A host says one model release raised Legora's citation accuracy from 70% to 99% without further work.
In a discussion with Legora CTO Jacob Lauritzen, a host says early customers refused AWS and Google Cloud, leading the company to run on Azure. The discussion also covered Legora's post-training of open-weight models for legal research.
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@realbasilchatha@WeAreLegora announced a $550M Series E a couple weeks ago, so @swyx and I sat down with @jacsebl (CTO, @WeAreLegora) to talk about the nuances of building AI agents for the legal industry. We got into: -how one model release took Legora's citation accuracy from 70% to 99% with zero work -why Legora runs on Azure (early customers refused AWS / GCP) -why law firms punish partners for codifying what they know -why Legora post-trains open-weight models and much, much, more… Enjoy! Timestamps 0:00 Cold open 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 4:12 The switch from assistant to 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% 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 19:39 Where the money goes now 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 34:24 How a lawyer uses Tabular Review 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 47:24 Why Legora resisted building APIs 49:26 Can the agent be the API? 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 57:41 Do lawyers check the citations? 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 Link in the comments.
@latentspacepodRT @realbasilchatha: @WeAreLegora announced a $550M Series E a couple weeks ago, so @swyx and I sat down with @jacsebl (CTO, @WeAreLegora)…@realbasilchatha@WeAreLegora announced a $550M Series E a couple weeks ago, so @swyx and I sat down with @jacsebl (CTO, @WeAreLegora) to talk about the nuances of building AI agents for the legal industry. We got into: -how one model release took Legora's citation accuracy from 70% to 99% with zero work -why Legora runs on Azure (early customers refused AWS / GCP) -why law firms punish partners for codifying what they know -why Legora post-trains open-weight models and much, much, more… Enjoy! Timestamps 0:00 Cold open 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 4:12 The switch from assistant to 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% 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 19:39 Where the money goes now 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 34:24 How a lawyer uses Tabular Review 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 47:24 Why Legora resisted building APIs 49:26 Can the agent be the API? 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 57:41 Do lawyers check the citations? 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 Link in the comments.@latentspacepodRT @realbasilchatha: @WeAreLegora announced a $550M Series E a couple weeks ago, so @swyx and I sat down with @jacsebl (CTO, @WeAreLegora)…@realbasilchatha@WeAreLegora announced a $550M Series E a couple weeks ago, so @swyx and I sat down with @jacsebl (CTO, @WeAreLegora) to talk about the nuances of building AI agents for the legal industry. We got into: -how one model release took Legora's citation accuracy from 70% to 99% with zero work -why Legora runs on Azure (early customers refused AWS / GCP) -why law firms punish partners for codifying what they know -why Legora post-trains open-weight models and much, much, more… Enjoy! Timestamps 0:00 Cold open 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 4:12 The switch from assistant to 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% 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 19:39 Where the money goes now 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 34:24 How a lawyer uses Tabular Review 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 47:24 Why Legora resisted building APIs 49:26 Can the agent be the API? 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 57:41 Do lawyers check the citations? 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 Link in the comments.@latentspacepodRT @realbasilchatha: @WeAreLegora announced a $550M Series E a couple weeks ago, so @swyx and I sat down with @jacsebl (CTO, @WeAreLegora)…@realbasilchatha@WeAreLegora announced a $550M Series E a couple weeks ago, so @swyx and I sat down with @jacsebl (CTO, @WeAreLegora) to talk about the nuances of building AI agents for the legal industry. We got into: -how one model release took Legora's citation accuracy from 70% to 99% with zero work -why Legora runs on Azure (early customers refused AWS / GCP) -why law firms punish partners for codifying what they know -why Legora post-trains open-weight models and much, much, more… Enjoy! Timestamps 0:00 Cold open 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 4:12 The switch from assistant to 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% 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 19:39 Where the money goes now 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 34:24 How a lawyer uses Tabular Review 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 47:24 Why Legora resisted building APIs 49:26 Can the agent be the API? 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 57:41 Do lawyers check the citations? 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 Link in the comments.@latentspacepodRT @realbasilchatha: @WeAreLegora announced a $550M Series E a couple weeks ago, so @swyx and I sat down with @jacsebl (CTO, @WeAreLegora)…—
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