Anonymization
PROJECT SUMMARY
CHALLENGE
The client needed to leverage public AI for contract analysis without exposing sensitive contract details (names, bank info, addresses, monetary values, etc.)
PRODUCT
We deployed a lightweight, on‑premise anonymization engine that masks sensitive fields and works via internal email or web portal
DELIVERABLES
Anonymization engine deployed on-premise; n8n-powered integration with corporate email and web portal; Blends into the internal workflowCLIENT REVIEW
We’re actually quite AI-friendly — we don’t have a problem using public LLMs for internal tasks. But when it comes to legal contracts, data privacy is a huge blocker. You can’t just throw unfiltered legal docs into GPT and hope for the best. So we had to manually remove all sensitive data from docs before feeding them into public LLMs. Sure, it took time, but more critical — it was easy to overlook some PII in the process. We needed a way to keep sensitive data in-house while still using AI where it makes sense. This anonymizer was exactly what we needed. It scrubs the sensitive stuff but keeps the context, so we can still get useful insights from AI.
OVERVIEW
Our client wanted to use public AI chat and analytics tools to review legal contracts, but couldn’t risk sharing personally identifiable or financial data with external services.
We built a lightweight, on-premise contract anonymizer that automatically detects and masks sensitive elements like names, addresses, bank details, sums, and percentages, while preserving context. Users simply upload documents via a secure web interface or email them to a dedicated corporate address; the system returns anonymized versions suitable for public AI tools. When needed, the original documents can be de-anonymized in-house.
CHALLENGE
Data sensitivity: Contracts contained personally identifiable information and financial data that could not be shared externally.
Speed & scale: Manual redaction took up to 30 minutes per document — unsustainable for high‑volume review.
Usability: Legal and paralegal staff needed a simple interface, no new software or complex prompting.
Infrastructure constraints: The solution had to run on existing standard servers, without GPUs or extra hardware.
SOLUTION
On‑Premise Anonymizer Engine:
- Uses pattern matching and NLP to locate names, addresses, bank details, monetary amounts, percentages, dates, and more.
- Replaces each instance with a unique placeholder.
Integration via n8n:
- Email workflow. Send any contract as an attachment to a dedicated corporate address. Within seconds, the anonymized file is emailed back.
- Web portal. Drag‑and‑drop interface on the corporate intranet. Users upload documents and download anonymized versions instantly.
Deployment & Training:
Week 1. n8n workflow configuration, security review, and end‑to‑end testing.
Week 2. Onboarding sessions for legal staff, creation of quick‑start guides, and final adjustments.
VALUE DELIVERED
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OUR RECENT PROJECTS
Speeding Up Contract Review by 90% with On-premises AI
- Automated contract reviews with private AI
- 90% faster than manual review
- Fully trained on internal policies
- Seamless integration with internal tools
- On-premise deployment for full privacy
Private LLM for Legal Contract Drafting – 86% Faster
- 86% faster contract drafting
- Fully aligned with internal legal style
- On-premise for total data privacy
- Clause generation from simple instructions
- Less time spent on manual edits
- Seamless internal integration
