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Law Degree + AI Engineering — Building & Advising
AI Architect with a Law Degree — Production AI That's Technically Excellent and Legally Sound
Founder and consultant helping engineering teams ship AI that survives audits, lawyers, and production — without slowing the engineering org down.
MA in Law (University of Wroclaw) + NeurIPS & ACL published AI researcher. 14+ years and 30+ production systems bridging two worlds most companies hire separately. I ship AI that makes it past both code review and legal review — which is why engineering orgs in regulated domains (legal, tax, finance, healthcare) trust me with the hard calls.
14+ Years in Production AI30+ Systems ShippedNeurIPS & ACL Peer ReviewerMA in Law, Wrocław
471+
Citations
25+
Publications
14+
Years Experience
30+
Production Systems
Trusted by leading organizations
AvayaPanasonicWELLEdvantisBrand24MeaningCloudWroclaw University of Science and TechnologyStanford University
Sound Familiar?
These are the challenges I help CTOs and engineering leaders solve
Your AI pilot has been in 'production' for 18 months
Endless POCs that never ship. Leadership is losing patience.
I deploy production AI in 4-6 weeks with my Forward-Deployed Engineering approach.
Avaya: 500K calls/day in productionYour team is paralyzed by AI framework choices
LangChain vs LlamaIndex vs custom? GPT-4 vs Claude vs open source? Analysis paralysis.
I bring research-backed recommendations and build what works for your specific needs.
CLARIN-PL: Built Polish NLP infrastructure from scratchLeadership wants ROI numbers you can't provide
AI feels like a black box. You can't quantify impact or justify continued investment.
Every project I deliver includes measurable business metrics and clear ROI.
Construction AI: 88% accuracy, 20% cost avoidanceDealing with a different AI challenge? Let's talk
Rare Intersection
AI + Law: Where Most Consultants Can't Go
Dual degree in Computer Science and Law. I help teams ship AI that's both technically rigorous and legally defensible — before your regulator, client, or board asks the hard questions.
EU AI Act Compliance
Risk classification, technical documentation, and conformity assessments for high-risk AI systems. Ship with audit-ready artifacts from day one.
Legal NLP & LegalTech
Production RAG over case law, contract analysis, and legal intelligence systems. AI-Tax serves 50+ researchers across 3M+ legal documents.
CS + Law Dual Degree
I read transformer papers and GDPR articles in the same sitting. No translation layer between your engineers and your compliance team.
How We Can Work Together
Three engagement models designed around your timeline and goals
2-4 weeks
Discovery
Strategy & Assessment
- •AI Readiness Assessment
- •Architecture Review
- •Implementation Roadmap
Most Popular
1-3 months
Build
Custom AI Development
- •Custom ML/LLM Development
- •RAG Systems
- •Production API Deployment
Retainer
Scale
Ongoing Support
- •Performance Monitoring
- •Model Optimization
- •Team Training
Featured Case Studies
Production AI systems delivering measurable business impact
AI-Tax Legal Assistant
87% reduction in legal research time
Production-grade legal intelligence platform serving 50+ researchers across 3M+ legal documents with RAG and hybrid search
87%
timeSaved
3M+
documents
PythonFastAPILangChainRAG
Buy It - TV Fashion Discovery
90% cost reduction in video processing
AI platform automatically identifying shoppable fashion from TV shows using computer vision (YOLO) and Gemini Vision
10K+
products
92%
accuracy
Next.jsYOLOGeminiComputer Vision
PanasonicWELL - Family Wellness AI
Global-scale AI for Panasonic — 2+ years
AI Solutions Architect for Panasonic enhancing everyday well-being of families globally through conversational AI and automation
Global
scope
2+ years
duration
LLMsConversational AIProcess Automation
Proven Results
Real outcomes from production AI systems
+15%
Agent Productivity
Avaya Conversational AI
500K+ calls/day processed
88%
Prediction Accuracy
Construction Project AI
4-6 week risk forecasting
+25%
Conversion Rate
B2B Lead Generation
10M+ companies covered
Verified Testimonial
“
Lukasz brings a rare combination of deep academic knowledge and practical engineering skills. His ability to translate complex research into production-ready systems is exceptional.
Prof. Tomasz Kajdanowicz
Professor & Research Lead
Wroclaw University of Science and Technology
Multiple commercialized research products
441+ Citations
NeurIPS & ACL Published
30+ Production Systems
Free AI Tools
Interactive tools to help you evaluate and plan AI initiatives
Popular
LLM Comparison
Compare GPT-4, Claude, Gemini — pricing, context windows, and capabilities
Interactive
AI ROI Calculator
Estimate return on investment from AI consulting services
Useful
Token Cost Calculator
Estimate API costs across OpenAI, Anthropic, Google, and more
Academic
Citation Manager
Generate citations in APA, MLA, Chicago, and BibTeX formats
Research Publications
Peer-reviewed work at top venues including NeurIPS, ACL, and EMNLP
471+
Citations
25
Publications
12
h-index
EMNLP 2020A
WER we are and WER we think we are
Questions reported word error rates from ASR systems on spontaneous speech datasets, revealing systematic issues in evaluation methodology.
76 citations
Entropy 2016Q2 Journal
Comprehensive study on lexicon-based ensemble classification sentiment analysis
Novel frequentiment method for counting sentiment orientation that outperforms supervised learning approaches in time and memory complexity.
61 citations
Computer Speech & Language 2021Q1 Journal
Comprehensive analysis of aspect term extraction methods using various text embeddings
Comprehensive study of aspect-based sentiment analysis methods, proposing comparisons with ablation analysis using various text embedding methods, particularly focusing on LSTM architectures with CRF enhancement.
56 citations
Speaking Engagements
Presenting research at top AI and NLP conferences worldwide
24 talks across 16 countries:
PolandUSAAustriaCanadaItalyUKBelgiumChinaVietnamSingaporeIrelandFranceJapanSpainSwedenThailand
NeurIPS 2023
Massively Multilingual Corpus of Sentiment Datasets
New Orleans, USA
NeurIPS 2022
LEPISZCZE: A Comprehensive NLP Benchmark for Polish
New Orleans, USA
ACL 2023
Why Aren't We NER Yet? ASR Errors in Named Entity Recognition
Toronto, Canada
EMNLP 2020
WER we are and WER we think we are
Virtual
Forward Deployed Engineering
From Concept to Production
A proven methodology for delivering production AI systems at startup speed with enterprise reliability
Embedded with Your Team
Not just a consultant who delivers and leaves. I work alongside your engineers, understanding your codebase, culture, and constraints.
Weeks, Not Quarters
Rapid deployment methodology honed over 30+ projects. From concept to production MVP in 4-6 weeks.
Research-to-Production Bridge
NeurIPS and ACL publications meet real-world engineering. Academic rigor with startup speed.
Typical Engagement Timeline
Week 1-2
Discovery & Architecture
Deep dive into your business context, data landscape, and technical requirements. Deliver architecture blueprint and project roadmap.
Requirements Document
Architecture Design
Project Timeline
Week 3-4
MVP Development
Rapid iteration on core functionality. Working prototype with real data integration and initial model training.
Working Prototype
Initial ML Models
API Integration
Week 5-8
Production Hardening
Scale, optimize, and prepare for production. Performance tuning, testing, documentation, and team handoff.
Production Deployment
Documentation
Team Training
Ongoing
Support & Iteration
Continuous monitoring, optimization, and feature expansion based on production feedback and evolving needs.
Performance Reports
Model Updates
Strategic Guidance
Week 1-2
Discovery & Architecture
Deep dive into your business context, data landscape, and technical requirements. Deliver architecture blueprint and project roadmap.
Requirements DocumentArchitecture DesignProject Timeline
Week 3-4
MVP Development
Rapid iteration on core functionality. Working prototype with real data integration and initial model training.
Working PrototypeInitial ML ModelsAPI Integration
Week 5-8
Production Hardening
Scale, optimize, and prepare for production. Performance tuning, testing, documentation, and team handoff.
Production DeploymentDocumentationTeam Training
Ongoing
Support & Iteration
Continuous monitoring, optimization, and feature expansion based on production feedback and evolving needs.
Performance ReportsModel UpdatesStrategic Guidance
Ready to get started? Book a discovery call
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Ready to Ship AI That Legal Will Sign Off On?
Free 30-minute discovery call. Bring your use case and your compliance constraints — we'll figure out together whether this is a fit. No slides, no pitch.
Typical engagement: MVP in 4-6 weeks | Full production in 2-3 months