LoGeo

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  Our Landing Page!

Inspiration

Every day, tech companies accidentally violate regional regulations because they don't know which features trigger geo-specific compliance. A simple "age verification" feature might be fine in the US but require extensive GDPR compliance in the EU, or trigger California's strict minor protection laws. We built LoGeo to solve this blind spot helping developers understand regulatory implications before they ship features globally.

What it does and how we built it

We developed a multi-stage AI pipeline that processes features through preprocessing, regex pattern matching, and Named Entity Recognition (NER). Following that, our LLM base assists in standardising the various entities in the feature through processes such as FAISS Vector searches and semantic retrieval. Upon full classification, we then cross-reference the classified entities to existing international, country and state laws appended in our database for matches; a joint, hybrid level with LLM searches and more algorithmic NER searches. Upon returning the confidence levels based on the need to geo-tag the feature, the confidence in classification and the confidence in ensuring it requires law-based geo-tagging, and its reasons why, it is cross-checked against a minimum threshold for each type of law (for example, a confidence score of above 0.85 for safety and health related features), where any score below that would entail human intervention. The human feedback is once again tokenised and updated in the glossary, as well as prompts used in LLM classification through prompt engineering.

Challenges we ran into

Regulatory Complexity: Legal documents are dense and context-dependent. We solved this by building a RAG system with specialized regulatory summaries and semantic search. Confidence Calibration: Determining when the AI is "confident enough" for legal decisions. We implemented category-specific thresholds and multi-stage validation. Law and Geographic Nuance: "EU compliance" can mean different things for different features. We built jurisdiction-specific rule matching with detailed reasoning.

Accomplishments that we're proud of

Debut Hackathon! Comprehensive Coverage: 8 major regulatory frameworks with semantic search across 50+ compliance requirements Smart Uncertainty Handling: Confidence scoring with human review alerts, the system knows when it doesn't know

What we learned

AI + Legal = Complex: Legal reasoning requires nuanced understanding that pure LLMs struggle with. RAG systems with curated regulatory content significantly improve accuracy. Confidence Matters: For compliance tools, being wrong is worse than being uncertain. Building confidence scoring and escalation paths is crucial for adoption. Human-in-the-loop is Essential: Even great AI needs human oversight for edge cases. We learned to design for collaboration, not replacement.

Short-term goals for LoGeo

Integration with CI/CD pipelines for automated compliance checks Real-time regulatory updates via legal database feeds Enhanced LLM integration (GPT-4, Claude) for nuanced analysis Mobile app for on-the-go compliance checking

Built With

• css
• docker
• faiss
• fastapi
• hugging-face
• json
• pandas
• python
• pytorch
• sentence-transformer
• streamlit