You are paying several hundred Euros a month for legal research tools designed before AI existed. And you are still doing the research manually!
Most legal research workflows today look like this:
- Search a database
- Read through the results
- Synthesise the relevant points yourself
- Repeat for the next issue
The platforms got better interfaces. Some added AI overlays. The core workflow stayed the same.
Gen AI is now enabling something different: tools that complete research tasks, not just assist with them. Agentic workflows that move through a legal question autonomously, surface relevant material, and return structured answers.
Both Westlaw and LexisNexis launched agentic products in early 2026. So did a wave of startups, without the legacy subscription costs attached.
The disruption question for legal research is no longer "will AI replace lawyers?" That debate is over.
The real question is whether the platforms lawyers have trusted for 40 years will still be the ones running the workflows in five.
If you are evaluating legal research tools right now, what matters most: depth of content, quality of AI outputs, or cost per hour saved?
Die meisten Kanzleien diskutieren noch, welches KI-Tool das richtige ist.
Freshfields hat darauf eine andere Antwort: alle!
Die mehrjährige Partnerschaft mit Anthropic, bekanntgegeben am 23. April 2026, folgt auf die frühere strategische Zusammenarbeit mit Google. Beide ergänzen die eigene proprietäre KI-Plattform der Kanzlei, die vom Freshfields Lab entwickelt wurde.
Der klassische Reflex lautet: das beste Tool finden, standardisieren, alle schulen. Das wirkt übersichtlich. Es ist aber auch eine Wette darauf, dass sich der KI-Markt bald stabilisiert.
Freshfields geht diesen Weg nicht. Stattdessen verfolgt die Kanzlei einen Mehranbieter-Ansatz:
*️⃣ Claude wird für 5.700 Mitarbeiterinnen und Mitarbeiter in 33 Büros weltweit eingeführt.
*️⃣ Früher Zugang zu künftigen Anthropic-Modellen ermöglicht Tests und strukturiertes Feedback.
*️⃣ Mehrere führende KI-Anbieter laufen parallel.
*️⃣ Eine eigene interne Plattform liegt über allen externen Tools.
Der Wert dieses Modells liegt nicht darin, den Gewinner zu wählen. Er liegt darin, nah an der technologischen Grenze des Machbaren zu bleiben, unabhängig davon, wer sich am Ende durchsetzt.
Für die meisten Kanzleien ist dieser Umfang an Infrastrukturinvestitionen nicht realistisch. Aber die Grundlogik gilt in jeder Größenordnung: Tiefe Integration mit einem einzigen Anbieter ist heute bequem und morgen womöglich eine Abhängigkeit.
Genau deshalb kannst du in e! by Lexemo bei der Automatisierung deiner Prozesse aus einer Reihe von LLMs wählen, die am besten zu deiner Lösung passen.
Denkt eure Kanzlei bei der KI-Strategie noch in Kategorien der Einzeltool-Einführung, oder verfolgt ihr bereits bewusst einen Mehranbieter-Ansatz?
Ihre Kanzlei setzt KI-Tools ein. Ihr Team erstellt jede Woche Dokumente, Recherchen und Mandantenkommunikation damit.
Ab dem 2. August 2026 gilt Artikel 50 des EU AI Acts. Und er sagt nicht, was viele gerade annehmen.
Die Kennzeichnungspflicht trifft nicht automatisch jedes KI-generierte Dokument Ihrer Kanzlei. Sie gilt vor allem für Deepfakes, synthetische Medien sowie KI-generierte Texte zu öffentlichkeitsrelevanten Themen, die nicht von einem Menschen geprüft wurden.
Wer als Anwalt einen KI-generierten Entwurf prüft und redaktionelle Verantwortung übernimmt, ist in der Regel nicht kennzeichnungspflichtig.
Die echte Gefahr liegt woanders.
Die meisten Kanzleien können heute nicht beantworten, wer in welchem Workflow KI-Outputs prüft, wer redaktionelle Verantwortung trägt und welche Inhalte ohne menschliche Kontrolle weitergegeben werden. Genau dort entsteht Haftung.
Was vor August geklärt sein sollte:
- Welche KI-Tools erzeugen Outputs, die direkt an Mandanten gehen, ohne vorherige Prüfung?
- Wer trägt in jedem Workflow die redaktionelle Verantwortung für KI-unterstützte Inhalte?
- Gibt es eine interne Richtlinie, die das schriftlich regelt?
Bei Verstößen gegen die Transparenzpflichten drohen laut EU AI Act Bußgelder von bis zu 15 Millionen Euro oder 3 Prozent des weltweiten Jahresumsatzes.
Die Frage ist nicht, ob Sie KI einsetzen. Die Frage ist, ob Sie nachweisen können, wer dabei die Verantwortung trägt.
Hat Ihre Kanzlei diese Frage bereits beantwortet?
Daten sind das neue Gold. Ohne die richtige Sicherheit werden sie jedoch schnell zum Risiko.
Lexemo ist jetzt nach ISO 27001 zertifiziert – dem internationalen Standard für Informationssicherheit.
Was das für dich bedeutet:
✅ Geprüfter und unabhängig verifizierter Schutz deiner Daten
✅ Sicherheitsstandards auf Enterprise-Niveau
✅ Die Sicherheit, auch mit sensiblen Fällen zu arbeiten – ganz ohne Workarounds
Bei Lexemo ist Sicherheit kein ausgelagerter Bereich, sondern fest im Team verankert: Diese Zertifizierung wäre ohne Olaf, unseren CTO und Co-Founder, nicht möglich gewesen.
Mit über 20 Jahren Erfahrung in der IT-Infrastruktur und mehr als 15 Jahren in der Hedgefonds-Branche bringt er genau die Sorgfalt und Sicherheitskompetenz mit, die für eine solche Zertifizierung entscheidend sind.
Aber es ist weit mehr als nur ein Zertifikat: Es stärkt unsere internen Prozesse und sorgt gleichzeitig dafür, dass die Daten unserer Kunden bestmöglich geschützt sind.
Denn Innovation braucht Sicherheit, vor allem im Rechtsmarkt.
Wir sind stolz auf diesen Meilenstein und unser Team!
#LegalTech #ISO27001 #Datensicherheit #Lexemo #LegalInnovation
Build smarter, not just faster.
That distinction matters more in law than in almost any other profession.
The excitement around vibe coding in legal tech is well-founded. The ability for lawyers to build their own tools without a development team is a genuine shift in how the profession can operate.
But enthusiasm without structure is where things start to go wrong and in law, the consequences of getting it wrong are not a bad user experience. They are a compliance gap, a missed obligation, or a liability.
Research shows that AI-generated code contains more critical issues than code written by humans.
When law students at Case Western Reserve University were taught to build their own AI tools, one of the most valuable lessons they took away wasn't how capable these systems are. It was a clear-eyed understanding of precisely where they break.
Knowing the limits of the tools you build turns out to be just as important as knowing how to build them.
This is the line that separates AI without guardrails from a legal automation platform that was built for the profession.
Automate 2.0 by Lexemo isn't a blank AI canvas. It is a structured environment designed specifically for legal workflows, one that gives lawyers the flexibility to automate what matters while maintaining the rigour that a regulated profession demands.
Innovation needs structure. That's what Automate 2.0 was built for.
👉 /automate2launch
#LegalTech #VibeCoding #LegalRisk #LegalAI #LegalAutomation #Lexemo #ResponsibleAI #FutureOfLaw
What's most significant about where legal practice is heading isn't any single tool or technology, it's the shift in who gets to build them and what that means for the profession.
Not long ago, a lawyer with an idea for a practice tool faced a long road before seeing anything usable. Brief a developer. Wait weeks for a first version. Go through multiple rounds of revisions.
By the time the tool was delivered, the process had consumed significant time and budget. For many, the idea never moved forward at all.
Jamie Tso, a Senior Associate at Clifford Chance in Hong Kong, is a compelling example of what that journey looks like today.
Through vibe coding, he has independently built a range of working legal AI tools including a bulk document analysis and review app, a redlining tool called RedlineNow that he built and shipped in two days, and a signature packet automation tool.
Some of his tools went viral internally at Clifford Chance and were ultimately adopted by the firm.
What makes his story so significant isn't just the tools themselves, it's what they signal. The lawyers who will stand out over the next decade won't just be exceptional at legal reasoning. They'll be the ones who know how to translate that reasoning into tools that make their practice sharper, faster, and more scalable without waiting on anyone else to do it for them.
We have designed hashtag#e! by Lexemo with that lawyer in mind. AutoMate 2.0 utilizes the power of vibe coding to allow you convert your legal expertise into automations 👉/automate2launch
#LegalTech hashta#VibeCoding hahtag#LegalInnovation #LegalAI hahtag#LegalAutomation hahtag#Lexemo hshtag#FutureOfLaw
From idea to legal tool in an afternoon sounds bold. But Vibe coding has made it an achievable goal.
That's not a marketing claim, it's what's already happening in legal practice, and it's changing who gets to build technology in law.
The process is simpler than most lawyers expect. You describe your legal need in plain language. AI writes the code. Your tool is ready to use.
No developer, no lengthy procurement process, no technical background required. Just a clear understanding of the problem you're trying to solve.
We're already seeing this in action across the profession.
- In-house counsel building their own clause checkers.
- Lawyers creating GDPR compliance comparators.
- Practitioners automating redline reviews without ever touching a development environment.
What once required months of back-and-forth with a software team can now be done independently, by the lawyer who understands the problem best.
This is vibe coding and it's one of the most significant shifts in legal practice right now. The power to build tools is no longer reserved for those with a technical background. It belongs to anyone with a clear legal mind and the right platform behind them.
Automate 2.0 by Lexemo was built with exactly this concept in mind.
/automate2launch
There is a version of your organisation's knowledge that lives in documents. And then there is everything else.
Recorded client meetings. Deposition audio. Site visit photographs. Printed regulatory circulars that arrived as scanned images. Guidance webinars from regulators that were never transcribed. Legacy contracts that exist only as scanned files from ten years ago.
This second category is not marginal. In most legal teams it represents a significant portion of institutional knowledge the kind that often determines the outcome of a matter or the accuracy of a compliance position. And in most legal AI deployments today, it is completely invisible to the search layer.
The reason is architectural. Legal AI assistants built on text-only retrieval can only surface what text-only embeddings can index. Everything outside that scope audio, images, scanned documents, video simply does not exist from the system's point of view.
Everlaw, operating in the high-stakes context of litigation discovery, reported a 20% improvement in recall after adopting Gemini Embedding 2 precisely because their implementation could now connect textual evidence with visual exhibits and audio testimony in a single search index.
That gap between what your AI can find and what is actually relevant is not a future risk. It is present in every query your system runs today against an incomplete index.
The practical question for any legal team investing in AI right now is straightforward: what percentage of your institutional knowledge is currently invisible to your search infrastructure? And what decisions are being made on the basis of answers that were built from an incomplete picture?
Those are the questions worth pressure-testing before your next deployment.
Read how Everlaw applied this in active litigation discovery: /data/googles-gemini-embedding-2-arrives-with-native-multimodal-support-to-cut
Google's Gemini Embedding 2 just changed what legal AI search can do and most legal teams haven't caught up yet.
Until now, searching across a case file meant running separate queries for contracts, emails, audio recordings, and images. You'd manually piece together results from fragmented tools, losing time and risking gaps in your review.
Gemini Embedding 2 is the first fully multimodal embedding model that maps text, images, video, audio, and documents into a single unified search space. One query. Every format. Simultaneously.
For legal teams, this unlocks something genuinely new:
🔍 Search across entire case files (contracts, emails, recordings, and images) through one semantic layer
⚖️ Move beyond keyword matching to meaning-based retrieval across 100+ languages
📂 Eliminate the fragmented pipelines that slow down discovery and inflate compliance risk
✅ Early adopters like Everlaw have already reported measurable improvements in precision and recall across millions of legal records
This isn't just a better search engine. It's a new foundation for how legal teams handle information at scale.
We broke down exactly what Gemini Embedding 2 means for legal AI search and what it could mean for your workflows.
👉 Full article:/blog/gemini-embedding-2-legal-ai-search
💡 How is your team currently handling multimodal evidence in discovery or contract review? We'd love to hear what's working and what isn't.
Most people evaluating legal AI focus on the quality of the answers. The more important question is what the AI was able to search before it answered.
Every serious legal AI assistant today uses embeddings to retrieve relevant content before generating a response. An embedding converts a document into a mathematical fingerprint that captures its meaning. When you ask a question, the system finds the fingerprints closest in meaning to your query and builds its answer from those results.
The quality of that retrieval step determines everything. A wrong retrieval produces a wrong answer. An incomplete retrieval produces a dangerously incomplete one. And if your retrieval layer only covers text documents, your AI is building answers from a fraction of what actually exists in your information landscape.
Until March 2026, this was an unavoidable constraint. Every content format required its own embedding model, its own index, and custom code to attempt cross-format retrieval. Each translation step introduced errors and lost information that existed in the original format.
Gemini Embedding 2 removes that constraint. It is the first model to embed text, audio, images, scanned PDFs, and video in a single unified space natively, without transcription pipelines or format conversion. A recorded deposition is a first-class input alongside a digital contract. A scanned handwritten note is as searchable as a native PDF.
For legal teams building AI assistants today, the retrieval architecture you choose determines what your AI can and cannot know. Building on text-only embeddings now means a full rebuild later.
The foundation matters more than most teams realise when they start.
Read more on Gemini Embedding 2: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/
Most legal teams are not losing time because they lack information. They are losing time because their information lives in the wrong places.
Contracts in one system. Deposition recordings in another. Scanned court exhibits in a separate folder. Regulatory PDFs somewhere that technically belongs to no one. Each format sits in its own silo, requiring its own search tool, its own workflow, and its own person to manage it.
This fragmentation has always looked like an irreducible inefficiency of legal work.
But it is not. It is a structural problem created by a technical limitation: until very recently, AI search tools could only handle one content type at a time. A text model could search documents. An audio tool could search recordings.
But they operated in entirely separate systems. Cross-format search was architecturally impossible.
That limitation is now being solved. Google's Gemini Embedding 2, released in March 2026, is the first embedding model to map text, images, audio, scanned PDFs, and video into a single unified mathematical space.
A single query now retrieves semantically relevant results across all of those formats simultaneously, ranked by meaning rather than by keyword match.
For legal teams, this changes what is searchable at all. A question like "were there any communications indicating awareness of this defect before shipment" can now search emails, recorded calls, scanned handwritten notes, and photographs of whiteboards at the same time, in one query, without separate workflows.
The information was always there. The architecture to find it across every format it lives in is what was missing.
Read the full technical breakdown: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/
Most legal thought leadership is forgettable. Not because lawyers lack expertise, but because expertise alone doesn't move people.
✨Stories do ✨
Here's the challenge attorneys face: how do you tell a compelling story when your content is about court decisions, regulatory shifts, or legal trends? It feels like storytelling and legal writing live in different worlds.
But there's one content format where the two meet naturally: the "best practices" article 💬
Done right, it's not a sales pitch. It's not a "look how great we are" recap. It's a story about a real challenge a client faced, the thinking that went into solving it, and the best practices that emerged explained in an objective, educational tone that actually builds trust.
This approach works because it shows your thinking in action.
Clients don't just want to know what you know. They want to understand how you think. And when you walk them through a real situation, the stakes, the complexity, the resolution, they feel it. They remember it. They come back.
The goal isn't just to be a source of thought leadership. It's to become the destination for it 🎯
📖 Worth a read if you're an attorney or legal marketer thinking about how to make your content stick:
/thelegalintelligencer/2026/03/12/this-is-how-you-inject-storytelling-into-your-legal-thought-leadership/
