This post is the sixth in a series about how to implement legal AI that knows your law firm. In the series, we cover the differences between LLMs and search, the elements that make a good search engine, the building blocks of agentic systems (e.g. RAG), and how to implement a system that is fully scalable, secure, and respects your firm’s unique policies and practices.
Enterprise search is the critical link between all your firm’s data and your AI-driven workflows
What would it mean to have an AI platform that “knows” all the institutional knowledge of your firm? It would need to have real-time access to all the firm’s data; it would need to be scalable; it would need to be able to power a variety of legal workflows as needs arise. A search engine with access to all the firm’s data—fully up-to-date regardless of where it is stored or generated in the firm’s systems—is what enables an AI platform to become an extension of all that stored knowledge.
We’ve already shown many ways an enterprise search system is needed: A good enterprise search has the ability to find and understand relationships in data from across multiple systems, provide a consistent view of all that data, find similar concepts as well as important keywords, identify duplicates and establish connections in metadata (Part 2). It comprises a secure and compliant knowledge layer, accessing all that data without compromising security and confidentiality (Part 3). Not having to move documents to new containers to leverage it for AI is a key element of that security piece.
When the search index is robust and permission-aware, it becomes a foundation for AI, enabling any workflow, agent, or application to draw on the same holistic and authoritative knowledge source without duplicating data or introducing governance risk. Such a system can be “always on,” by which we mean instantly available to your AI without adding additional retrieval and compliance steps that slow down workflows and introduce risk.
In fact, enterprise search is the only way to get to this result.
Any AI platform that doesn't rely on a firm-wide, real time knowledge search index is by definition incapable of knowing everything your firm knows. The alternatives all rely on people or systems that have to manually or intermittently find, curate and present the AI platform with data that might be incomplete, out-of-date, or divorced from critical contexts. Tempting as it may be to rely on existing curation, profiling, or manual processes, or easy as it might be to rely on source systems search and federate the results, these all suffer from the same problem—an incomplete and potentially inaccurate picture of the firm's knowledge. In short, an integrated search layer is not just a technical enabler, but a prerequisite for coherent and scalable AI adoption.
An enterprise-search based AI platform has additional benefits:
- Restores strategic control: firms always know where their data is, who can access it, and how it’s being used.
- Replaces a disjointed tool stack with consistent, auditable AI capabilities grounded in the firm’s whole body of work.
- Requires systems in the stack to have application programmer interfaces (APIs) or other secure integration methods with the search system and/or each other.
- Relies on adherence to good data governance practices.
- Allows firm data to remain in source systems (e.g., document management, practice management, experience tracking), each serving its intended function.
- Depends on a robust enterprise search layer that operates across all data in real time.
- Ensures the AI platform has the knowledge it needs to generate answers that reflect the firm’s true expertise.
Finally, a search-centric AI platform helps to future-proof a firm’s investment in documents and data systems, precisely because it doesn't require migrating the data. A firm can swap out internal systems, or keep existing systems while adding new ones, without having to rebuild all or part of the AI platform. The search layer keeps the river of input data flowing to the AI platform, regardless of which combination of tributaries deliver the data to the flow. This also provides benefits on the output side—agents and workflows built on this modular approach can be model-agnostic, and workflows can still function even if the firm implements new or modified internal systems.
Explore the blog series “Legal AI That Knows Your Firm”
Posts in this series:
- The Allure (and Danger) of Using Standalone LLMs for Search
- Why Retrieval Augmented Generation (RAG) Matters
- All Search Engines Are Not Created Equal
- Why good legal search is informed by the entire context of your institutional knowledge—not siloed or “federated”
- How can your AI securely use all of your firm’s data?
- Why an “always on” search engine is a prerequisite for scalable AI adoption (this post)
- How do you build generative AI tools that are tuned to your real-world legal processes? (Coming soon)
- As the variety of tasks automated by AI agents proliferate, how does a firm manage it all? (Coming soon)
- How do I adapt workflow agents to the specific needs of my firm? (Coming soon)
- Does your AI platform set your firm apart from the competition? (Coming soon)
This post was adapted from our forthcoming 24-page white paper entitled "Implementing AI That Knows Your Firm: A Practical Guide." Sign up for our email list to be notified when the guide is available for download.