Anonymise Constructive Dismissal Correspondence for Advice and Disclosure – UK GDPR-compliant anonymisation per ERA 1996 s.94

Constructive dismissal correspondence — including the employee's resignation letter citing fundamental breach, grievance escalations, and prior management communications — contains names, detailed incident descriptions, and sensitive allegations. anonym.legal pseudonymises this personal data so that the chain of correspondence can be reviewed by solicitors or used in training without revealing the identities of those involved.

When this applies

Apply this workflow when an employee has resigned claiming constructive dismissal under ERA 1996 s.94 and the employer's legal team or HR function needs to review the correspondence bundle without unnecessary disclosure of personal data.

  1. Upload the full chain of constructive dismissal correspondence, including the resignation letter, any prior grievance letters, and management responses.
  2. The engine identifies names, job titles, dates linked to individuals, and sensitive incident descriptions across the entire correspondence set.
  3. All individuals mentioned — the resigning employee, named managers, HR personnel, and witnesses — are pseudonymised consistently across every document.
  4. Substantive content — dates of alleged breaches, contractual terms at issue, and procedural history — is retained in plain text.
  5. The mapping between pseudonyms and real identities is stored encrypted with EU data residency.
  6. The pseudonymised bundle is shared with legal advisers for merits assessment; re-identification is available via the stored key for formal proceedings.

What you provide

  • Resignation letter citing fundamental breach
  • Prior grievance letters and employer responses
  • Relevant management communications and meeting notes
  • Any attached investigation or outcome documents

Limitations & cautions

  • anonym.legal does not assess the legal merits of a constructive dismissal claim; solicitor advice remains essential.
  • Highly context-specific allegations — for example, referencing a unique internal incident known to a small team — may need manual redaction beyond automated pseudonymisation.
  • The tool processes documents provided to it; it does not retrieve correspondence from email or HR systems.

FAQ

Can the full correspondence chain be processed as a single batch?

Yes. Multiple related documents — resignation letter, grievance letters, management responses — can be uploaded together. The engine assigns consistent pseudonyms across the entire set so that the narrative relationship between documents is preserved.

Will dates of alleged incidents be pseudonymised or retained?

Dates that are not linked to an individual's personal identifier are retained as factual content. Only dates that directly identify a person — such as a date of birth — are pseudonymised. Incident dates, grievance submission dates, and contractual milestone dates remain in plain text.

How does pseudonymisation help when sharing with an external solicitor?

Sharing pseudonymised correspondence reduces the volume of personal data leaving the organisation while still enabling the solicitor to advise on the legal merits. Under UK GDPR Art. 6, this supports the principle of data minimisation. A full-identity version can be provided to the solicitor under a data-processing agreement if required for formal proceedings.

Is special category data such as health information detected in the correspondence?

Yes. Where employees have raised health issues — such as stress or disability — as part of the fundamental breach allegation, the engine flags this as special category data under UK GDPR Art. 9 and applies enhanced pseudonymisation. You should review the output to confirm all sensitive content has been addressed.

Employment Law

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

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

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

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

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.