What an AI Proof of Concept Involves (and Costs)
Learn what an AI Proof of Concept (PoC) involves, from scoping and data requirements to timelines and costs. Discover how to validate your AI use case before committing to full production.
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LegalTech
LLM-Powered Legal Document Automation
Built a secure RAG (Retrieval-Augmented Generation) pipeline to automate the first-pass review of 10,000+ contracts, reducing manual review time by 70%.
ClientInternational Law Firm
CategoryAI/ML
The Challenge
High accuracy requirement (zero-hallucination tolerance) and strict data residency laws prohibiting the use of public LLM endpoints.
Review Time
70% decreaseAccuracy
98.5% verifiedCost per Document
Reduced by $12The Technical Solution
We avoided the 'easy' path of templates and plugins, instead engineering a system built for scale and long-term maintainability.
Architecture
Implemented a local Llama-3 instance deployed via vLLM on private GPU clusters, with a hybrid search strategy (BM25 + Vector search) using Qdrant.
Tech Stack
PythonFastAPIQdrantvLLMReact
Key Decisions
Hybrid Search Strategy
Pure vector search missed specific legal citations; combining it with keyword search ensured 100% retrieval of critical clauses.
Self-Hosted LLM
Ensured data never left the regional VPC to comply with strict legal confidentiality and residency requirements.
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