Hebbia

Hebbia is an enterprise AI platform for indexing, searching, and analyzing large volumes of unstructured documents. It targets high-stakes industries including asset management, private equity, investment banking, legal services, and corporate strategy. The platform converts dense, multi-source document repositories into structured, queryable data. The core interface is called Matrix. Users upload documents into a secure […]

ResearchRabbit

ResearchRabbit is a web-based literature discovery and citation mapping platform for academic researchers and students. It converts a small set of user-supplied papers into a visual, expandable network of related academic works. The tool targets the problem of manual, fragmented keyword searching across multiple disconnected databases. Users create collections, add seed papers, and the platform […]

LegalOn

LegalOn is an AI contract review platform built specifically for in-house legal teams, legal operations professionals, and procurement specialists. It automates pre-signature contract review and post-signature obligation tracking. The platform reports an average 85% reduction in contract review times and is trusted by over 8,000 customers globally. Users upload contracts in .docx or PDF format […]

Harvey

Harvey is a generative AI platform built specifically for legal professionals. It uses large language models fine-tuned for legal tasks across litigation, transactional law, and corporate compliance. The platform addresses the operational burden of document-heavy legal work by automating the initial layers of synthesis and extraction. Users interact with Harvey through a natural language interface. […]

Maket

Maket is an AI-powered platform built for generating, iterating on, and visualizing residential floor plans. Users describe their requirements in natural language, including room types, square footage, shape, and architectural style, and the AI produces layout options based on those inputs. The platform targets people without architecture or CAD backgrounds, aiming to simplify the early […]