CoCounsel

CoCounsel AI

CoCounsel AI

CoCounsel AI

CoCounsel AI

CoCounsel AI

CoCounsel AI

CoCounsel AI

CoCounsel AI

CoCounsel AI

CoCounsel AI

Legal: Claude + CoCounsel MCP Released

So this just happened today. All Anthropic needs now is the regional datacentres for both information at rest and inference, and we're off to the races. https://www.thomsonreuters.com/en/press-releases/2026/may/thomson-reuters-and-anthropic-expand-partnership-to-connect-claude-with-cocounsel-legal We all knew this was eventually going to happen but didn't think it would be so soon. My strategies just shifted for sure. submitted by /u/helraiser [link] [comments]

Using Claude Desktop + Code CLI as the core of a multi-model ER practice setup — curious who else has gone this deep

Background: I'm a former L&E management-side labor and employment defense attorney, turned in-house ER practitioner. Got frustrated with the limitations of any single AI tool for the kind of work we do — fact-gathering, documentation review, policy analysis, drafting PIPs and separation agreements, thinking through investigation strategy, etc. Over the past year, I've built a setup I think of as an "AI Council" — several models running in parallel, each assigned based on what it's actually good at. Perplexity handles real-time research, citation verification, and pre-decision fact checks. Grown to really love it over the past few months, and even started using Comet browser. Gemini and (more so) ChatGPT Plus are my strategy, analysis, and validation layer — long-context analysis, feasibility pressure-testing, and a check on whether my reasoning would survive scrutiny. Grok runs adversarial: it's my least favorite, but sometimes catches edge cases, hostile readings, and the arguments the other side will make. Anthropic's products are the hub — primary drafting, synthesis, and the final pass on anything that might end up in a file or a courtroom. That breaks into two surfaces: Claude Desktop for interactive work and Claude Code CLI for heavier, tool-driven execution — file operations, multi-step workflows, and anything that benefits from running against the actual repository rather than a pasted excerpt. Codex handles Windows-native scripting and automation on the back end, and cross-checks Claude's work. The whole stack is wired together through over a dozen MCP servers/Connectors/Extensions— Desktop Commander for controlled file writes, Filesystem for direct repository access, Google Drive and Gmail for organizational documents and correspondence, and Google Calendar for timeline reconstruction on investigations. NotebookLM sits on top of the document repository for source-grounded synthesis when I need to stay anchored to the record. Obsidian is the connective tissue, tied mostly to Claude's products — the knowledge base everything feeds into and draws from. I treat them less like individual tools and more like a panel of advisors running in parallel, with different members on point at different phases of the matter. One thing that's made a real difference: I've built out a local repository the AI can reference — org charts, reporting structures, personnel titles and manager relationships, employee characteristics relevant to ER patterns, investigation templates, policy libraries. So instead of re-explaining context every time, the models are working from a shared, structured picture of the organization. It's closer to how I'd brief a co-counsel than how most people describe using AI. For ER specifically, the biggest wins have been: Comparator and consistency pulls — before recommending discipline, having the AI surface similar past cases from the repository to flag disparate treatment risk before I'm standing in front of a plaintiff's attorney explaining it Pretext-proofing the record — checking whether the documented performance history actually supports the stated reason for action, not just whether the decision feels right Credibility framework structuring — in he-said/she-said investigations, using it to stress-test my witness weighting and surface what a hostile reviewer would attack in my findings Manager coaching in real time — drafting the actual words for difficult conversations (PIPs, termination, accommodation denials) so managers stop improvising their way into liability Intake triage and scope-setting — determining early whether a complaint warrants a formal investigation or a managed resolution, and what that decision's downstream exposure looks like Drafting that litigation-proofs itself — one AI drafts, another redlines for ambiguity, passive voice, and weasel language that wouldn't survive discovery Chilling effect and retaliation risk flags — identifying when a situation's timeline creates a proximity problem before the next adverse action goes through Hasn't replaced judgment — still very much human-in-the-loop, and the attorney background makes me *very* paranoid about accuracy in ways that probably help here. But it's fundamentally changed how I work. Curious if anyone else has built something similar, or is using AI in ER at all. submitted by /u/TheLawIsSacred [link] [comments]

https://t.co/ha9aNCSnhU From #SerbiaFloods

https://t.co/ha9aNCSnhU From #SerbiaFloods