An AI Lawyer on Call – and a Global Business Set Up Before Breakfast

01 /The Concept

Skala is a legal platform built for startup founders who want to "set up a global business before breakfast"

At its core is an AI lawyer available around the clock – one you can task with reviewing an investor's term sheet to check whether the terms are unreasonably aggressive, among other things.

Here's the fuller picture of what the platform handles: company formation in the US, UAE, and offshore jurisdictions like the British Virgin Islands; drafting and reviewing fundraising documents – equity rounds, debt financing, SAFEs, and crypto tokens; trademark registration across relevant classes and jurisdictions; and employment agreements for full-time staff, contractors, and advisors, including equity option grants. A template catalog covers additional documents – from NDAs to service agreements.

If the AI's answers feel incomplete or a situation requires more depth, users can escalate to the team's human specialists for additional consultation.

A free tier covers access to legal document templates, an employee contract registry, investment agreement generation, and access to a marketplace of corporate governance service providers.

Company formation is priced at $750 to $6,000 depending on jurisdiction; annual support runs $900 to $5,600. Trademark registration starts at $990 for the first class and $550 for each additional class.

Over 100 startups are already using the platform. No funding information has been disclosed.

02 /Why It Matters

Skala's co-founder Roman Buzko has spent years working on legal support for startups. He wrote his thesis on online legal services over a decade ago, went on to build a conventional legal practice – and has now come full circle, back to the same question he started with.

The reason is straightforward: AI has finally made it viable to encode professional expertise into a product that can deliver the same quality of output a human specialist would, but faster and cheaper – and at a scale that a traditional practice never could.

The fair objection is that AI still hallucinates and struggles with genuinely novel legal problems that require creative thinking. That's precisely why Crosby ([related review](/review/dlja-odnih-jeto-povod-dlja-rasstrojstva-a-dlja-tebja-sposob-zarabotat)), which raised $5.8M this June, is building a "hybrid" legal firm where AI does the first-pass work and human lawyers verify and refine the output – trading some speed for a guaranteed accuracy floor.

But the broader point stands regardless of which model wins: AI has made it possible to turn individual expertise into an autonomously operating product. As the underlying models improve, the quality ceiling rises. The current workarounds – human review, hybrid models, escalation paths – are temporary scaffolding, not permanent features of the architecture.

This means the infobusiness model in its traditional forms – courses, consulting, advisory work – is under structural pressure to transform into AI-powered end-user products. The ironic rallying cry: "If you can't teach an AI to do what you do, you probably can't teach anyone to do it either"

AutoGenAI ([related review](/review/prostoj-sposob-ubedit)) demonstrates one version of this: the founder spent a decade learning how to write winning commercial proposals and tender bids, encoded that expertise into an AI platform, and raised $65.3M across two rounds in the first six months of 2023.

GrowthX ([related review](/review/novaja-biznes-model-dlja-bystrogo-i-pribylnogo-rosta)) shows a different version. Its founder built a proprietary SEO process at his company – combining an AI content platform with human editors to review the output – then tried selling the methodology through paid seminars. Attendees loved it but didn't implement it, because doing so was a hassle. So he left, built it as a standalone agency, and delivered results turnkey. Revenue went from zero to $600K per month between July of last year and April of this year. The expertise was the same. The delivery model made the difference.

03 /Opportunities

The accumulation of startups attacking the "expertise-to-AI-product" opportunity from different angles isn't a coincidence – it's a signal. Infobusinesses and professional service firms are at the beginning of a structural transformation, and the direction of travel is clear.

For people who already have expertise – whether or not they've ever monetized it – the question isn't whether to encode it into an AI product but when. If you've been selling it through consulting or coaching, waiting is a competitive risk. If you haven't been selling it at all, this may be the lowest-friction moment in history to start: an AI product doesn't require the time, energy, or interpersonal overhead of a traditional service model, even as a side project.

For those who are stronger on the technical side: rather than hunting for ideas, find an expert whose knowledge you could encode into a product and build it together. Or go one level up and build the platform that lets experts in a specific field encode their own expertise – the picks-and-shovels play on the whole trend.

Two examples of that last model already exist. Remark ([related review](/review/kak-nanjat-togo-kogo-nanjat-ne-mozhesh)) raised $26.3M (including $16M in early July) for a platform that lets e-commerce stores deploy AI consultants modeled on real human experts – including Olympic athletes advising in sports retail. Amigo ([related review](/review/ii-experty-eto-sovsem-ne-ii-sotrudniki) – pre-pivot) built a platform for medical clinics to create AI replicas of their doctors, scaling their reach for online patient interactions and converting more of them into paid services.

Amigo's trajectory is worth noting: it originally targeted the infobusiness market – coaches, consultants, educators – then pivoted into healthcare, where demand turned out to be more concrete and the value proposition more obvious. That suggests the most fertile ground for encoding expertise into AI products may not be the highest-profile knowledge workers, but the more grounded, industry-specific ones – where the market is bigger, the experts are more accessible, and the benefit is easier to demonstrate