Large language models have transformed the legal profession. Ask a question, get a fluent and confident answer. Draft faster. Summarize quicker. Explore ideas that once took hours in minutes.
For law firms though, a hard truth remains: all generic AI can perform the above tasks, but none can know what your firm knows.
Leaders in legal innovation like Kristina Bakardjiev, Director of Legal Practice Innovation at Cozen O’Connor, see this gap clearly. The promise of AI is real, but firm-specific context is essential to the value it offers.
“We’re less focused on individual tools and more focused on building a future-proof AI strategy tied to good data—not tied to one model or one use case.” Kristina Bakardjiev, Director of Legal Practice Innovation, Cozen O’Connor
Why Firm-Wide Context Drives Legal Judgment
Legal judgment depends on more than language. It depends on understanding how past work was negotiated, why certain positions were taken, which risks were accepted or rejected, and how those decisions varied by client, counterparty, or practice group. When AI can’t see that history, it doesn’t just lack information—it lacks perspective.
Lawyers ask deeply contextual questions:
- Have we negotiated this before in a stock purchase in the range of $X-Y when representing the buyer in [pick an industry]?
- What position has the opposing counsel taken in the past?
- How have we defended this type of claim, given XYZ facts?
- Which partners have handled similar matters?
- What have judges ruled when faced with this fact pattern?
Those answers cannot be found in public internet information underlying LLMs. They live across document management systems, SharePoint sites, billing systems, internal knowledge bases, emails, and dozens of other disconnected tools within the firm.
Firm-specific context is what actually creates value. Kristina recognizes that in legal AI, if the right knowledge can't be surfaced at the right moment, everything downstream breaks, no matter how powerful the language model actually is. Further, AI may produce answers that sound correct, but reflect only a partial view of the firm’s experience.
This is important because in legal work, partial context can quietly distort judgment, resulting in flattening nuance, overlooking precedent that matters, or treating firm-specific practice as interchangeable with market norms.
Manual curation can help but it can’t close the gap. Even the best-maintained knowledge bases capture only a fraction of a firm’s lived experience, and they inevitably lag behind active matters, evolving client preferences, and the way lawyers actually work. Asking lawyers or KM teams to continuously pre-select, tag, or sanitize “gold-standard” materials shifts the burden upstream, without solving the core challenge of comprehensive, real-time retrieval.
A Different Starting Point: Enterprise Search as the Foundation
As Kristina puts it, in legal AI, retrieval is what actually matters more than anything else. Rather than chasing one-off tools or narrow use cases, Cozen O’Connor focused on something more durable: building a future-proof AI strategy grounded in the firm’s own data.
That meant starting with a question most AI platforms often don’t prioritize: What if a firm could access all of its internal knowledge using a single search platform, without moving data, breaking permissions, or re-creating governance from scratch?
Beyond unifying and indexing internal systems, DeepJudge’s enterprise search understands content, context, relevance, and institutional knowledge, providing a complete view of the firm’s prior work that AI workflows can rely on. The result is one shared AI platform that practice groups can build on while fully preserving existing access controls, security, and data governance.
For Kristina, this mattered because it aligned with what lawyers consistently ask for: AI that knows what the firm knows.
Building on Search to Inform Strategy: A Practical RFP Example
Beyond unlocking efficiency at Cozen O’Connor, this approach enables AI-powered workflows that directly support firm strategy and business development.
One example is RFPs, where speed matters, the work is non-billable, and firm experience is a key differentiator. By grounding responses in a firm’s own prior work, AI workflows can dramatically reduce turnaround time while preserving accuracy and credibility.
DeepJudge supports purpose-built workflows that help firms respond to RFPs by drawing directly on their institutional knowledge, without relying on extensive prompting or manual collection across teams.
What once required extensive manual coordination can now be turned around dramatically faster, without sacrificing confidence in what’s being submitted.
It’s a clear illustration of what becomes possible when search and execution are finally connected.
From Judgment to Architecture: Cozen O’Connor’s Approach to Legal AI
Over time, every firm will have access to the same public information, the same AI models, and the same baseline AI capabilities.
What will continue to differentiate firms is context: the institutional knowledge they’ve built over decades, the depth of specialty expertise within practice groups, and the ability to apply that experience precisely to each client and matter.
At Cozen O’Connor, this understanding has shaped how the firm approaches legal AI. Rather than optimizing for isolated tools or short-term use cases, the firm focused on building an AI strategy that is anchored in its own knowledge and reflective of how its lawyers actually practice.
For Kristina, that meant starting with a simple but demanding requirement: AI must reliably surface what the firm already knows, across matters, clients, and practice groups, without compromising governance or security. The technology had to adapt to the firm—not the other way around.
That perspective ultimately led Cozen O’Connor to DeepJudge, whose enterprise-search-first foundation makes it possible to surface the right knowledge at the right moment and use it consistently across workflows.
In Kristina’s words, the legal industry is in a race for context. Better data leads to better results, for lawyers and for clients.
That race doesn’t start with different models or prompts.
It starts with retrieval.
See this working on real firm data
Explore how firm-wide retrieval supports real legal workflows—without moving data or reworking governance.