When top law firms face AI hallucinations in filings, it exposes a trust gap that erases productivity gains. Korekt adds a source-backed, real-time fact-checking layer into any AI workflow—verifying citations, figures, and stats against primary sources via a browser extension and API. Its freemium SaaS model scales from individual seats to enterprise integrations.

• Sullivan & Cromwell had to apologize to a federal judge over AI-hallucinated citations in a court filing, showing manual fact-checking still erases AI's productivity gains in high-stakes fields.
• Korekt acts as an LLM-agnostic verification layer (API or browser extension) that checks citations, figures, and stats against primary sources like Westlaw or Bloomberg.
• Its business model layers a freemium "Hallucination Grader" and open-source validation library to drive adoption, then monetizes via Pro, Business, and usage-based Enterprise API tiers.
• Its main defense against in-house AI suites from Thomson Reuters and LexisNexis is staying LLM-neutral while building workflow lock-in once embedded in a lawyer's drafting process.

This article discusses BGE-M3, a new AI model that improves how AI systems retrieve and understand information. It addresses the limitations of traditional methods by combining speed, precision, and context, ultimately reducing inaccuracies in AI-generated responses.

• BGE-M3 unifies Dense, Sparse, and Multi-Vector retrieval in one model instead of requiring separate systems for speed and precision.
• Multi-Vector mode compares every query word to every document word, preserving detail that single-vector compression would lose.
• This combined approach directly targets RAG's "semantic gap" problem, reducing hallucinations by balancing speed, accuracy, and context depth.