First, Habeas is trained exclusively and exhaustively on Australian legal documents. This training and Australian context window fundamentally changes how Habeas retrieves and reasons legally.
For instance, when you use a generic AI model, like ChatGPT, it retrieves from an expansive global dataset that includes non-legal sources. The system makes educated guesses, or token predictions, about legal reasoning, but operates without guaranteed grounding in real legal authorities.
Further, when you use an international legal AI tool, it retrieves from global legal datasets, often optimised for UK or US law, where the context window and interpretive framework default to legal traditions outside Australia.
Habeas retrieves exclusively from Australian legal documents. This creates a meaningful practical difference.
Generic AI models will confidently explain principles that do not exist in Australian law, and international legal tools might surface international precedents.
Habeas is architecturally set up to only provide outputs based on Australian law. Habeas’ system is search-first, meaning it only generates reasoning after verifying it against authority, rather than generating, or hallucinating, authority based on pre-emptive reasoning generation.
Generic AI Models
International Legal AI Models
Habeas Search Engine
Second, Habeas reasons over and lets you store your own materials. Habeas’ new Documents feature lets you create matter-specific repositories, where you can filter Habeas to retrieve outputs only from your document store, or reason exclusively over your materials.
This creates a ‘speak to your case’ experience, where you can ask questions about your specific matter and receive answers grounded in your own documents, rather than external hallucinatory datasets. Habeas is a secure platform, with servers hosted in Australia and exclusive data privacy, but users should still be responsible in the handling and processing of sensitive information.
The benefits of the Documents feature are significant. Habeas’ reasoning enables evidence synthesis across complex documents, connecting disparate sources and revealing patterns across a brief.
Furthermore, and less obviously, this feature also creates the potential for searchable precedent libraries within a specific practice area — for instance, if you upload 5 years’ worth of your employment law briefs to document stores, you can search over your own cases to see if similar patterns have emerged in previous briefs.
The Documents feature is particularly useful in dense litigation work, discovery review, or commercial disputes.
The capacity to unearth ‘needle in the haystack’ type content in your briefs without spending hours manually reading through discovery is significantly transformative for a practice.
Third, Habeas also offers Research Assistants, to be chosen from the 9 template options or to be customised yourself, that synthesise search and document store outputs into structured legal work.
Even with precise search and document intelligence, lawyers need to turn materials into outputs, such as research reports, advice memos, and submissions outlines.
Generic AI produces inconsistent results, while Habeas Research Assistants increasingly configure to your practice.
This offers numerous benefits, including most prominently (for firms) a consistent practice-aligned output baseline across differing seniority levels.
These Research Assistants never clock off, and are tailored to your needs.
Ultimately, the three differentiators of Habeas that have been herein identified:
Together unify and streamline any legal research workflow.
If you want to try for yourself or get in contact, book a demo with us here. We also offer the capacity for self-serve individuals to sign up, and subscribe or register a free trial at app.habeas.ai.
