Renesis.ai

Implement predictive analytics and AI copilots for risk assessment, fraud detection, and automated reporting in the financial services industry.

Deploy AI assistants in healthcare for administrative task automation, predictive patient outcome analysis, and personalizing patient communication

Deploy AI agents that monitor financial transactions in real-time to flag suspicious activity and automate compliance reporting. Reduce risk, ensure accuracy, and cut down on manual review hours. Streamline client onboarding with a conversational AI that gathers patient data, answers common questions, and intelligently triages cases to the appropriate department, freeing up staff for critical care.

This isn't a single technology but a pipeline of several AI systems working together with minimal delay (low latency): Automatic Speech Recognition (ASR): The system first "listens" to the spoken language (e.g., a witness speaking in Spanish) and converts it into text. This needs to be highly accurate, even with different accents, poor audio quality, or rapid speech. Legally Tuned: The text is then fed into a specialized translation model. This is the core of the system. A generic NMT model (like Google Translate) is not sufficient. This model must be fine-tuned on: Legal Corpora: Millions of documents like contracts, case law, statutes, and legal correspondence. Terminology Databases: Glossaries of specific legal terms ("legalese") for each language pair (e.g., "estoppel," "subpoena duces tecum," "force majeure"). Contextual Understanding: It needs to differentiate the legal meaning of a word from its everyday meaning (e.g., "consideration" in a contract vs. "showing consideration").