In today’s edition, we break down how Harvey built custom legal AI that lawyers prefer 97% of the time.

In today’s edition:

• AI Case Study— Harvey AI’s $5B Legal Fine-Tuning [Case Study]

• Build Together— Here’s How I Can Help You

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[AI Case Study]

Harvey AI’s $5B Legal Fine-Tuning

Harvey AI is one of the fastest-growing AI companies in the legal industry.

They reached $100M in annual revenue in just 3 years and are now valued at $5B.

How? The answer is fine-tuning.

While most companies try to use general-purpose AI models like GPT-4, Harvey proved that in some industries, you cannot get by with generic models.

You need domain-specific fine-tuning to truly win.

To understand it better, let’s break down the case study:

• Challenge

• Solution

• Technical Side

• Results

• Lessons for AI Leaders

• Prediction

The Challenge

If your AI generates close enough answers, it will fail in law.

The legal industry is one of the toughest fields for AI adoption.

Here’s why:

• one wrong case citation can get a lawyer fined, sanctioned, or worse, there are already documented cases where lawyers were fined $30K+ for using AI that hallucinated legal facts.

• legal writing is not everyday English. It has unique structures, citations, and reasoning styles that general-purpose AI often gets wrong.

• if an AI tool cites a case that doesn’t exist, that’s game over. Even legal AI tools from big companies hallucinate 17–33% of the time.

• lawyers don’t just answer simple questions. They reason across multiple laws, past cases, and regulations at the same time.

General models can’t keep up with this level of reasoning.

In short, generic AI = too risky for the law.

The Solution

Fine-tuned legal models.

Harvey’s answer was to build a custom architecture instead of just wrapping GPT-4 with a nice UI.

They built a 3-layer model stack:

1) Foundation models

• used OpenAI’s GPT-4 and GPT-5 as the base

• ran everything on Microsoft Azure for security and scale

2) Legal domain fine-tuning

• trained on 10 billion tokens of legal data

• included the full US case law corpus, starting with Delaware and expanding nationwide

• added domain-specific reasoning and legal vocabulary

3) Client customization

• adapted outputs to match each firm’s templates, style guides, and workflows

• incorporated firm-specific precedents and preferences

The Technical Side

Harvey’s tech went beyond just fine-tune and deploy.

They built:

1) Cascading model architecture

• orchestration across LLMs

• retrieval-augmented generation (RAG)

• reasoning agents

2) Custom embeddings

• trained with Voyage AI on 20B+ tokens of case law

• cutting irrelevant search results by 25%

3) Agentic workflows

• multi-step reasoning with error correction

• the model doesn’t just answer but executes legal tasks

4) Enterprise scaling

• billions of tokens processed

• sub-minute responses

• azure-backed compliance

The Results

Here’s what Harvey achieved with their fine-tuned stack:

• in blind tests across 10 law firms, lawyers preferred Harvey’s answers over GPT-4 almost every time

• harvey’s hallucination rate was just 0.2%

• serves 42% of the top 100 US law firms

• over 500 enterprise clients in 54 countries

• partnerships with firms like Allen & Overy (3,500+ lawyers used Harvey during testing)

• partnership with PwC to expand into tax and HR

• reached $100M ARR in 3 years

• 70%+ customer retention after 13 months

• weekly active users quadrupled in a year

Lessons for AI Leaders

Here are the key takeaways you can apply:

1. fine-tuning is non-negotiable in high-stakes industries where errors are costly

2. benchmarks, explainability, and citations are critical in professional markets

3. don’t try to build a foundation model yourself, specialize instead

4. harvey’s OpenAI partnership gave them tools others didn’t have

5. think law, finance, healthcare, and aviation

6. don’t force users to change workflows

7. make AI fit into what they already do

8. foundation > Domain > Client

9. transparency builds trust

10. layered architecture wins

11. partnerships beat DIY

12. integration matters

Final Thoughts

Harvey’s rise is proof that domain-specialized AI beats general-purpose AI in high-stakes environments.

They did not just build on GPT-4.

They built on top of it with fine-tuning, custom embeddings, and workflow integration.

That’s why 42% of top US law firms now rely on them.

• every industry will have its Harvey

• think in finance, healthcare, or engineering, fine-tuned AI for professionals

• generic models won’t cut it, if the cost of being wrong is high, close enough is not enough

• data is your asset that competitors can’t copy

For AI builders and leaders, the lesson is clear:

• if your domain requires precision, fine-tuning isn’t optional

• if your users demand trust, transparency and citations are a must

• if you want market dominance, integrate deeply into professional workflows

Build specialized, fine-tuned systems.

Make expert AI.

Until next time.

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