Quick overview: Who this comparison is for
If you are looking for a Harvey alternative in Switzerland, you rarely mean “features only.” Most teams care about three things: data flows, day-to-day usability in the document, and a pricing model that does not get out of control when rolling out to a full team. This article is for Swiss law firms and in-house legal teams that want to use legal AI productively without compromising professional secrecy, compliance, or the quality of work product.
Law firms vs. in-house legal – different needs, same risks
Law firms primarily need reliable handling of sensitive client data, clear traceability, and consistent quality across multiple lawyers. In-house teams often prioritize scale, standardization, and speed, especially for NDAs, DPAs, procurement contracts, licensing topics, or recurring negotiations.
The risks are similar in both worlds:
Unclear data residency and unintended data flows via third countries
Tool hopping and broken workflows that waste time and create errors
Inconsistent work quality when standards are not applied consistently
Cost models that suddenly explode with usage and seats
Why are so many people searching for “Harvey alternative Switzerland”?
The keyword is a signal: many teams want the productivity of legal AI, but not the risks that can come with international setups. In Switzerland, data protection, professional secrecy, and traceable work product are often top priorities.
Data protection, professional secrecy, and US data flows as dealbreakers
If data runs through US infrastructure or US provider setups, the key question quickly becomes whether that fits your risk profile. Even if content is encrypted, in practice metadata, access paths, subprocessors, and the question of which legal access mechanisms exist in other jurisdictions can matter.
For many Swiss organizations, the decisive point is not whether a provider is “secure in general,” but whether the specific setup matches internal policies, industry requirements, and professional secrecy.
Cost and scaling: legal AI must remain economical
Legal AI becomes strategically relevant only once more than one or two power users rely on it. That is where budget questions typically start:
What does a broader rollout to a team cost?
What happens with heavy usage, for example in data rooms or with many reviews?
Which add-ons are necessary to make the workflow truly work?
If the per-user costs are very high, legal AI quickly becomes a niche tool rather than a standard workflow.
CASUS as a Swiss alternative to Harvey
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. The core idea is a setup built for Swiss requirements instead of trying to retrofit them into a global standard model.
Swiss data residency: no data in the US
For many Swiss teams, a clear statement matters: no data transfer to the US. This reduces complexity in risk assessments and simplifies internal approvals, especially for sensitive mandates, transactions, or HR data.
Security and compliance setup for Swiss requirements (FADP and GDPR)
In Switzerland, it is not only about whether a system works, but whether it integrates cleanly into governance and compliance. CASUS focuses on a security and data setup designed for Swiss and European requirements, including clear data residency and settings such as zero data retention and no human review.
Pricing advantage: why CASUS is significantly more affordable
If legal AI is not just used occasionally but introduced as a standard way of working, pricing becomes a major lever. CASUS is designed to scale economically so that each additional seat does not break the business case.
(more on this later in the article)
At-a-glance feature comparison: Harvey vs. CASUS
Up front: a fair comparison is not a feature checklist game. The real question is whether a tool measurably saves time in Swiss legal day-to-day work while delivering controllable, usable results. The points below show how CASUS covers typical legal AI workflows.
1) In-document AI chat: navigate contracts precisely and apply changes instantly with Agent Mode
In practice, you rarely need a full contract summary. You need targeted answers, instant citations, and fast implementation. With CASUS AI-Chat, you work directly in the document: ask questions, find relevant passages, and jump to the right place. Agent Mode applies changes directly in the document, including structure, numbering, and formatting.
Law firm example: You review a liability clause in a supply agreement. Instead of searching manually, you ask about scope of liability, cap, exclusions, and term. You jump to the relevant section and insert an alternative wording in the correct place, without copy-paste and without formatting chaos.
In-house example: A standard NDA needs to be adjusted to Swiss language conventions and internal terminology. Agent Mode rewrites and checks consistency across the full document.
2) Benchmark and playbook check: detect missing clauses and deviations
Benchmark is at the heart of many quality workflows: does the document match the internal standard or an established playbook? CASUS checks whether standard clauses are present, sufficiently drafted, or deviating. Typical gaps become visible, for example in data protection, termination, liability, or IP ownership.
The key is execution: a finding becomes a concrete recommendation. Missing or weak parts can be inserted as a suitable clause in the right place, correctly formatted. In addition, the match to the standard can be shown as a percentage, which simplifies team reporting.
3) Proofread: secure language, Swiss conventions, and consistency
CASUS Proofread does not replace legal judgment, but it is a strong quality lever before sending or signing. CASUS checks spelling, grammar, and style without changing legal meaning and follows Swiss conventions using ss instead of ß. It also catches common document issues:
incorrect or missing cross-references
inconsistent definitions and terminology
conflicting deadlines or terms
placeholders and open items
numbering and formatting consistency
This reduces the typical embarrassment and risks that appear in rushed phases.
4) Risk and quality review: prioritize red flags and create a risk report
In reviews, prioritization is everything. Not every deviation is critical and not every red flag matters in negotiation. CASUS Review identifies risks, assigns them to the relevant party, and prioritizes them as low, medium, or high. It also provides concrete wording options that can be applied directly in Word, cleanly formatted.
Example for M&A or commercial: The review highlights one-sided clauses from your party’s perspective, prioritizes them, and delivers concrete alternatives. This accelerates negotiation strategy and improves consistency across multiple reviewers.
5) Legal research: legal research with reliable sources
In Switzerland, research becomes risky when results are not traceable. CASUS provides a legal research mode that focuses on statutes, case law, and legally robust sources and delivers structured outputs you can integrate into work product.
The practical value: you do not only get an answer, but a clear structure, argument lines with pros and cons, and recommended actions, for example hold position, propose a fallback, or add a supplementary clause.
6) AI data room: extract and compare clauses across many documents
In data rooms, the key is parallelization: many documents, defined fields, tabular outputs. CASUS extracts content based on fields you define and produces a table suitable for Excel, due diligence, and compliance. This enables clause matrices such as liability, IP, SLA, or termination periods per document.
In addition, anomalies can be flagged and prioritized by risk, for example liability without a cap or termination periods over twelve months. For data protection use cases, the AI Data Room can identify personal data, prioritize sensitive categories, and support anonymization when documents need to be shared securely.
What really decides “Harvey alternatives Switzerland”
If we are honest, it is rarely the prettiest demo. What matters is whether a tool works in daily team operations, stays controllable, and can be rolled out economically.
Workflow fit: in the document (Word) instead of tool hopping
The biggest productivity loss comes from switching tools, copy-paste, formatting breaks, and context loss. If analysis and implementation happen directly in the document, legal AI moves from an experiment to a process. CASUS is built for exactly that, including clean formatting when applying changes.
Controllability: traceability, citations, standards
Legal teams need to explain why they changed something, recommended something, or escalated an issue. That only works if outputs are traceable, with clear document references, structured findings, and a link to standards such as a playbook or best practices. CASUS targets this level of controllability in the workflow.
Team scaling: consistent quality across multiple lawyers
Scaling is not only more seats. It means the same standards, the same review logic, and consistent output quality. Workflows like Benchmark and Risk Review help prevent two lawyers from producing completely different results for the same contract and help juniors and seniors align faster.
Cost comparison: why affordable does not mean cheap
Price is not just a number. It is the combination of seats, usage, add-ons, and the effort required to make a tool truly productive. Affordable does not mean cheap if it saves real work and reduces risk.
Typical pricing levers (seats, usage, add-ons) – where it gets expensive fast
In practice, three factors often drive total cost:
Seats: high per-user costs prevent team rollouts
Usage: volume-based billing can surprise teams with heavy document work
Add-ons: features needed for day-to-day work are not always included in the base plan
For context: a frequently cited Reddit thread mentions around USD 1,200 to 2,400 per month per user for Harvey. This is not verified, but it illustrates why many teams look for alternatives in the first place. At that level, a tool often becomes exclusive rather than a team platform.
CASUS aims for a model that remains scalable for Swiss teams without breaking the business case with every additional user.
Decision guide: when CASUS is the better choice – and when it is not
No tool is optimal for every situation. The points below help you decide internally faster.
CASUS is ideal if …
You want a Swiss legal AI solution where data does not flow to the US
You need workflows directly in Word or tightly around the document, including clean formatting
You want to enforce playbooks and standards systematically instead of reinventing every review
You need prioritized, structured risk and quality reviews with concrete wording options
You want tabular data room extraction and fast visibility into deviations
You need legal research that is traceable and usable in work product, not generic answers
You want to roll out legal AI economically across a team
Another solution makes sense if …
Your main criterion is a globally uniform stack across many countries, regardless of Swiss data and governance preferences
You primarily need very specific integrations into an existing international tool ecosystem you have already standardized
Your use case is far outside document-centric legal work, for example if documents play little role and you are looking for other process automation
Conclusion: the Swiss, more affordable Harvey alternative for productive legal teams
If you are searching for a Harvey alternative in Switzerland, you typically want a clear set of qualities: no sensitive data flows, a workflow directly in the document, traceable outputs, and costs that allow team scaling. CASUS addresses exactly these points with Swiss and EU hosting, zero data retention, and no human review, plus modules that cover contract work, standards, and data room analysis in a practical way.
If your goal is productive legal AI in daily work, not just an impressive demo, CASUS is especially worth a look if Word workflows, Swiss requirements, and cost control are at the top of your list.
FAQ: Harvey alternative Switzerland
Is CASUS really a Harvey alternative?
If you mean a capable legal AI for contract work, reviews, playbooks, and research, then yes. CASUS covers typical workflows but clearly focuses on Swiss requirements, data residency, and working directly in the document.
Does CASUS send data to the US?
CASUS is positioned as a Swiss alternative, with hosting in Switzerland or the EU and without data transfer to the US.
Is CASUS cheaper than Harvey, and why?
CASUS is designed to scale economically. The key difference is that team rollout should not fail due to very high per-user costs. In addition, a workflow directly in Word reduces operational effort because copy-paste, tool hopping, and formatting work disappear.
Which features does CASUS cover?
In-document AI chat including Agent Mode, Risk and Quality Review, Benchmark against playbooks or best practices, Proofread for Swiss conventions and consistency, Legal Research with structured outputs, and AI Data Room for extraction and comparison across many documents.
Is it better for law firms or in-house?
Both. Law firms benefit from client-work proximity, traceability, and consistent quality across multiple reviewers. In-house teams benefit from standardization, scaling, and data room workflows that make many documents analyzable in parallel.
How fast can you get started?
If your standards are clear, you can become productive quickly. It is especially fast for recurring document types such as NDAs, DPAs, SaaS agreements, or procurement terms because playbooks and check logic are easy to operationalize.
