Technical Pre-Sales Engineer

Identity crises? The rumour that AI companies are bulk buying books and destroying them so that their content can be scanned in to train their AI models has been verified twice: by court documents in California and by journalists at 404 Media who hid an Airtag in a rare book sold to a bulk purchaser. The book was tracked to an Amazon facility near Las Vegas, where books are dismantled, scanned and discarded. Amazon confirmed that it uses older books to develop and improve its products, i.e. for AI training, not for reading. Following 404 Media's report, book database ISBNdb took down the web page which was advertising a printed books sourcing service for LLM dataset needs. But booksellers worldwide are still seeing suspiciously large orders for second-hand books. As Kathryn James explained in The Guardian, this is to get around the permissions needed to reuse digital content and to provide clean training data from books published before the launch of ChatGPT in 2022 which are less likely to contain AI-generated material. Source code Ironically, given that the scale of Anthropic's 'Project Panama' which involved similar destructive scanning of pre-2022 books to train Claude only emerged from unsealed court filings in relation to the northern California district court copyright case Bartz v Anthropic which was finalised in late July, August saw the introduction of Claude Text Watermarking. How does this work? Claude embeds statistical text watermarks during autoregressive token generation so that the watermark is deeply embedded in the text and doesn't disappear if you copy and paste. However, it is relatively straightforward to get around watermarked content: you can run the text through another process, like translation or summarisation, or if it's not a massive document, you can simply retype it! But obviously there are risks involved, especially in legal, where there have been high profile instances of lawyers and litigants using unedited AI content. Legal-specific models One solution is a legal specific GenAI model. Last week Harvey released a research preview of Harvey Tenet, which is built on Chinese open-weight model Kimi K3 and post-trained on legal data and practice. As Ryan McDonough, head of software engineering at KPMG Law explained on LinkedIn "The model gets matter documents, tools and realistic legal tasks with its work assessed against detailed expert rubrics... to encode how legal work should be performed, not simply what the law says." Playbooks have been around for a while, and this new model usefully incorporates a playbook for legal work. McDonough also highlighted Harvey's intelligence per token benchmark. Thomson Reuters launched Thomson which is based on Chinese OSS model Qwen and trained on its own data, making CoCounsel's Tabular Analysis more cost-effective. Mike OSS founder Will Chen commented on LinkedIn, "The point is not about having the best model in the market that is competitive with the frontier but a good enough model for some legal workflows. The reality is that it's more of a cost control measure." He continued: "I'm sure Thomson is going to be more than enough for summarisation and will help TR save some money on tokens," highlighting the significance of large legal AI companies shifting to Chinese OSS models. And turning to frontier models, Google has joined Microsoft, Anthropic, OpenAI etc and launched Gemini Enterprise for Legal, recruiting Tony Ensinger from DeepJudge to head its legal AI division. Gemini Enterprise for Legal includes purpose-built skills, partner agents and model context protocol (MCP) integration with legal AI platforms, including Harvey, Legora, and Thomson Reuters. Digital twins and avatars Twin1 AI, a start-up building digital AI twins for lawyers, helps firms preserve lawyers' tacit knowledge and boost efficiency by automating routine communications in a personalised way. Lawyers can address the risk of oversharing (which is a concern given GenAI's tendency to be verbose) by managing their digital twin's privacy/permissions/review settings across multiple channels. While digital twins have been around for a while, Twin1 takes the concept further with deep personalisation and (the option of) setting up a Twin Network where digital twins can communicate and coordinate with each other and lawyers across the firm's teams and systems. In education, the AI debate is increasingly polarised. While some universities are strongly against students using AI, with a few banning its use altogether, others are using it to create and deliver personalised e-learning programmes. Harvard has taken this in a new direction, introducing AI avatar tutorials. TechCrunch reported that Harvard's $699 HBS Foundry startup bootcamp follows up live sessions with instructors with individual feedback on practice pitches and board meetings from AI avatars of its instructors created by HeyGen. This is unusual as educational institutions tend to opt for AI to deliver group/general sessions/e-learning on demand and human tutors to provide personalised feedback and support. While digital twins and avatars save time, they are of course replacing human interaction, which doesn't matter when they are answering repetitive questions or managing routine tasks, but they also risk limiting the personal connection that often underpins business and client relationships. August announcements included more partnerships and MCP integrations between legal AI vendors. Given last month's headlines about rogue AI agents, there are surely questions around whether multiple integrations, semi-autonomous agents, including digital twins and avatars, etc make it easier for sophisticated AI models to find pathways into law firm systems and client data. Planning for an AI future Bill Gates highlighted the potential for AI cyberattacks when he wrote last week that the choices Legal Geek make now are critical to ensure that AI is a force for good. He urged governments and organisations to prepare now for the AI era. For example on AI replacing jobs, Gates recommends designating human-reserved jobs and taxing robots and AI tokens because employing people carries tax obligations while technology is treated as a business investment/expense. "The tax system nudges you toward replacing people with machines," he wrote. In anticipation of a legal AI future, both Harvey and Legora are in talks to raise significant additional funding. Automating AI slop! Finally, on 30 July, LinkedIn added a 'seems like AI slop' button where you can notify the platform if you suspect posts have been AI generated (which is interesting as it previously offered AI assistance to 'enhance your post'). Meanwhile, one (not entirely serious) website offers a quick way to add more AI slop to a platform whose brand has been significantly contaminated by GenAI. The viral LinkedIn CringeBot 3000 helps you "become a thought leader in seconds"!

DeepJudge and Legora partner to bring institutional intelligence to collaborative legal work. Bidirectional integration brings institutional knowledge to collaborative legal workflows. ZURICH, SWITZERLAND, August 18, 2026 / EINPresswire.com / - DeepJudge, the institutional intelligence platform for law, and Legora, the agentic operating system for legal work, today announced a partnership that brings an organization's knowledge, precedent, and expertise to the AI-powered workflows where legal work happens. New work product in Legora is carried back into DeepJudge. Law firms and in-house legal teams have decades of precedent, work product, drafting standards, and institutional judgment that reflect how they practice. That accumulated knowledge is unique to each organization and becomes increasingly important as AI takes on a greater role in legal work. DeepJudge turns a legal organization's collective experience into governed institutional intelligence that lawyers and AI agents can apply to new work. This integration brings that intelligence to Legora's users, and creates a continuous cycle between what an organization knows and the work it produces. - From knowledge to work product: Lawyers can search and analyze their organization's collective knowledge in DeepJudge and send the selected documents directly to Legora for negotiation, review, and drafting, from either the Legora or DeepJudge platforms. - Intelligence inside agentic workflows: Legora automatically retrieves relevant precedent and prior work through DeepJudge. Lawyers can carry the context of their task into DeepJudge whenever they want to search deeper through DeepJudge's recently announced Agent Handoff Protocol, which preserves context as work moves between AI environments. - One search across everything: At the organization's direction, DeepJudge can index work created in Legora and documents stored in Legora projects, alongside their other proprietary knowledge sources, to create a single, unified enterprise search. For organizations already using DeepJudge, that means the knowledge they have accumulated can be easily accessed and applied to their work in Legora: the same governed index and the same permissions, now powering agentic legal research, drafting, review, due diligence, and more. Legora's outputs will cite the source document across all these sources, enabling users to easily verify their work. "Every legal organization now has access to powerful AI. What they don't share is the judgment they've built through years of matters, negotiations, and decisions," said Paulina Grnarova, Co-Founder and CEO of DeepJudge. "That institutional intelligence is their advantage. Our partnership with Legora puts it directly into the flow of legal work, so lawyers and AI agents can draw on relevant knowledge from the entirety of the organization's own experience when it matters." As legal work continues in Legora, new work product can flow back into DeepJudge, becoming part of the institutional intelligence available for future matters. Past work informs what comes next, and new work adds to the organization's experience, creating a continuously richer source of intelligence for lawyers and AI agents to draw on. "I have always said that the right context is what makes legal AI genuinely useful," said Andrei Salajan, Director of Legal Tech & Innovation at Schoenherr. "DeepJudge enables our teams to access the full depth of our institutional knowledge, and that is what lets us deliver exceptional results for our clients. This integration brings our lawyers' judgement and precedent directly into review, drafting and analysis in Legora. We are working at the frontier of this shift, where agents take on far bigger tasks across longer context. That makes context quality the constraint. The know-how we encode and connect into those systems is the layer no one else can replicate." "As AI takes on more of legal work, context becomes increasingly critical, and DeepJudge makes a firm's institutional judgment available at scale," said Max Junestrand, Co-Founder and CEO of Legora. "Bringing that intelligence into Legora means every draft, review, and analysis can draw on what is unique to that firm: the precedent, experience, and judgment it has built over years of practice." Together, DeepJudge and Legora are bringing institutional intelligence into the collaborative flow of legal work, so lawyers and AI agents can build on the organization's expertise - and on each other's work. About DeepJudge DeepJudge is the core AI platform for legal professionals to harness their institutional intelligence. DeepJudge transforms institutional knowledge into structured, governed intelligence that enables legal teams to find and analyze information, uncover insights across matters, and apply what their organization already knows to new work. DeepJudge is trusted by leading law firms and in-house legal teams globally, including Holland & Knight, Greenberg Traurig, ArentFox Schiff, Cozen O'Connor, CMS, and Homburger, among others. To learn more, visit www.deepjudge.ai. About Legora Legora is the agentic operating system for legal work, supporting lawyers in research, review, and drafting across complex matters. It is used by more than 100,000 legal professionals at more than 1,500 leading law firms and in-house legal teams across over 50 markets. Legal Disclaimer: EIN Presswire provides this news content "as is" without warranty of any kind. Australian News Journal do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.

DeepJudge introduces Agent Handoff Protocol, with Harvey and Thomson Reuters among first to adopt. Open protocol enables users to move between AI platforms without starting fresh, carrying the context needed to continue their work. ZURICH, SWITZERLAND, August 13, 2026 / EINPresswire.com / - DeepJudge, the institutional intelligence platform for legal AI, today introduced Agent Handoff Protocol, an open protocol designed to enable context continuity across AI systems. Increasingly, people are moving between different AI products and experiences to complete their work. While existing protocols like the Model Context Protocol (MCP) enable systems to exchange information and perform specific actions, the broader context required to continue a task - including objectives, supporting materials, conversation history, and prior work - is often lost in the process. The Agent Handoff Protocol addresses this challenge by enabling AI platforms to transfer the user and the context needed to continue work from one system to another. Rather than rebuilding context each time they switch products, users can move between AI experiences while preserving the information and progress that led to the current point. For example, a lawyer researching a precedent question in DeepJudge can move into Harvey with the relevant context already in place, allowing the work to continue without re-uploading materials, recreating prompts, or rebuilding prior analysis. "No single AI experience will be the right one for every task," said Yannic Kilcher, CTO and Co-Founder of DeepJudge. "People should be able to use the AI tool that's best for what they need to do and take their context with them. That is the experience we built Agent Handoff Protocol to enable." DeepJudge developed Agent Handoff Protocol and is releasing it as an open protocol. Harvey, the leading legal operating system for legal and professional services, has announced an integration implementing Agent Handoff Protocol that is entering beta this month. Thomson Reuters is also supporting Agent Handoff Protocol, with additional details on their implementation and timing to follow in future announcements. Joel Hron, Chief Technology Officer of Thomson Reuters said, "The future of professional AI will not be defined by a single model, agent or application. Professionals will increasingly work across specialized AI experiences, and those systems need to work together without sacrificing the context, trust, and expertise required for high-stakes work. Open standards like Agent Handoff Protocol can help enable that interoperability, which is why Thomson Reuters is excited to support this effort and help shape what comes next." "The most capable legal workflows will draw on more than one specialized system. Agent Handoff Protocol lets a user move from Harvey into a partner's product and back without losing the thread of their work, while leveraging both products to their full potential. We're glad to be among the first to implement it," said Siva Gurumurthy, Chief Technology Officer at Harvey. As the institutional intelligence platform for legal work, DeepJudge is positioned to offer Agent Handoff Protocol as a shared resource rather than a proprietary advantage. The company invites other AI providers, specialist platforms, and law firms building AI solutions in-house to adopt the protocol and help shape its ongoing development. The Agent Handoff Protocol specification and implementation resources are available on GitHub. About DeepJudge DeepJudge is the core AI platform for legal professionals to harness their institutional intelligence. DeepJudge transforms institutional knowledge into structured, governed intelligence that enables legal teams to find and analyze information, uncover insights across matters, and apply what their organization already knows to new work. DeepJudge is trusted by leading law firms and in-house legal teams globally, including Holland & Knight, Cozen O'Connor, ArentFox Schiff, CMS, and Homburger, among others. To learn more, visit www.deepjudge.ai. Legal Disclaimer: EIN Presswire provides this news content "as is" without warranty of any kind. Australian News Journal do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.