Legal AI Infrastructure for Transactional Practice
Not a chatbot. Not a co-pilot. Infrastructure that processes, structures, and surfaces contract intelligence at the scale transactional practice demands.
Request DemoThe Problem with Legal AI Today
Most legal AI tools fall into two categories. Neither solves the core challenge of transactional practice.
Chatbots
Chat-based AI answers questions one at a time. Ask about a contract and get a summary. Ask another question, get another answer. This works for research. It does not work when you need to extract 30 provision types from 500 documents and produce structured output for client deliverables.
Co-Pilots
Co-pilot AI assists attorneys inline as they work on individual documents. This is useful for drafting and editing. It does not help when the task is to analyze an entire data room, compare provisions across contracts, and surface risks that span multiple agreements.
What Transactional Practice Actually Needs
Transactional attorneys do not need a chatbot or a co-pilot. They need infrastructure that ingests entire document portfolios, classifies and extracts structured data across them, and produces the tabular, source-linked output that diligence workflows require. That is what Mage builds.
Three-Layer Architecture
Ingestion
Upload entire data rooms. Mage handles PDFs, Word documents, and scanned images with OCR. Documents are parsed, normalized, and prepared for analysis. The system processes hundreds of documents concurrently.
Intelligence
AI classifies each document by type, extracts provisions at the paragraph level, and flags risks. Multi-model consensus ensures accuracy. Every finding maps to a specific location in the source document.
Output
Findings are organized in structured grids, comparison views, and exportable formats. Attorneys review tabular output, verify against source text, and export results for diligence memos and client deliverables.
Built for Transactional Workflows
M&A Due Diligence
Process entire data rooms in minutes. Extract change of control provisions, assignment restrictions, liability caps, and 30+ other provision types. Produce structured diligence output that attorneys can verify and export.
Disclosure Schedule Preparation
Identify provisions that require disclosure before buyers request them. Map contracts to schedule items. Prepare comprehensive schedules faster and with greater confidence that nothing material has been missed.
Portfolio Analysis
Analyze contract portfolios for private equity firms, corporate development teams, and serial acquirers. Maintain consistent extraction standards across multiple targets and compare findings across deals.
Financing Transactions
Review loan agreements, credit facilities, and security documents. Extract covenant terms, default triggers, and consent requirements across the capital stack. Surface provisions that impact deal execution.
Frequently Asked Questions
Common questions about Mage Legal AI Infrastructure
Legal AI infrastructure is a category of technology that processes, structures, and surfaces intelligence from legal documents at scale. Unlike chatbots that answer questions or co-pilots that suggest text, infrastructure handles the foundational work of ingesting, classifying, and extracting structured data from contract portfolios.
A chatbot answers questions through conversation. Mage processes entire data rooms and produces structured output: classified documents, extracted provisions in tabular format, and risk-flagged findings linked to source text. The output is a comprehensive analysis, not a conversation thread.
A co-pilot assists attorneys inline as they work, suggesting edits or answering questions about the document in front of them. Mage operates at the portfolio level, analyzing hundreds of documents concurrently and producing cross-document intelligence that no single-document tool can generate.
Mage operates across three layers: ingestion (document upload, OCR, and parsing), intelligence (classification, extraction, and risk analysis), and output (structured grids, exports, and deliverables). Each layer is optimized for the specific demands of transactional legal work.
Transactional practice involves analyzing large portfolios of contracts under tight deadlines. General AI tools designed for research or drafting cannot process hundreds of documents, extract structured data across them, or produce the tabular output that diligence workflows require. Specialized infrastructure fills this gap.
Yes. Mage exports to standard formats including Excel, Word, and PDF that integrate with existing document management and client reporting workflows. The platform is designed to complement existing tools rather than replace entire technology stacks.
Mage uses enterprise-grade security including AES-256 encryption at rest, TLS 1.3 encryption in transit, isolated processing environments, and a zero data retention policy. The platform is designed to meet the confidentiality requirements of transactional legal work.
No. Any firm or legal team doing transactional work with contract-heavy diligence can benefit from infrastructure-level AI. Boutique firms, in-house teams, and large practices all face the same fundamental challenge of analyzing large document sets under time pressure.
See the Infrastructure Difference
Request a demo to see how infrastructure-level AI transforms transactional practice. Process your own documents and see structured results.
Request Demo