Quick overview: who this comparison is for
This comparison is for Swiss law firms and in-house legal teams that want to use Legal AI as a production tool for daily contract work, not as a demo. The typical trigger is a very practical question: How do we get to reliable results faster without compromising data protection, legal privilege, or internal standards?
Law firms vs in-house legal: same use cases, different priorities
Law firms and in-house legal often share the same use cases: reviewing NDAs, DPAs, SPAs, master agreements, addenda, and side letters. The difference is in what they prioritize.
Law firms primarily want speed with consistent quality, clear references to relevant passages, and outputs that stay consistent across multiple lawyers so partner review and client communication remain clean.
In-house legal additionally prioritizes scale, standardization across many stakeholders, and the ability to enforce internal playbooks consistently without turning every round into a bespoke exercise.
Why do so many people search for “Legora alternative Switzerland”?
Many teams start with the assumption that Legal AI is mainly about feature lists. In practice, the search for an alternative is usually driven by three things: enterprise procurement, privacy requirements, and whether teams can truly work in the document or constantly lose context.
Enterprise focus and procurement: not every solution fits every team
Some tools are built for large enterprise setups: multi-step procurement, complex security questionnaires, SSO integrations, custom quotes. For teams that want to become productive quickly, this can become a bottleneck.
The real question is less whether a tool can do everything and more whether it works in your reality: How much effort does rollout take? How quickly do lawyers become measurably faster? How well can quality be standardized across multiple people?
Data protection, legal privilege, and data location: Switzerland as a real criterion
In Switzerland, data protection is not just a checkbox. For law firms, legal privilege is an added layer. For in-house teams, there are often internal compliance requirements and strict expectations around data residency and access controls.
When teams look for a Swiss alternative, they typically mean something very specific:
Hosting in Switzerland or at least clearly defined data regions
no data transfer to the US as a default path
processes that treat contract and client data as highly sensitive
What is Legora and how is it positioned?
Legora is positioned in the market as a Legal AI platform for professional legal teams, often with an enterprise angle and a focus on productivity-oriented workflows. For Swiss teams, the international brand is less important than practical fit with Swiss requirements: data residency, legal context, language and drafting conventions, and most importantly execution directly in the document rather than analysis in a side window.
CASUS as the Swiss alternative to Legora
CASUS positions itself as a Swiss Legal AI platform for law firms and in-house legal teams, focused on document work directly in Microsoft Word or in the web app. This matters because in contract work, analysis is rarely the real problem. Execution is: inserting changes correctly, preserving formatting, staying consistent, and keeping everything traceable.
Swiss made and Swiss hosted
For teams searching for a Legora alternative in Switzerland, hosting is often a core criterion. CASUS is designed for hosting in Switzerland or the EU and treats data residency as a functional decision criterion, not a marketing slogan. Many teams also care that no data transfer to the US is intended and that sensitive content is not used for human review.
Built for legal workflows – designed around real contract work
Legal work inside a contract is not a copy-paste game. What matters is whether a tool supports the typical sequence cleanly:
find the right passage
assess the risk
propose an alternative
insert the change in the correct place
keep consistency across the document
standardize outputs across the team
CASUS is built around that sequence, from navigation in the document to executing changes in Word with structure, numbering, and formatting preserved.
Feature comparison at a glance: Legora vs CASUS
A fair comparison starts with reality: teams do not buy features, they buy quality and controllability. What matters is not whether a function exists somewhere, but whether it is reliable and traceable in day-to-day legal work.
1) AI chat in the document: ask, understand, implement directly
In practice, most time loss comes from context switching: open the document, search, summarize, draft a proposal, insert, verify.
CASUS focuses on AI Chat with document context:
answer questions about the contract and link the relevant passages so you can jump directly to the clause
Agent Mode that executes changes in the document, including correct structure, numbering, and formatting
typical actions like inserting, rewriting, expanding, or adjusting clauses without pulling the draft out of context
2) Benchmark: enforce standards and make deviations visible
A common need in law firms and in-house teams: the contract should not only be good, it should match the standard. This is where generic chat usage often fails because the standard remains implicit.
Benchmark in CASUS is designed as a comparison workflow:
check against a reference standard (internal playbook or best practices)
show whether standard clauses are missing, incomplete, or deviating
findings as concrete gaps or deviations, including recommendations
insert the right clause in the right place with correct formatting
an explicit match score in percent, which helps with team consistency and reporting
3) Proofread: lock in style, Swiss spelling, and consistency
Proofreading may not be glamorous, but it is business-critical. Broken cross-references, inconsistent deadlines, or open placeholders are typical reasons for late-stage escalation.
CASUS Proofread focuses on:
spelling, grammar, and style without changing legal meaning
Swiss spelling and consistent terminology, especially around parties and defined terms
checks of cross-references, definitions, and exhibits
final checks like numbering, heading hierarchy, format consistency, contradictions, and placeholders
4) Risk and quality review: prioritize red flags and generate a report
Many tools highlight everything and nothing. In practice, you need prioritization and party perspective: what is a risk for which party, and how severe is it?
CASUS Risk and Quality Review provides:
identification of risks and weaknesses mapped to the affected party
structured findings with severity levels low, medium, high
concrete improvement suggestions per finding as drafting options
direct transfer into Word without copy-paste, correctly formatted
This is especially strong when you need to move quickly from analysis to negotiation-ready language.
5) Legal research: research with reliable sources and citations
Legal research is often promised, but quality depends on traceability. For legal work, it is not just the answer that matters, but what it is based on and how it can be reused.
CASUS offers a legal research mode designed for statutes, case law, and legally reliable sources, with structured outputs. In practice, this supports:
initial assessments of legal positions and risk drivers
pro and contra argument lines
recommended actions such as hold the position, propose a fallback, add a clause, or add a disclaimer
Important for use: this is a tool for structured preparation, not a replacement for legal responsibility.
6) AI data room: extract and compare clauses across many documents
Once you are dealing with dozens or hundreds of documents, generic chat becomes hard to manage. The goal becomes extraction and comparability.
CASUS AI Data Room enables:
upload many documents and extract fields based on columns you define, where each column is driven by your prompt
structured tables for Excel, due diligence, or compliance
clause matrices across many documents, for example liability, IP, SLA, termination per document
flagging outliers and prioritizing by risk
For privacy workflows, this can also help detect personal data, prioritize sensitive content, and support anonymization for safer sharing.
What really decides in practice
In the end, it rarely comes down to one feature. What matters is whether the tool makes team contract work measurably easier.
Workflow fit: work in the document instead of losing context
If lawyers have to copy, paste, format, and re-check after every suggestion, the gains disappear. A workflow that executes changes cleanly in the document while respecting structure and numbering is often a larger productivity lever than another prompt shortcut.
Controllability: traceability and references
Legal teams need to justify outcomes. This is especially true for risk reviews and legal research. Controllability means:
references to the relevant passages
clear mapping of findings
structured outputs that can be transferred into internal workflows, partner review, or management updates
Team rollout: consistent quality across multiple lawyers
A tool succeeds when it makes the whole team consistent, not just one power user fast. Benchmarks against playbooks, standardized review outputs, and proofread checks are key rollout levers.
Data protection and data residency compared
For Swiss teams, privacy is not optional. It is often the reason the search for an alternative starts.
Switzerland vs EU regions: what teams should verify
Instead of relying on labels, it helps to use a concrete checklist:
where is content processed and where is it stored?
are Switzerland or EU regions clearly defined, and is this contractually documented?
are there technical and organizational measures that restrict access?
how is data handled in operations, especially logs, monitoring, and support?
Legal privilege and compliance
For law firms, client matters are especially sensitive. In practice, what matters is whether processes are set up so that:
content does not flow unnecessarily into systems outside the controlled environment
there is no human access to client data as a standard process
teams can document internally how confidentiality and access are protected
Pricing and scaling: why cheap vs expensive is the wrong question
Legal AI often looks like a seat decision on paper. In reality, price is only one piece. The bigger drivers are rollout effort, support load, and whether the tool is actually used day to day.
Legora pricing is often opaque: custom quotes, frequently enterprise setups
With enterprise-oriented tools, pricing is often not publicly listed and is offered case by case. For evaluation, comparing a single number matters less than comparing total cost of ownership: how much internal time goes into procurement, setup, training, and ongoing quality assurance?
Understand the cost logic: seats, usage, add-ons, rollout effort, support load
For a robust decision, split cost into five blocks:
Seats: who really uses it, and how many people must be covered for process consistency?
Usage: are there limits that become relevant for data room work or large documents?
Add-ons: are core functions priced separately?
Rollout effort: how many hours go into enablement, guidelines, and adoption?
Support load: how often do outputs need rework, formatting fixes, or verification?
A tool that executes changes cleanly in Word and supports benchmarking against playbooks often saves the most where senior time would otherwise be lost in rework.
Decision guide: when CASUS is the better choice and when it is not
An alternative is not automatically better. It has to fit better.
CASUS is ideal if…
you want to make contract work in Word or a web app more efficient without copy-paste loops
data protection and Swiss or EU data residency are real decision criteria with clear internal requirements
you want to enforce internal standards via benchmarks and playbooks instead of re-negotiating every time
you need risk reviews with prioritization and party perspective, plus drafting options you can apply directly
you need to analyze many documents in parallel, for example for due diligence or compliance, and want a clause matrix
Another solution makes sense if…
you primarily want a broadly integrated enterprise suite built for global rollouts and centralized IT standardization
your main need is not document work but another focus, such as internal knowledge management or generic Q and A outside contract documents
Conclusion: the Swiss alternative for productive legal teams
If you search for a Legora alternative in Switzerland, you are usually not looking for a different tool name. You are looking for a solution that takes Swiss requirements seriously and works in real workflows. CASUS focuses on where productivity is actually created: in the document, with traceable findings, clear standards, and a workflow that moves from analysis straight to implementable improvements.
FAQ: Legora alternative Switzerland
Is CASUS really an alternative to Legora?
Yes, if your criteria focus on productive document work, privacy requirements, and standardizable workflows. CASUS covers core contract workflows including AI chat in the document, benchmarking against standards, risk and quality review, proofreading, legal research, and an AI data room.
Where is data processed and stored?
CASUS is designed for hosting in Switzerland or the EU. For teams that treat data residency as a key criterion, this is a central difference compared to solutions built primarily for global enterprise setups.
Does data go to the US?
CASUS is positioned so that no data transfer to the US is intended. For law firms and in-house teams where this is a hard requirement, it is often a decisive factor.
Is CASUS cheaper than Legora and why?
Direct price comparisons are rarely meaningful when one side operates via custom enterprise quotes. In practice, the cost logic matters most: how much time do you save in review, rework, formatting, and standardization? CASUS aims to reduce cost primarily through workflow efficiency in the document and consistent team quality, with a plug-and-play setup.
Which features does CASUS cover?
AI chat in the document including Agent Mode, benchmarking against playbooks, proofreading for consistency and formal checks, risk and quality review with prioritized findings and drafting options, legal research with source-based outputs, and an AI data room for extraction and comparison across many documents.
Is it better for law firms or for in-house legal?
Both. Law firms typically benefit from faster review and negotiation work with clean execution in Word. In-house teams additionally benefit from playbook-driven standardization, consistent team quality, and scaling through data room workflows.
How fast can you get started?
CASUS is designed as a plug-and-play solution and can be rolled out very quickly even in larger teams. The Word add-in can be downloaded from the Microsoft Store or deployed company-wide via the admin environment. No long change processes – immediate value.
