Patent practice blends deep technical knowledge with precise legal drafting in a way that few other legal disciplines do. A single application might require fluency in polymer chemistry, compliance with ST.26 sequence listing standards, and an understanding of how drafting conventions differ between the EPO and USPTO.
The document itself is structurally interdependent: claims, the detailed description, figures and their corresponding reference numerals all need to stay consistent as the draft develops.
Generalist AI tools handle patent documents the same way they handle any other legal text. This delivers modest efficiency gains of perhaps 10-15%, but it misses the real time sink: the structural and technical layers that define whether a patent application or Office Action response is truly ready for the examiners.
It’s the purpose-built tooling, designed by patent attorneys at Solve Intelligence, that drives reported time savings of 60-90%.
One of the overlooked challenges in patent AI is the breadth of technical fields that practitioners cover, and the sheer depth of those fields. It isn't enough for a tool to be good at "drafting" in the abstract. It needs to handle the specific technical inputs that accompany the invention.
A life sciences attorney needs to import and validate ST.26 sequence listings. A chemistry practitioner needs Markush structures embedded directly into the summary and detailed description with their structural logic intact. A mechanical patent may require figures generated from 3D CAD files, with reference numerals mapped to the specification.
Solve Intelligence supports all of these within the drafting environment. They aren't separate tools or manual workarounds. They're part of the core workflow, because they reflect what patent attorneys across different disciplines actually need day-to-day.
Generalist platforms are designed to serve lawyers across practice areas, which means their interfaces focus on features for contract drafting, tabular review, and corporate work that slow patent attorneys down versus even manual drafting in Word.
Solve's front-end strips all of that away: claims, specifications, figures, element labels, prior art, definitions, examination guidelines and beyond, all live in one environment purpose-built for patent workflows. That focus is what makes it a daily-use tool rather than something used to occasionally sense-check an email.
Solve's in-house patent attorneys are also responsible for building features alongside software/AI engineers and evaluating the latest LLM capability that shapes output quality.
Far from generic, the prompting is informed by attorneys who have handled complex chemistry portfolios at firms like Carpmaels & Ransford, software/mechanical prosecution at D Young & Co, US patent strategy across multi-million dollar portfolios at Quarles & Brady, and high-volume SEP portfolio work at Fish & Richardson, for example. That depth of experience feeds directly into how the platform generates, structures, and reviews patent text.
One of the most popular features is custom templating. Solve ships with jurisdiction and subject matter-specific templates out of the box, but practitioners can also build their own (optionally helped by our in-house attorneys) to match their firm's house style, a specific client's preferences, or the conventions of a particular patent office.
The AI then drafts in a way that reflects those choices, so output reads like it came from the attorney, not from a model. Across the 700+ patent teams now on the platform, that ability to tailor the AI is a major reason they stay.
For many patent attorneys, the challenge isn't being convinced that a vertical tool is a necessity. It's getting organisational buy-in when a generalist platform is vying for a seat at the table.
At larger firms, generalist tools may serve more people across the business, so they tend to win procurement priority. That's understandable. But the question worth asking isn't whether the generalist tool is useful across the firm. It's whether it's sufficient for patent work specifically. In practice, these are complementary investments, not competing ones.
Solve Intelligence covers the patent workflow from invention harvesting through drafting, prosecution, and claim charting. The platform is continuously shaped by close partnerships with customers and direct input from Solve's in-house patent attorneys, and much of the tooling exists because of that feedback. Solve Review is one of the latest examples: a customisable, AI-powered review tool that teams are completing in minutes rather than hours. As the platform grows across the full patent lifecycle, that same practitioner-led approach is being applied to every stage.
Patent practice requires technical depth, structural precision, and domain awareness that generalist tools weren't designed to provide. That's not a knock on those tools. It's a recognition that the most specialised legal discipline benefits from software built specifically for it, by people that understand it.
Solve Intelligence is the AI patent platform used by 700+ IP teams on six continents. Request a demo to learn more.
Generalist legal AI is trained primarily on contracts, case law, and corporate documents, and performs well on tasks like research and summarisation. Patent-specific AI is built around the structural and technical demands of patent work, from claims and specifications through to figures, sequence listings, and chemical structures. The difference extends beyond the model to the interface, prompting, and domain expertise behind the product.
Solve supports discipline-specific requirements within the core drafting environment. Life sciences practitioners can import ST.26 sequence listings, chemistry users can create ChemDraw structures directly in Solve and embed Markush structures into the specification, and mechanical patents can draw on 3D model imports for figure generation.
Yes, and many do. Generalist platforms handle tasks like legal research and email drafting well, while a patent-specific platform covers the structurally complex work they weren't designed for. The two are complementary, not competing.
