Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Picture a junior lawyer who needs to review a thirty-page contract for a deal. In the past, that might have taken an evening or two. With an agent, it becomes a short series of steps the model runs through for them. Each step gives the model a different amount of text to read and asks it to write a different amount back.
First, the agent reads the whole contract (about 12,000 tokens of input) and writes a short summary of around 800 tokens. This covers the sections, key terms, and schedules. Next comes the hard part. The agent goes clause by clause and checks each one against a list of common risks, like liability caps, intellectual property, and termination rights. This step reads about 8,000 tokens and writes the most of any step: around 3,500 tokens of detailed notes.
After that, it cross-checks the risky clauses against past cases and local rules, reading about 6,000 tokens and writing 1,200. Then it writes the report the lawyer will actually read, with risk scores and suggested edits, about 4,000 tokens in and 2,500 out. Finally, it reviews its own work against the original contract to catch any mistakes: about 5,000 tokens in and 1,500 out.
Add it all up and one review uses about 44,500 tokens, roughly 35,000 read and 9,500 written. So the agent reads about four times as much as it writes. That ratio matters, because models charge very different prices for reading and writing.
Now scale it up. Imagine 5,000 lawyers using the platform. That is similar to Harvey's reach across several large firms. Its trial with one firm, Allen & Overy, alone put 3,500 lawyers on the platform and produced 40,000 queries. Say each lawyer starts three reviews per working day, over 250 working days a year. That comes to 3,750,000 reviews a year. In tokens, that is about 131 billion read and 36 billion written each year, all from this one workflow. One review is cheap. Millions of them are not. At that scale, the cost per review becomes the number that matters most.
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