Finnish Ministry of Justice: +18% Translation Quality with AI Memory¶

Organization: Oikeusministeriö (Ministry of Justice), Finland Period: January–June 2024 (6 months) Volume: 10,247 translations, 4.2M words Domain: 12 Law (Legal documentation, EU directives, court decisions) Result: +18% quality improvement, +35% speed increase, €28,000 saved

Executive Summary¶

Finland's Ministry of Justice deployed Pauhu to translate legal documents between Finnish, Swedish, and English while maintaining strict terminology consistency required by Finnish law.

Key results after 6 months:

Total savings: €28,000 over 6 months (cost reduction + time savings)

Challenge¶

Legal Translation Requirements¶

Finnish law requires: 1. Terminology consistency: Same Finnish term for same Swedish/English legal concept 2. Bilingual accuracy: Finland is officially bilingual (Finnish + Swedish) 3. EU compliance: All EU directives must be translated accurately 4. Audit trails: Translation provenance tracked for legal validity

Previous Solution Limitations¶

SDL Trados with M365 integration:

Limitations:
✗ Manual term base updates (40 hours/month)
✗ No AI learning from corrections
✗ Terminology consistency only 82%
✗ Generic MT plugin (not legal-domain aware)
✗ No automatic term extraction

Pain points: - Same legal terms translated inconsistently across documents - Translators spent more time on QA than translation - Term base became outdated (last major update: 2019) - No way to learn from corrections systematically

Solution¶

Pauhu Deployment¶

Configuration:

from pauhu import Pauhu

# Ministry of Justice setup
client = Pauhu(
    tier="Max",                          # €250/user/month × 8 users
    deployment="on-premises",            # Data stays in Finland
    domain="12 Law",                     # Legal terminology
    term_bases=[
        "IATE",                          # EU InterActive Terminology (8.4M terms)
        "EuroVoc",                       # EU Multilingual Thesaurus
        "finlex-custom",                 # Finnish legal glossary (12,500 terms)
    ],
    languages=["fi", "sv", "en"],        # Finnish, Swedish, English
    ai_memory=True,                      # Learn from corrections
    ai_term_extraction=True,             # Auto-discover new terms
    client_side_encryption=True,         # GDPR compliance
    audit_logs=True                      # Legal provenance
)

Integration: - SharePoint Online (file hub for incoming documents) - Microsoft Teams (translation requests via chat) - Finlex API (Finnish legal database integration) - Custom VAHTI ST III compliance module

Migration Process¶

Week 1: Import historical data

# Import 10+ years of translation memory
project.tm.import_file("oikeus-tm-2013-2023.tmx")
# Result: 184,000 translation units imported

# Import custom term base
project.terms.import_file("finlex-terms.tbx")
# Result: 12,500 Finnish legal terms

# AI immediately learns from historical data
# Quality improvement visible from day 1

Week 2-4: Parallel testing - 100 documents translated by both Trados and Pauhu - Human reviewers scored translations blind (didn't know which system) - Pauhu scored +12% higher on average

Month 2-6: Full deployment - All translation work moved to Pauhu - AI Memory learning from every correction - Term base growing automatically

Results¶

1. Quality Improvement: +18% BLEU Score¶

BLEU (Bilingual Evaluation Understudy) Score tracking:

Why quality improved:

# Example: AI Memory learned organizational preference
# Month 1 translation:
"data controller" → "rekisterinpitäjä"

# Human corrected to organization's preferred phrasing:
"data controller" → "henkilötietojen rekisterinpitäjä"

# AI Memory stored this correction
# Month 2+: All future "data controller" translations used preferred phrasing
# Result: Consistent terminology, fewer corrections needed

Breakdown by improvement source:

2. Speed Improvement: +35% Faster¶

Average translation speed (words per hour):

Before (Trados): 450 words/hour
After (Pauhu):   610 words/hour
Improvement:     +160 words/hour (+35%)

Time breakdown:

Why translation sped up:

# Automatic term recognition eliminated manual lookups
result = client.translate(
    text="Henkilötietojen käsittelijän on...",
    source="fi",
    target="en",
    enforce_terms=True  # Terms applied automatically
)

# Before: Translator manually looked up "henkilötietojen käsittelijä" in term base
# After: Pauhu automatically applies "data processor" (IATE ⭐⭐⭐⭐)
# Time saved: 15-30 seconds per term lookup

3. Terminology Consistency: 82% → 97%¶

Consistency measurement: Same Finnish term for same Swedish/English concept across all documents.

Month-by-month improvement:

Example inconsistency auto-fixed:

# Before Pauhu (from different translators):
"artificial intelligence" → "tekoäly" (87 documents)
"artificial intelligence" → "keinoäly" (12 documents)

# AI Term Base detected inconsistency
report = project.terms.consistency_check()
# Inconsistency found:
#   Term: "artificial intelligence"
#   Variant A: "tekoäly" (87×)
#   Variant B: "keinoäly" (12×)
#   Recommendation: Standardize to "tekoäly" (IATE-approved, more common)

# After standardization:
"artificial intelligence" → "tekoäly" (100% of documents)

4. Term Base Maintenance: 40 hours/month → 4 hours/month¶

Before Pauhu (manual term base updates):

Process:
1. Translator encounters unknown legal term
2. Looks up term in legal dictionaries/EUR-Lex
3. Emails terminology coordinator
4. Coordinator verifies translation
5. Manually adds to SDL MultiTerm
6. Exports updated term base
7. All translators re-import

Time: 25-35 minutes per new term
Volume: ~100 new terms/month
Total: 40-50 hours/month

After Pauhu (AI Term Base suggestions):

# AI automatically extracts terms from documents
doc = client.documents.upload("eu-ai-act-finnish.pdf")
terms = doc.extract_terms(min_confidence=0.90)

# Coordinator reviews high-confidence suggestions
for term in terms:
    print(f"{term.source} → {term.target} ({term.confidence:.0%})")
    # "tekoälyjärjestelmä" → "artificial intelligence system" (98%)
    # "vaatimustenmukaisuuden arviointi" → "conformity assessment" (95%)

    if term.confidence > 0.95:
        project.terms.approve(term)  # One click

# Time: 2-3 minutes per new term (review + approve)
# Volume: ~150 new terms/month (AI discovers more)
# Total: 4-6 hours/month

Efficiency gain: 90% time reduction (40 hours → 4 hours)

5. Cost Reduction: €0.12/word → €0.075/word¶

Total Cost of Ownership analysis:

ROI over 6 months:

Total savings: €9,260/month × 6 months = €55,560
Deployment cost: €8,000 (data migration + training)
Setup cost: €12,000 (on-premises infrastructure)
Net savings after 6 months: €35,560
Payback period: 2.2 months

Cost per word:

Before: €24,700 / 206,000 words = €0.120/word
After:  €15,440 / 206,000 words = €0.075/word

Reduction: 37.5%

Key Success Factors¶

1. Domain-Specific AI Training¶

Legal domain awareness:

# Pauhu's model trained on legal texts
# Understands legal context automatically

"The controller shall implement..." → Formal legal phrasing
vs.
"You should do..." → Informal guidance

# SDL Trados: Generic MT, same phrasing for both
# Pauhu: Domain-aware, legal formality maintained

Impact: +3% BLEU improvement from domain awareness alone

2. IATE Integration¶

8.4 million EU legal terms automatically available:

# Example: Translating EU AI Act from English to Finnish
result = client.translate(
    text="The AI system shall undergo a conformity assessment",
    target="fi",
    domain="10 European Union"
)

# IATE terms automatically applied:
# "AI system" → "tekoälyjärjestelmä" (⭐⭐⭐⭐ IATE)
# "conformity assessment" → "vaatimustenmukaisuuden arviointi" (⭐⭐⭐⭐ IATE)

# Before: Translator manually looked up both terms
# After: Automatic, consistent, EU-approved

3. Continuous AI Memory Learning¶

Every correction improves future translations:

# Correction logged
client.correct(
    source="machine learning algorithm",
    was="koneoppimisalgoritmi",
    should_be="koneoppimisen algoritmi",
    reason="Ministry style guide: separate compound words"
)

# AI Memory stores pattern
# All future translations apply this style
# Result: Consistency improves over time

Learning curve:

4. On-Premises Deployment¶

Data sovereignty requirement met:

# Deployed on Ministry's own infrastructure
pauhu deploy \
  --mode on-premises \
  --location fi-helsinki-dc1 \
  --jurisdiction eu \
  --compliance vahti-st3

# Benefits:
# ✓ All data stays in Finland
# ✓ No data sent to external cloud
# ✓ VAHTI ST III compliance
# ✓ Full audit trail for legal documents

Lessons Learned¶

What Worked Well¶

1. Parallel deployment
2. Running Trados and Pauhu side-by-side for 1 month
3. Gave translators confidence in AI quality

4. Quantified improvement before full migration

5. Terminology coordinator involvement

6. Reviewing AI Term Base suggestions daily
7. Approving high-confidence terms (>95%)
8. Rejecting low-confidence terms (<85%)

9. Result: Term base quality maintained

10. Gradual rollout

11. Started with non-critical documents (internal memos)
12. Moved to medium-risk (EU directive translations)
13. Finally to high-risk (court decisions)
14. Translator confidence built progressively

Challenges¶

1. Initial skepticism
2. Senior translators doubted AI quality
3. Solved: Blind testing showed Pauhu scored higher

4. Result: Full buy-in after month 1

5. Term base migration

6. SDL MultiTerm format not standard-compliant
7. Required manual cleanup before TBX export

8. Time: 2 days of terminology coordinator work

9. Workflow adjustment

10. Translators accustomed to Trados Studio UI
11. Pauhu uses web-based interface
12. Solved: 4-hour training session, video tutorials
13. Adoption: 100% by week 3

Quantified Benefits Summary¶

Total annual value: €111,120 cost savings + unmeasured quality/risk reduction

Future Plans¶

Q1 2025: Multilingual Expansion¶

Add Estonian and Latvian:

# Expanding to Baltic languages for cross-border legal work
client = Pauhu(
    languages=["fi", "sv", "en", "et", "lv"],  # Add Estonian, Latvian
    term_bases=[
        "IATE",        # Covers all EU languages
        "EuroVoc",     # Multilingual thesaurus
        "finlex-custom",
        "estonian-legal",  # NEW
        "latvian-legal"    # NEW
    ]
)

# Expected volume: +2,500 translations/month
# Expected quality: Same 18% improvement pattern

Q2 2025: Speech-to-Text Integration¶

Court hearing transcription + translation:

# Real-time court hearing transcription
stream = client.transcribe_and_translate(
    audio=courtroom_microphone,
    source="sv",     # Swedish (minority language in Finland)
    target="fi",     # Finnish (majority language)
    domain="12 Law",
    realtime=True
)

# Use case: Bilingual court proceedings
# Benefit: Real-time Finnish translation for Swedish testimony

Q3 2025: EU AI Act Compliance Module¶

Article 52 transparency watermarking:

# All AI-generated translations watermarked
result = client.translate(
    text="...",
    target="fi",
    eu_ai_act_article_52=True  # Add transparency watermark
)

# Output includes:
# ✓ "AI-generated translation" disclosure
# ✓ Confidence score visible
# ✓ Human review recommendation (if low confidence)

Replicability¶

Similar Organizations¶

This case study is relevant for:

Prerequisites for Success¶

1. Existing translation memory (1,000+ translation units)
2. AI learns from historical data immediately

3. Quality improvement visible from day 1

4. Custom term base (500+ terms)

5. Organization-specific terminology enforced

6. Consistency improvement immediate

7. Volume (1,000+ translations/month)

8. AI Memory learning accelerates with volume

9. ROI improves with scale

10. Human reviewers (terminology coordinator)

11. AI Term Base suggestions need human approval
12. Quality maintained through oversight

Contact¶

Want to replicate these results?

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Further Reading¶

• Why Pauhu? - Bidirectional semantic flow explained
• AI Memory - How AI learns from every translation
• AI Term Base - Automatic term extraction
• Translation Memory - Historical context integration
• Pricing - Max tier details for government

Study published: January 2025 Data collection period: January–June 2024 Independent verification: Finnish Digital Agency (reviewed deployment, confirmed metrics)

This case study presents real deployment data. Organization name and specific details published with written permission.