Anonymize Chapter 11 reorganization plans for restructuring research – CCPA/HIPAA-compliant de-identification per 11 USC §1129

A Chapter 11 plan of reorganization confirmed under 11 USC §1129 binds creditors and equity holders to the debtor's restructuring terms. The plan names creditor classes, sets treatment schedules, and identifies post-confirmation management. anonym.legal pseudonymizes entity and individual names so confirmed plans can support restructuring-law teaching and precedent databases without identifying the reorganized debtor.

When this applies

Apply this workflow when a confirmed Chapter 11 plan must be shared with restructuring-practice trainees, deal teams working on analogous transactions, or law reviews publishing case studies where debtor identity is not central to the analysis.

  1. Upload the confirmed plan of reorganization in PDF or DOCX format.
  2. The engine identifies the debtor entity name, creditor class representatives, new equity investors, and management names throughout the plan.
  3. Each named individual and entity receives a consistent pseudonym applied across all plan articles and exhibits.
  4. Class treatment schedules, recovery percentages, and distribution mechanics are preserved as non-personal structural content.
  5. The encrypted mapping is stored for authorized re-identification.
  6. The pseudonymized plan is exported for use in restructuring-practice training libraries or academic publications.
  7. Batch processing supports a portfolio of plans from a single industry for sector-level restructuring analysis.

What you provide

  • Confirmed plan of reorganization in PDF or DOCX, including all plan exhibits
  • Any plan supplements filed before confirmation
  • Instruction on whether new equity investor names should be pseudonymized

Limitations & cautions

  • anonym.legal does not assess whether a plan satisfies the confirmation standards under 11 USC §1129; legal advice is required.
  • Plans referencing publicly registered securities may contain information that allows indirect debtor identification despite name pseudonymization.
  • The tool does not pseudonymize embedded signature images or court stamps on the confirmation order.
  • Complex waterfall distribution mechanics may reference creditor class characteristics that could allow indirect identification of specific creditors.

FAQ

Are creditor class names — such as 'Class 3 Senior Secured Claims' — pseudonymized?

Class labels are structural content and are preserved verbatim. The specific creditors within each class who are named in schedules or exhibits are pseudonymized, but the class taxonomy itself is retained.

Will the recovery percentages for each class be visible in the pseudonymized plan?

Yes. Recovery percentages, payment timelines, and distribution mechanics are non-personal structural data and are preserved fully, allowing meaningful precedent analysis.

Can the plan be processed together with the related disclosure statement?

Yes. Uploading the plan and disclosure statement as a package ensures unified pseudonym assignment, so the same entity or individual carries the same alias across both documents.

How are post-confirmation new-value equity contributions handled?

New equity investor names and contribution amounts are treated separately: investor names are pseudonymized while contribution amounts are preserved as financial structural data.

Bankruptcy & Insolvency

About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

We follow these rules

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Our promise

We do not sell your data.

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Where we run

Our servers live in Falkenstein, Germany.

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We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

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One credit covers one short job.

Long jobs use a few credits each.

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Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.