Building Legal Agents When You Can't See the Data - Jacob Lauritzen, CTO of Legora

Legora can’t read a single trace of what its agent does for a lawyer. The data belongs to the law firm’s clients. How do you build and test an agent when you can’t see its work?

Jacob Lauritzen is the CTO of Legora, one of the fastest-growing companies in legal tech history. The team started in a Stockholm basement with ChatGPT, Swedish law, and a search tool. Today, a lawyer can use Legora to draft and edit Word documents, review 10,000 contracts in a single table, or run due diligence. The agent also coordinates work between lawyers, clients, and other agents. When Legora made its own acquisitions, the team used it for diligence.

• Why Legora resisted adding a model picker for years, and why it finally added one

• Jacob’s estimate that models already do about 60% of legal work well, and why the rest takes a smarter model

• How three engineers and three lawyers build test environments modeled on customers’ work, much like the “worlds” used in reinforcement learning

• A citation problem where the model got it right 70% of the time, then 99% after one release. Legora didn’t have to change anything.

• Why Legora runs everything on Azure and why it doesn’t use ElevenLabs

• Tabular Review, the million-cell spreadsheet the industry copied down to the name

• How intake works, how agents create lists, and how a list can start another agent

• What legal engineers and forward-deployed engineers each do, and who gets to write code

• The 300 Spartans slide Jacob used to argue that 20 engineers would be enough forever. Now there are 100, and he still feels short-handed.

• Why Jacob finds Fable and Opus too chatty for drafting and likes Astra’s concise writing

• Why drafting a long contract is a search problem

• Whether model labs will cut off legal tools the way they cut off coding tools

• What happens to law firm partnerships, the billable hour, and jobs for junior lawyers

Legora is hiring engineers in New York, Stockholm, and London.

0:40 Meet Jacob Lauritzen, CTO of Legora

1:05 Starting out as underdogs in a Stockholm basement

1:54 How Legora grew from ChatGPT with Swedish law into a legal agent

5:23 Which models Legora uses and why it added a model picker

7:48 Are today’s models good enough for lawyers?

8:20 What agents couldn’t do nine months ago

9:18 Testing the agent in simulated worlds and a 10,000-file data room

10:07 The evals team and Legora’s 100 legal engineers

11:03 When evals didn’t seem worth it and one release took citations from 70% to 99%

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12:18 What legal practice could look like in five to ten years

14:18 Will the law firm partnership model survive?

16:40 What happens to paralegals and junior lawyers?

18:00 The billable hour and why earlier legal tech fell short

22:44 How Legora used its own product for acquisition diligence

23:59 How Swedish and American law firms differ

26:12 Product metrics when you can’t read the traces

27:41 Paraphrasing traces and narrowing failures down to 10 or 20 types

28:42 Why data privacy led Legora to run everything on Azure

30:04 Open-weight models and model arbitrage

30:55 Transcription models and why Legora doesn’t use ElevenLabs

33:33 The agent, Tabular Review, projects, and the Word add-in

35:51 Why a spreadsheet works and what you learn from the first 10 rows

37:53 How the legal intake bot works in Slack

39:11 How agents create lists and lists start agents

40:15 Building products on those same pieces and acquiring Wexler

42:37 What forward-deployed engineers do at Legora

43:52 How legal engineers and forward-deployed engineers onboard a big client

46:43 What’s next for the forward-deployed team over the coming year

50:12 The 300 Spartans slide and the plan to stay at 20 engineers

51:32 The bets nobody asked Legora to make

53:05 Post-training open-weight models for legal research

54:05 Search, retrieval, and late interaction

54:51 What makes a good legal agent harness and why drafting depends on search

56:25 Which model writes the best draft?

57:03 Hallucinations and Legora’s citation system

58:19 MapReduce or one big context window for a million-token contract?

1:00:16 Getting anonymized examples and the idea of buying a law firm for its data

1:01:53 Why Jacob advises against default embeddings

1:02:44 Will lawyers work in Legora or through Claude?

1:03:26 Will the model labs cut Legora off?

1:04:24 Law schools and scholarships for computer science majors

1:05:28 Hiring in New York, Stockholm, and London

1:06:09 Why most work eventually becomes legal work