Module 3.3 · Topic 4

Model Access and Deployment

Bottom Line Up Front: AI is accessible through multiple channels: web interfaces (ChatGPT), APIs (integrate with custom applications), desktop/mobile apps (native performance), browser extensions (Gmail, Docs), and…

4.1 API-Based Access and Cloud Services

APIs integrate AI into your software. Your case management system calls the AI API automatically, processes output, and displays results. Firms automate contract review, memo generation, document prioritization, and conflict flagging via APIs.

Custom automation: Contract analyzer, deposition summarizer, conflict checker—integrated into case management.
Integration: Fine-grained control: adjust parameters, chain calls, process output, log interactions for compliance.
Costs: Pay per call; scales with usage. Optimize with batching or smaller models.
Data flow: Transmitted to provider's servers. Understand data handling: logging policy? Model improvement use?

4.2 Desktop and Mobile Applications

Native apps offer better performance, offline capability, and device integration. Desktop apps run locally, respond instantly, work offline. Mobile apps enable on-the-go research. Apps are either thin clients calling APIs (convenience + cloud cost) or local models (privacy + offline, larger size). For routine work, thin clients suffice. For sensitive work, local models offer privacy. Native apps integrate better with OS: spell-check, search, clipboard, file access.

4.3 Browser-Based and Embedded AI

Browser extensions embed AI in tools you use daily: Gmail auto-compose, Google Docs memo drafting, case management AI. This eliminates tool-switching. Data flow concerns remain (info transmitted to provider), but usability benefit is substantial: AI one click away. Many legal vendors embed AI directly, recognizing firms adopt integrated tools over isolated interfaces.

4.4 On-Premise and Private Deployment Options

On-premise deployment runs AI on your firm's servers so no data leaves your infrastructure. Complete control, most secure, but requires server investment and technical staff. Fits high-confidentiality work (M&A, privilege counsel, sensitive litigation) that justifies operational overhead. Microsoft Sovereign Cloud and Anthropic/OpenAI on-premise options exist. Cost is higher than cloud APIs, but for billion-dollar deals or sensitive work, privacy assurance is worth it.

Topic 4 details

Topic overview

Bottom Line Up Front: AI is accessible through multiple channels: web interfaces (ChatGPT), APIs (integrate with custom applications), desktop/mobile apps (native performance), browser extensions (Gmail, Docs), and on-premise servers (maximum privacy). Choose the deployment pattern that fits your workflow and security needs. Most law firms start with web interfaces or APIs, then layer in specialized apps where the workflow benefit is clear.

The same foundation model can be accessed in multiple ways, each with different tradeoffs. ChatGPT.com is convenient but requires data upload to OpenAI's servers. An API integration automates AI into your existing tools (case management system, document automation) but requires development effort. A desktop application offers faster performance and offline use. A browser extension embeds AI directly into your workflow (Gmail, Google Docs) without tool switching. An on-premise deployment keeps all data on your servers but requires infrastructure management. This topic maps out your options and helps you pick the right access pattern for each use case.

Topic details

Key takeaway

Knowledge check

Model Access and Deployment Patterns: The same model can be reached through a web interface, an API, a desktop or mobile app, a browser extension, or an on-premise install. What is the central trade-off that distinguishes these patterns, and how should it guide a privacy-sensitive matter?

Show answer

Each deployment pattern trades convenience against data exposure, privacy, and operational overhead, for example a web interface is easy but sends data to the provider, while on-premise keeps data in-house at the cost of more setup and maintenance. For a privacy-sensitive matter you lean toward the pattern that minimizes data leaving your control, accepting the added operational burden.