Diash Firdaus

TechVal AI is an AI-powered, multi-agent technology assessment and patent valuation platform developed through a research collaboration between SINC LAB and ITENAS Bandung to bridge the *“Valley of Death”* between academic inventions and industrial commercialization. By orchestrating eight specialized models from the Meta Muse multimodal ecosystem—including muse-spark, muse-wonder, muse-image, and muse-voice-transcribe—through LangGraph, TechVal AI automates patent document analysis, source-verified evidence extraction, intellectual property assessment, technology readiness evaluation using NASA TRL 1–9, and commercialization readiness scoring through Saaty’s Analytic Hierarchy Process (AHP) with a consistency ratio of CR ≤ 0.10. The platform integrates a deterministic financial engine aligned with WIPO (2024) valuation approaches, including Discounted Cash Flow (DCF) with Monte Carlo simulation, Relief-from-Royalty, Cost, and Market Comparables, producing transparent Indonesian Rupiah (IDR) valuations without arbitrarily converting qualitative scores into monetary values. Its architecture enforces strict separation between technical assessment, commercialization readiness, economic valuation, and evidence completeness, while incorporating quote-level traceability, cryptographic document verification, encrypted storage, prompt-injection protection, and revision-bound human expert approval. Built with Next.js, React, TypeScript, FastAPI, PostgreSQL, and LangGraph, TechVal AI aims to reduce preliminary patent valuation preparation from 4–8 weeks to under one minute at an estimated AI processing cost of $0.05–$0.15 per assessment, while targeting evidence verification F1-scores above 0.94 and deterministic financial calculation accuracy. By transforming complex patent documentation into structured, explainable, and auditable valuation dossiers, TechVal AI empowers universities, Technology Transfer Offices (TTOs), research institutions, investors, and industry partners to accelerate licensing decisions, improve commercialization readiness, and unlock the economic potential of research-driven innovations.