Announcing the Blackstone Graph
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Isaacus
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Verified
AI & ML interests
We build legal AI models for legal tech firms that help them ship smarter products, faster.
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umarbutler
published a
dataset about 14 hours ago
ladyofcode
published an article 5 months ago
Article
Announcing the Blackstone Graph
isaacus
• • 3 ladyofcode
published an article 6 months ago
Article
Build a legal RAG app that won't be held in contempt
isaacus
• • 4
abdurrahmanbutler
posted an update 7 months ago
Post
179
I just built a Harvey's tabular review app — but better — and open-sourced the code.
https://huggingface.co/blog/isaacus/tabular-review
https://huggingface.co/blog/isaacus/tabular-review
- 1 reply
abdurrahmanbutler
published an article 7 months ago
Article
Building Harvey-style tabular review from scratch, but better
isaacus
• • 10 abdurrahmanbutler
posted an update 7 months ago
Post
193
Isaacus just shipped a major update to semchunk: AI-powered chunking based on a document’s knowledge graph representation⚡
This isn’t a tweak on existing semantic chunking. It’s an entirely new paradigm, built on hierarchical document segmentation rather than heuristics or standard embedding-based semantic approaches.
We benchmarked our AI chunking mode across a full RAG pipeline against popular alternatives like LangChain, Chonkie, and our own non-AI semantic chunker. The results were clear: semchunk’s AI mode delivered a 15% relative improvement in RAG correctness over Chonkie. It also produced more aesthetically coherent and readable when judged by a human evaluator while also being faster than all other chunking methods when run on a consumer PC.
These gains are powered by Isaacus' Kanon 2 Enricher model, which performs hierarchical document segmentation and directly powers our AI chunking mode.
As far as we know, semchunk is one of the first chunking libraries to offer true AI-powered, hierarchical-segmentation-based chunking, and the results show how much better RAG can get when chunking improves.
https://huggingface.co/blog/isaacus/introducing-ai-chunking-to-semchunk
This isn’t a tweak on existing semantic chunking. It’s an entirely new paradigm, built on hierarchical document segmentation rather than heuristics or standard embedding-based semantic approaches.
We benchmarked our AI chunking mode across a full RAG pipeline against popular alternatives like LangChain, Chonkie, and our own non-AI semantic chunker. The results were clear: semchunk’s AI mode delivered a 15% relative improvement in RAG correctness over Chonkie. It also produced more aesthetically coherent and readable when judged by a human evaluator while also being faster than all other chunking methods when run on a consumer PC.
These gains are powered by Isaacus' Kanon 2 Enricher model, which performs hierarchical document segmentation and directly powers our AI chunking mode.
As far as we know, semchunk is one of the first chunking libraries to offer true AI-powered, hierarchical-segmentation-based chunking, and the results show how much better RAG can get when chunking improves.
https://huggingface.co/blog/isaacus/introducing-ai-chunking-to-semchunk
umarbutler
published an article 7 months ago
Article
Introducing AI chunking to semchunk
isaacus
• • 9 abdurrahmanbutler
updated a
dataset 7 months ago
umarbutler
posted an update 7 months ago
Post
5,012
Isaacus, the AI research company building legal superintelligence, is hiring!
We're looking for passionate engineers who love to build and tinker and want to have an impact on the world. Specifically, we're hiring:
• ML engineers (Australia).
• Data engineers (Australia).
• Full-stack engineers (Australia).
• DevRel engineers (Australia, San Francisco, and London).
• DevOps engineers (Australia, San Francisco, and London).
If you'd like to be a founding employee at one of the few VC-backed LLM research labs in the world, receive generous equity compensation, and work alongside other highly motivated, highly skilled engineers, get in touch: https://isaacus.com/careers
We're looking for passionate engineers who love to build and tinker and want to have an impact on the world. Specifically, we're hiring:
• ML engineers (Australia).
• Data engineers (Australia).
• Full-stack engineers (Australia).
• DevRel engineers (Australia, San Francisco, and London).
• DevOps engineers (Australia, San Francisco, and London).
If you'd like to be a founding employee at one of the few VC-backed LLM research labs in the world, receive generous equity compensation, and work alongside other highly motivated, highly skilled engineers, get in touch: https://isaacus.com/careers
umarbutler
updated a
Space 8 months ago
umarbutler
updated a
collection 8 months ago
umarbutler
published a
dataset 8 months ago
umarbutler
updated a
dataset 8 months ago
abdurrahmanbutler
posted an update 8 months ago
Post
222
Isaacus just shipped a new state-of-the-art model, this time focused on reranking for legal RAG.
Although Kanon 2 Embedder already represents the frontier of legal-domain retrieval, we know that not everyone is ready to re-embed their entire corpus. We also knew there was still accuracy left on the table for teams handling highly sensitive legal work.
Enter Kanon 2 Reranker: the world’s best legal reranking model.
We tested it across both production RAG pipelines and standalone retrieval tasks, and the results were remarkable.
Not only does it outperform the competition in a category where there are still very few serious alternatives, it also delivers major retrieval accuracy gains over our standalone embedder. Those improvements translated into exceptional downstream performance.
In our final test, we compared Voyage AI by MongoDB 2.5 Rerank with Kanon 2 Reranker on Legal RAG Bench, using identical embedding models, generative models, and pipeline hyperparameters. The only difference was the reranker.
The result: Kanon 2 Reranker decisively outperformed Voyage 2.5 Rerank.
On holdout questions, the head-to-head margin was one of the most extreme we have seen: for every 1 question Voyage got right and we got wrong, there were 6 questions we got right and Voyage got wrong.
We share an example in the blog post where Voyage Rerank actually underperforms Kanon 2 Embedder on its own, delivering the wrong context to the LLM. In that case, not using a reranker at all would have led to the correct answer.
All in all, I’m immensely proud of the performance gains we’ve achieved.
But as we always say, the best benchmark is your own data.
So redeem your free credits, give Kanon 2 Reranker a try, and see firsthand the difference our models can make:
https://huggingface.co/blog/isaacus/kanon-2-reranker
Although Kanon 2 Embedder already represents the frontier of legal-domain retrieval, we know that not everyone is ready to re-embed their entire corpus. We also knew there was still accuracy left on the table for teams handling highly sensitive legal work.
Enter Kanon 2 Reranker: the world’s best legal reranking model.
We tested it across both production RAG pipelines and standalone retrieval tasks, and the results were remarkable.
Not only does it outperform the competition in a category where there are still very few serious alternatives, it also delivers major retrieval accuracy gains over our standalone embedder. Those improvements translated into exceptional downstream performance.
In our final test, we compared Voyage AI by MongoDB 2.5 Rerank with Kanon 2 Reranker on Legal RAG Bench, using identical embedding models, generative models, and pipeline hyperparameters. The only difference was the reranker.
The result: Kanon 2 Reranker decisively outperformed Voyage 2.5 Rerank.
On holdout questions, the head-to-head margin was one of the most extreme we have seen: for every 1 question Voyage got right and we got wrong, there were 6 questions we got right and Voyage got wrong.
We share an example in the blog post where Voyage Rerank actually underperforms Kanon 2 Embedder on its own, delivering the wrong context to the LLM. In that case, not using a reranker at all would have led to the correct answer.
All in all, I’m immensely proud of the performance gains we’ve achieved.
But as we always say, the best benchmark is your own data.
So redeem your free credits, give Kanon 2 Reranker a try, and see firsthand the difference our models can make:
https://huggingface.co/blog/isaacus/kanon-2-reranker
umarbutler
published an article 8 months ago
Article
Kanon 2 Reranker: the most powerful reranker for legal RAG
isaacus
• • 7 umarbutler
updated 2
datasets 8 months ago
umarbutler
posted an update 8 months ago
Post
2,053
This awesome visualization by @abdurrahmanbutler tracks how reliant the High Court of Australia has been on UK precedents over time.
Back in the early 1900s, up to 70% of citations in High Court decisions were from the UK. Today, that number sits around 20%.
This change seems to have happened gradually as Australia gained more and more independence from the UK, culminating in the Australia Acts of 1986, where we see a nice bump in the proportion of Australian cases cited.
These insights would not be possible without our latest legal AI model, Kanon 2 Enricher, which we used to extract dates and citations from High Court decisions in isaacus/open-australian-legal-corpus and categorize citations by jurisdiction. You can learn about Kanon 2 Enricher here: https://isaacus.com/blog/kanon-2-enricher.
Back in the early 1900s, up to 70% of citations in High Court decisions were from the UK. Today, that number sits around 20%.
This change seems to have happened gradually as Australia gained more and more independence from the UK, culminating in the Australia Acts of 1986, where we see a nice bump in the proportion of Australian cases cited.
These insights would not be possible without our latest legal AI model, Kanon 2 Enricher, which we used to extract dates and citations from High Court decisions in isaacus/open-australian-legal-corpus and categorize citations by jurisdiction. You can learn about Kanon 2 Enricher here: https://isaacus.com/blog/kanon-2-enricher.
abdurrahmanbutler
updated a
dataset 8 months ago
umarbutler
updated a
dataset 8 months ago