited | Unlimited | Unlimited |
| Members | Unlimited | Unlimited | Unlimited | Unlimited |
| Verification modes | Casual | Casual, Tracking, Blocking | Casual, Tracking, Blocking, Strict | Casual, Tracking, Blocking, Strict |
| Question difficulty | Easy | Easy, Medium | Easy, Medium, Hard | Easy, Medium, Hard |
| Diff preview in challenges | — | |||
| Organization analytics | — | |||
| Advanced verification | ||||
| Anti-cheat controls | — | — | ||
| Teach Me lessons | — | — | Available when enabled by an org admin | Available when enabled by an org admin |
| Governance and audit trails | — | — | ||
| Enterprise capabilities | ||||
| Audit log export | — | — | — | |
| Knowledge maps and baselines | — | — | — | |
| Dedicated support and SLA | — | — | — | |
| Custom branding | — | — | — | |
| AI spend analytics | — | — | — | |
Standard and Pro prices are per seat, billed monthly. The Hobbyist scored-PR limit is a soft cap: challenges continue, but additional PRs are not scored that month.
Eden is testing Ninchi inside a real engineering workflow. The case study focuses on whether lightweight verification can preserve developer engagement without disrupting delivery.
Public findings will be limited to results Eden and Ninchi have reviewed together, with measured outcomes added only when the data is ready.
Read the research behind Ninchi's challenge loop, inspectable evidence model, and transparent difficulty-weighted score.
The paper clearly separates shipped capabilities from proposed models and validation targets.
Straight answers about workflow impact, privacy, evidence, and the larger Ninchi vision.
Ninchi is built for software teams today. Ninchi Direct supports the same challenge-and-evidence workflow for other kinds of submitted work.
Ninchi was founded around a simple conviction: AI can accelerate production without turning people into passive reviewers. We build lightweight verification and inspectable evidence for teams that want speed and human ownership together.
Founder, CEO & CTO
CPO & CMO
Head of Research
Book a working session or share your source platforms, team size, and security requirements. We will route the right pilot and onboarding path.
Book Team DemoAffiliate, design-partner, or investor inquiry? support@ninchi.ai
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