The Problem: Privacy vs. Research Value
A mental health research team came to us with a unique challenge: they conduct 50+ participant interviews per month across multiple Indian languages—Hindi, Tamil, Telugu, and English—often mixed within the same conversation.
Each interview transcript is 15-20 pages and contains deeply sensitive information: participant names, family members, therapist identities, specific clinic locations, and personal details shared during therapy sessions.
The critical problems:
- Inconsistent: One researcher might redact "Dr. Kumar", another might leave it thinking it's generic
- Error-prone: Easy to miss a Tamil name ("Lakshmi Amma") buried in an English paragraph
- Context destruction: Replacing everything with
[REDACTED]makes conversation analysis impossible - Language barriers: Code-mixed text ("I met amma at the clinic in Mylapore") requires understanding both languages
- Massive time sink: Three researchers spend 2-3 hours each per document, reading line-by-line
The trust problem: Researchers needed to verify AI suggestions before finalizing redactions. Fully automated redaction without human review was a non-starter for privacy-critical research.
Why Existing Tools Failed
The team tried off-the-shelf redaction software. Standard tools trained on English business documents couldn't parse:
- Hindi-English code-mixing
- Regional names in Devanagari or Tamil script
- Family relationship terms ("amma", "nani", "dada") that are PII in context
This led to either over-redaction (document becomes meaningless) or under-redaction (privacy violations).
Our Solution: AI + Human Review Workflow
We built a system that combines AI intelligence with human judgment. The AI does the heavy lifting, but researchers have final say.
The two-stage process:
- AI reads the entire document for context and language patterns
- AI identifies potential PII with confidence scores (95%+ = definitely PII, 70-95% = review recommended)
- Researcher sees document with AI-highlighted suggestions
- For each suggestion, researcher can accept, reject, or edit the redaction type
- System applies final redactions and creates audit log
Why this hybrid approach works:
- Privacy-safe: Researchers verify every redaction before it's final
- Faster: AI reduces 2-hour review to 20 minutes
- Accurate: Human judgment on edge cases, AI consistency on obvious ones
- Auditable: Complete record of what was changed and why
The Technical Challenge: Multilingual Context Understanding
Modern AI models (like Google's Gemini) are trained on massive multilingual datasets including Hindi, Tamil, Telugu, Bengali, and other Indian languages.
What this means in practice:
Hindi-English code-mixing:
"Meri behen ne kaha ki therapy helpful hai."
AI recognizes: "Meri behen" (my sister) →[FAMILY_MEMBER_1]
Tamil names in English text:
"She spoke about how Annamalai supported her recovery."
AI recognizes: "Annamalai" (Tamil personal name) →[PERSON_1]
But more impressively, the AI understands context. It distinguishes between:
- "mother" (generic reference, keep for context)
- "Radha" appearing later in conversation (specific name, likely the mother →
[PERSON_1])
Numbered markers preserve research context:
Bad redaction: "The [REDACTED] spoke to [REDACTED] about [REDACTED]."
Smart redaction: "The [PARTICIPANT_1] spoke to [THERAPIST_1] about family support. Later [PARTICIPANT_1] mentioned [FAMILY_MEMBER_1] was helpful."
Researchers can still analyze who said what, relationships between people, conversation patterns, and therapy progress—without knowing actual identities.
The Results
Before AI-assisted redaction:
- ⏱️ Time: 2-3 hours per document (pure manual work)
- 📊 Accuracy: 80-85% (audit found 15-20% of personal info was missed)
- 💰 Cost: 80 hours/month × $30/hour = $2,400/month in researcher time
- 🌐 Language coverage: Hindi/Tamil names frequently missed
- 😓 Team morale: Researchers resented this tedious, non-research work
After AI-assisted redaction:
- ⏱️ Time: 15-20 minutes per document (AI + human review)
- 📊 Accuracy: 96% (tested on 100 gold-standard documents)
- 💰 Cost: $0.20 AI per document + 15 hours/month review = $250/month
- 🌐 Language coverage: Consistent across English, Hindi, Tamil, Telugu, code-mixed text
- ✨ Team morale: Researchers focus on actual research
- 🔒 Trust: Researchers maintain final control, building confidence in the system
ROI: System saved $2,150/month. Paid for itself in the first week.
Unexpected benefits:
- AI caught patterns humans consistently missed (family member names mentioned casually mid-paragraph)
- Consistency across research team (AI applies same logic to all documents)
- Confidence scores helped prioritize review time (accept 95%+ suggestions automatically, focus on 70-95% edge cases)
What We Learned
1. Human-in-the-loop is essential for privacy-critical work
We initially considered fully automated redaction. The research team rejected it immediately. When dealing with mental health data, researchers needed to verify that AI didn't over-redact (removing research-critical context) or under-redact (missing privacy violations). The hybrid approach worked: AI does the heavy lifting (reading 20 pages, identifying 50+ entities), humans do final verification (15 minutes of focused review).
2. Indian language support is non-negotiable for Indian research
Early testing with English-only AI models achieved 65% accuracy. With multilingual AI: 96%. The difference was recognizing "Amma" (Tamil/Hindi for mother) as a family reference, understanding "Dr. Sharma" is a person, and parsing code-mixed sentences. For any Indian language use case, test multilingual models from day one.
3. Confidence scores changed everything
Version 1 showed AI suggestions without confidence scores. Researchers reviewed every suggestion equally. Version 2 added confidence scores. Researchers now auto-accept 95%+ suggestions, focus review time on 70-95% edge cases, and manually verify 50-70% suggestions. Impact: Review time dropped from 35 minutes to 15 minutes per document.
Building a healthcare, research, or compliance-heavy product that handles sensitive documents? Let's talk →
We've built similar AI-powered systems for invoice processing, legal document analysis, and medical record redaction. The pattern is the same: AI that understands context beats pattern-matching every time.