GC AI is a platform for in-house legal teams to research legal questions, review contracts, draft documents, and communicate legal guidance. More than 1,500 companies, including Zscaler, News Corp, and Nextdoor, use the platform. Claude powers the intelligence layer within GC AI's proprietary legal workflows, which combine company-specific playbooks, institutional knowledge, and a privileged setup made for in-house teams to generate research, contract summaries, issue spotting, and draft responses to internal legal questions.
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Stiamo aggiornando il nostro modello più avanzato. Il nuovo Claude Opus 4.6 migliora le capacità di programmazione del suo predecessore. Pianifica con maggiore attenzione, gestisce attività agentiche più a lungo e offre una finestra di contesto da 1 milione di token.
Stiamo aggiornando il nostro modello più avanzato. Il nuovo Claude Opus 4.6 migliora le capacità di programmazione del suo predecessore. Pianifica con maggiore attenzione, gestisce attività agentiche più a lungo e offre una finestra di contesto da 1 milione di token.
Stiamo aggiornando il nostro modello più avanzato. Il nuovo Claude Opus 4.6 migliora le capacità di programmazione del suo predecessore. Pianifica con maggiore attenzione, gestisce attività agentiche più a lungo e offre una finestra di contesto da 1 milione di token.
In-house legal teams at midsize and enterprise companies face a persistent ratio problem: each lawyer supports dozens of internal stakeholders, all with competing deadlines. Even straightforward questions can require hours of research and writing before the lawyer can provide a useful answer. At one company, a solo lawyer using GC AI supports $8 billion in revenue.
"Before using stronger models, legal AI systems required more manual prompting and editing to reach a useful result," said Cecilia Ziniti, Co-founder and CEO of GC AI. "The technology could assist with drafting and research, but the outputs often required significant cleanup before they were ready for use, which limited the practical value for busy legal teams. Combine that with the need to verify sources, and early AI models alone didn’t save time or yield more excellent results for most lawyers."
GC AI set out to close that gap. "We needed models that could produce clear, structured outputs that lawyers could review quickly and integrate into their work," Ziniti explained. "This is built on trust: trust in the process and sources, as well as minimizing hallucination and enabling the lawyers and legal teams in the loop to verify and work at their speed."
GC AI continuously evaluates leading models using real legal tasks across research questions, contract analysis, legal drafting, and structured explanations. The key measure is whether a lawyer can take the output and turn it into a final answer quickly.
GC AI uses Claude Sonnet as its primary model for customer-facing workflows for the model’s balance of performance, speed, and cost. Opus is reserved for more complex reasoning tasks. "Sonnet performed well on tasks that required clear explanations and structured reasoning," Ziniti said. "That made it easier to integrate into legal workflows where lawyers review the output, verify the reasoning, and refine the answer before sharing or actioning it internally."
The team found the strongest differences in contract analysis and clause interpretation. “When identifying nuanced obligations or deviations from standard terms in agreements, Claude was more consistent in both spotting the issue and clearly explaining the reasoning behind it,” Ziniti said.
Inside GC AI, lawyers typically start by bringing a legal question, issue, contract, or document into the platform. The system routes that input through governed workflows designed for common in-house tasks: contract review, regulatory research, and internal legal guidance. While GC AI is built on an agentic infrastructure, the focus is on reliable, operational outputs rather than open-ended model interaction. Unlike general-purpose AI tools, GC AI is able to embed a legal department's institutional knowledge on approved positions, risk tolerance, preferred language, and internal guidance into the workflows the team already uses, ensuring consistency whether they're reviewing a contract, drafting a document, or researching an issue.
A central design principle is verifiability. GC AI's Exact Quote feature links conclusions directly to source text, so lawyers can trace each finding to its origin. The platform also uses integrated search to surface primary and secondary legal materials, producing complete citations to specific statutes, regulations, and official guidance.
"Many of our users are working with dozens of documents exceeding 100,000 tokens each, quickly filling available context windows," Ziniti noted. To handle this, GC AI built a proprietary knowledge management system that ensures relevant documents, sections, and metadata are available at the right time for each attorney's workflow. This system manages the complexity of large document sets across matters, something that requires purpose-built infrastructure beyond what a general-purpose model can provide out of the box.
In addition to the web interface, GC AI integrates into the tools lawyers already use, including Microsoft Word and Google Drive, with a consistent intelligence layer that carries the lawyer's company-specific context, playbooks, and prior work product across every surface. Lawyers review the output, check the reasoning, and confirm the relevant legal sources before finalizing advice or documents.
"We can deliver legal AI workflows that lawyers actually use every day," Ziniti said. "Instead of treating AI as a separate experiment, GC AI embeds it directly into the way in-house lawyers complete their work."
In a structured survey of more than 100 active GC AI customers, in-house legal teams reported saving an average of 14 hours per week. That recovered time shifts how lawyers spend their days. "GC AI becomes their first stop when they encounter a legal question," Ziniti said. "Instead of starting with open-ended research or outside counsel calls, they begin by analyzing the issue in GC AI and then decide how to proceed."
The same survey found a 14% reduction in outside counsel spend and 21% greater accuracy compared to general-purpose AI tools without legal workflow structuring.
"Our legal team supports a global sales organization closing deals across dozens of countries,” Ziniti said. “Before GC AI, a single NDA redline cycle could take half a day between research, markup, and internal review. Now our attorneys run contracts through GC AI with our playbook loaded, and they get a first-pass redline with cited reasoning in minutes. We've been able to absorb a 30% increase in deal volume without adding costs to the legal team."
GC AI's architecture now supports coordinated multi-agent workflows, with specialized agents handling different parts of a task: retrieving relevant statutes, analyzing documents, and synthesizing findings into structured legal analysis.
As Claude's capabilities continue to advance, GC AI continues to leverage those improvements into deeper, more specialized legal workflows that a horizontal AI tool cannot replicate. “Our roadmap is focused on making the legal function operate at the speed of the business and driving more agency across all a company’s legal needs,” Ziniti said. “That means proactive legal operations and more strategic impact.”