u/manuayala — reddlx

Claude for Legal: I think the vendor pitch is about to get annoying

This might be a bit long because i’m still thinking it through, but i’ll try to keep it tight.

i’ve been watching the Claude for Legal launch and wanted to sanity check my read here.

Everyone is understandably comparing it to Harvey, CoCounsel, Legora, Westlaw, etc. makes sense. that’s the obvious legaltech angle.

but i’m not sure the important part is “Claude is now for lawyers.”

Feels more like legal is just the latest vertical Anthropic is plugging into a bigger infrastructure strategy.

a week before the legal launch, they pushed a similar thing into finance. agents/templates/workflows for pitchbooks, audits, credit memos, financial analysis, all that kind of stuff.

Then small business. QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365.

Now legal. Westlaw, CoCounsel, Harvey, DocuSign, Box, Everlaw, Microsoft 365, legal plugins, MCP connectors.

and at the same time you have Notion turning the workspace into a place where agents, data, workers and humans can operate together. Microsoft putting Copilot where work already happens. Salesforce pushing agents into the customer operating system.

So to me the pattern is less “AI company launches legal tool” and more “get the model closer to the actual workflow.”

the boring historical comparison in my head is Word.

Lawyers didn’t stay in Word because Word is beautiful. they stayed because the work lived there. drafts, comments, redlines, templates, partner edits, client versions, final_final_v7, all of it.

once the work lives somewhere, everything around the work adapts to that place.

That’s why i think people sometimes underestimate these connector/MCP moves. not because MCP-ready is magic. it’s not. but if models can call tools, pull context, trigger actions and sit inside the systems where the work already happens, then the model is only part of the story.

the real question becomes: who owns the workflow layer before the lawyer even sees the work?

For legal, that means Word, Outlook, DMS, CLM, DocuSign, intake, research, matter updates, e-discovery, billing, client systems, whatever people already live in.

and this is where i think the next vendor wave gets annoying.

A year ago everything was AI-powered. then everything was a copilot. then everything was an agent. now i think we’re going to get the same thing with Claude/MCP/connectors.

built on Claude. MCP-ready. agent-native. workflow orchestration. connects to every model. plugs into your existing tools.

Some of that will be legit. some of it will be reheated wrapper language.

the questions i’d ask are way more boring:

what workflow does this actually improve?
where does human review happen?
what does the audit trail show?
what system of record does it touch?
what happens when it’s wrong?
what breaks if the vendor disappears?
does the connector actually change the workflow, or is it just a nicer entry point into the same product?

i’m not anti-vendor (maybe i am). some vendors are going to be very valuable here, especially if they own real workflow, review, implementation, auditability, or domain-specific data.

but i do think Claude-powered or MCP-ready is a bad starting point for buying anything.

better starting point is probably: what work are we trying to make easier, safer, faster, or more auditable?

then figure out if the answer is a vendor, an internal workflow, a connector, a model, or some boring mix of all of it.

am i overreading this?

are firms actually starting to think in terms of workflow ownership, or is the buying conversation still mostly “which AI tool should we get?”

btw i did a waaay longer version of this for the blue social media but waay too long for here, can share it in comments if wanted.

reddit.com

u/manuayala — 7 days ago

▲ 17 r/legaltech

Been following BigLaw AI deployments pretty closely, and Freshfields seems to be taking a different route from most of the market.

On April 15, they described their Google Cloud partnership as “no longer an experiment. It is infrastructure.”

The reported numbers were pretty notable:

5,000+ lawyers on Gemini
2,800 Workspace seats
2,100 NotebookLM Enterprise daily users

Then, on April 23, they added Anthropic Claude across all 33 offices.

Multi-year deal. Reported 500% adoption growth in the first six weeks.

What I find interesting is that this does not look like a simple “Google vs Claude” story.

It looks more like Freshfields is treating the model layer as interchangeable, while trying to own the application and governance layer through Freshfields Lab, internal AI Champions, and firmwide governance.

That feels different from most of the BigLaw deployment patterns so far.

CMS, DLA Piper, Latham, A&O Shearman: Harvey-heavy.

Clifford Chance: Microsoft / Azure OpenAI.

Reed Smith: internal build through Gravity Stack.

Freshfields: multi-LLM plus owned application layer.

At the same time, the Sullivan & Cromwell hallucination issue feels like a reminder that written AI policies alone are not enough.

If 40 AI hallucinations can make it into a Chapter 15 motion at an Am Law top 10 firm, the real question probably is not “which tool did they use?”

It is: what verification layer existed between AI output and filed work?

Curious how people here see this:

Is the multi-LLM approach actually sustainable, or does it become expensive complexity?
For mid-size firms that cannot build a Freshfields Lab, what is the realistic version of “owning the application layer”?
If Harvey gets deeper into Microsoft 365 and Copilot environments, do Microsoft-standardized firms effectively inherit parts of the Harvey ecosystem whether they planned to or not?

Would especially love to hear from people implementing this inside firms, not just evaluating vendors.

reddit.com

u/manuayala — 21 days ago

▲ 3 r/SaaS

Two weeks ago, I posted a Week 1 Update about reviving a dead domain to see if strict programmatic architecture and FAQ schema could trigger AI citations faster than traditional SEO.

Just for context, I haven't done SEO before other than just writing blog pieces when I first started working as a freelancer, but it's not the same optimizing article pieces back in 2010-2014 than actually optimizing sites and getting them ranked. So I've been basically speedrunning my knowledge on it and what's widely accepted as AEO, tho it's been called GEO and LSEO too.

Anyways, week 1 of my project ended with 43 Copilot citations. Today, I'm at 2,400+ citations and 7,130 Google impressions in the last 24hs.

To be completely honest, I am figuring a lot of this out as I go. I’m a builder, and distribution has always been my bottleneck. Over the last 3 weeks, I’ve run this experiment across 4 different domains, screwed up, learned, and refined it into a phased system.

Here are the hard numbers at Week 3, the exact phases I’m running, and a crazy data-tracking metric I stumbled onto by accident today.

The Hard Numbers (The Original Test Domain)

I scaled the original domain from 370 pages to 1,000+ pages. Google finally caught up to Bing's index speed, and the engines are compounding, I went from about 1.5k impressions in GSC to around 7k in the last 24hs for two straight days.

Microsoft Copilot Citations: 2,400+ total citations across Word, Outlook, and Teams (up from 43 in Week 1) . Receipts.
Google Search Console: 7,130 impressions in the last 24 hours (21k in the last 7 days) Receipts.
Traffic: Over 620 active users in the last 30 days Receipts.

The "Accidental" Discovery: Tracking Live AI Fetches

One thing I've noticed were some weird discrepancies between GA4 and my Vercel analytics. I was worried about spam scrapers eating my bandwidth or diluting my data, so I dug into the Vercel Firewall logs for the first time. I didn't even know this specific feature existed until today.

I found a goldmine. In a single 8-hour window this evening, the firewall logged the following real-time bot fetches:

ChatGPT-User/1.0: 398 hits
Perplexity (Bot/User): 114 hits
Microsoft Corporation (Azure host for OpenAI): 400 hits

Because ChatGPT-User only fires when a human actively prompts ChatGPT and it needs to search the live web, this means ChatGPT fetched my pages nearly 400 times in just a few hours to answer users' live questions. Receipts

The Playbook: How I built this (The Phases)

This didn't happen by just spamming 1,000 pages on day one. I rolled this out in stages across 4 different projects to isolate what works. Total footprint is now 5,000+ pages.

Phase 1 (The Test): I launched the initial test on Domain 1. Then I replicated it on 3 other domains (different niches, different languages, different content strategies).
Phase 1.5 (The Scale): Project #2 indexed faster than Domain 1. Project #3 did even better. Project #4 was a massive performer right out of the gate (launched last week, and hit 900 impressions and 20+ clicks yesterday). Once I knew the architecture worked, I came back to Domain 1 and scaled it to 1,000+ pages. Receipts 1 and Receipts 2.
Phase 2 (The Audit & The Cron Job): Yesterday, I ran a deep audit across all 4 sites. To solve Google's notoriously slow programmatic indexing, I learned how to set up automated cron jobs via my terminal to push up to 200 URLs a day directly to Google's Indexing API for free while I step away from the keyboard.
Phase 2.5 (The "Pick & Roll"): Launched this today. I'm combining hot/trending topics in my niche with my proven evergreen structure to fill content gaps. I literally have a terminal script pushing 150 new pages live as I write this post.

The Reality of AEO (Zero-Click & Dark Social)

The architecture is working, but here is the reality of Answer Engine Optimization: Despite nearly 400 live ChatGPT fetches today, GA4 shows only 1 traditional click-through from chatgpt.com.

However, I am getting highly qualified clicks directly from inc-word-edit.officeapps.live.com (Microsoft Word web). Copilot is citing me inside users' Word documents, and they are actively clicking through. I’m also getting traffic through corporate emailprotection.link firewalls, meaning people are finding the data via AI and emailing it internally to colleagues (Dark Social).

What’s Next (Phase 3: Distribution & Monetization)

Right now, the site is purely reactive. I haven't built complex funnels because I refused to waste time on capture mechanics until I solved the traffic/citation problem first.

But Phase 3 starts this week. I need to actively monetize this traffic. I'm building tools (launching a SaaS one this week) and Chrome extensions to try and capture this highly specific intent.

I'm going to increase my intent on getting people to sign up to a newsletter as well besides LinkedIn (which btw benefits from this a lot, and the website benefits from LinkedIn as well. I went from 30 followers to close to 700 in the span of 6 weeks plus 260 subscribers to my newsletter, tho I'm not attributing this directly to the work showed here since this started afterwards, and it was even a way to not dilute my voice in LinkedIn).

Two questions for the sub:

Since AI search is largely "Zero-Click", has anyone successfully tested injecting their brand name into FAQ schema so the LLM outputs your brand directly in the chat response?
Has anyone found a reliable way to map bottom-of-funnel conversions back to these "Zero-Click" LLM citations, or does it all just bleed into "Direct" traffic?

Happy to share the exact programmatic architecture, Q&A hooks, or how I set up the terminal cron jobs for Google indexing if people want to dig into the technicals.

BTW IM NOT INTERESTED IN VENDORS TRYING TO SELL ME "How to write articles fast with this cool AI tool" I CAN FIGURE THAT AND OTHER STUFF, NOT INTERESTED IN YOUR SAAS BUDDY. I'm here just trying to provide value to the sub and hopefully learn from those who are doing something similar.

u/manuayala — 22 days ago