As enterprises put AI to work, model quality is only part of the equation. The information AI can securely access matters just as much. Everlaw chief technology officer Max Christoff explains why.
What does AI need to deliver reliable answers in high-stakes legal work?
In regulated industries, AI is only useful if it can access trustworthy, relevant information. In legal work, that includes statutory law, case law, court decisions and regulations. In many matters, that also includes evidence. What is the data? Who said what? What records or documents support each claim?
But access to evidence or legal research alone is not enough. AI tools must be configured to ground their answers in these sources instead of relying on the model’s existing knowledge. They must trace every claim to a source and admit when they don’t have enough information to answer. Lawyers have a duty of care to know where an answer came from, examine the supporting evidence, and decide whether they can trust the conclusion.
What challenges arise when AI must work across millions of sensitive documents?
A legal matter can contain far more information than an AI model can analyse at once. A lawyer may need to search through millions of emails, messages, contracts and other files. Tools like Everlaw were built for massive scale and have been used on cases as large as 239 million documents—far larger than the context window of typical AI tools.
Evidence can span historic file formats, security-camera footage, scans of handwritten time cards and multiple languages.
Everlaw helps lawyers find relevant information across large case datasets. Deep Dive, our AI research tool for evidence, lets lawyers ask natural-language questions across an entire case dataset and get answers quickly: “Is there any evidence the CFO knew about the internal audit report prior to June?” Every answer is grounded in the case evidence, with citations to the relevant documents.
Why does the future of AI depend on open, connected tools?
First, context matters. Effective legal work depends on the flow of information between legal professionals, the evidentiary record, legal research and a firm’s prior work product. Just as a lawyer needs access to this information to produce high-quality work, AI needs to be part of that flow rather than sitting in an isolated platform.
When systems are closed and don’t interoperate, AI is either missing vital context or people are manually moving information between systems. This is inefficient and creates governance risks, including duplicated sensitive information, loss of data control and weaker security.
Second, tools should meet people where they want to work rather than force them into a different system. The Everlaw experience should be rich in features. At the same time, if a lawyer is drafting a brief in Word and wants to cite a fact from the evidence record, we should make that a seamless experience that doesn’t require downloading documents from one system in order to upload them to another. Governance should remain intact throughout. That is why Everlaw integrates with tools including Google Gemini, Microsoft Copilot, Anthropic Claude, Thomson Reuters CoCounsel, Harvey and Legora. The aim is to give legal professionals access to the most reliable, governed evidence source, no matter where they choose to work, using features such as Deep Dive, analytics or search.
What can enterprises learn from legal teams about using AI responsibly?
Responsible AI begins with the question: “What does context look like for your industry?” In legal work, that means understanding the source and relevance of information, who can access it and how it relates to the wider evidence.
At Everlaw, AI workflows remain connected to governed evidence. That means security, permissions and an audit trail remain in place as information moves between people and tools.
Other enterprises can take a similar approach by grounding AI in governed business data, giving users access to new capabilities without sacrificing control over the information those systems rely on.
Why is generative AI proving so valuable for legal work?
Generative AI can help legal teams work across more information than people could feasibly review on their own, extending their capabilities while allowing lawyers to focus on work that requires human judgement.
At the same time, many lawyers are still hesitant about the risks of adopting AI, which is why I take a utilitarian view of the technology. Rather than treating AI adoption as an either-or choice, organisations should ask: What are the benefits, what are the costs and do the benefits outweigh the risks?
That assessment is especially important in regulated industries, where AI’s practical value must be weighed against its security, safety and permissions requirements. Crucially, it comes down to context. I believe generative AI is most valuable when it expands what legal teams can accomplish without compromising their professional responsibilities.
Learn how Everlaw keeps every insight grounded in the evidence of your case.
Max Christoff is the Chief Technology Officer at Everlaw, where he leads the company’s strategic vision for innovation in the legal technology sector. With over 25 years of experience in engineering and product development, Max is focused on applying generative AI and large-scale systems to solve complex challenges within the legal industry.
As enterprises put AI to work, model quality is only part of the equation. The information AI can securely access matters just as much. Everlaw chief technology officer Max Christoff explains why.
Learn how Everlaw keeps every insight grounded in the evidence of your case.
Max Christoff is the Chief Technology Officer at Everlaw, where he leads the company’s strategic vision for innovation in the legal technology sector. With over 25 years of experience in engineering and product development, Max is focused on applying generative AI and large-scale systems to solve complex challenges within the legal industry.
