Platform
Research that shows its working
DALI is built on one constraint: it may only tell you what it can cite. Everything else follows from that.
How it works
From question to cited note
- 01
Understand the question
DALI resolves what is actually being asked: the activity, the instrument and the jurisdictions in play. Where the question is genuinely ambiguous it confirms its reading with you, rather than answering a different question confidently.
- 02
Retrieve from primary sources
A hybrid search over the ingested corpus pulls the governing instruments, the regulator guidance that interprets them, and the case law that has tested them. Retrieval is weighted toward material that is actually in force.
- 03
Synthesise, with the citation attached
The note is written from what was retrieved. Each proposition carries the source it came from, so the reasoning can be checked rather than trusted, and where the corpus does not support a proposition, DALI does not make one.
- 04
Separate the settled from the contested
The residual uncertainty is stated explicitly: which factors are determinative, which turn on an untested reading, and where a regulator has not yet spoken. This is the part a general-purpose model reliably papers over.
Capabilities
What you can do with it
Regulatory research
A structured, cited note on a regulatory question, with a client-ready version alongside.
Learn more →Document review
An agreement read clause by clause against the rules that apply, issues ranked by severity.
Learn more →Jurisdiction mapping
The same activity compared across jurisdictions, with the divergences set out side by side.
Learn more →Taxonomy and characterisation
What a concept legally is in each regime, in that regime's own category, cited to its instrument.
Learn more →Precedent currency
Your firm's precedents in a private vault, flagged when the law beneath them moves.
Learn more →Matter review
A mandate broken into the stages it requires, sequenced by what depends on what.
Learn more →Source search
Search the sources directly, with in-force status and commencement dates on every result.
Learn more →Design decisions
Six choices that shape every answer
Corpus, not recollection
DALI answers from documents it has ingested and can point to. It does not answer from a model's memory of the law. This is the difference between a citation you can open and a citation that merely looks right.
In force is a first-class fact
Instruments carry a regulatory status and, where known, a commencement date. Material that is made but not yet in force is held apart from law currently in effect and is not allowed to answer a question about today.
Uncertainty is output, not noise
Where the law is genuinely unsettled, that is the finding. DALI marks which factors are determinative, which are merely material, and where the answer turns on a reading no regulator or court has yet confirmed.
Citations are built before the model writes
The references behind an answer are assembled from database records before the language model is called, each already carrying a real source and a precise pinpoint into the document. The model works from citations it was handed rather than composing its own, so there is no step at which it could produce one that does not exist.
Output clears a gate before you see it
Generated text is checked before it is returned: enough distinct sources were actually cited, the answer references material it was given rather than general knowledge, and every inline marker resolves to a pinpoint that was genuinely supplied. Output that fails is discarded and you get an explicit insufficient-corpus response instead of a fluent guess.
Claims it cannot ground are flagged, not buried
After an answer is written, substantive claims are checked back against the sources actually retrieved. Anything the sources do not support is marked as ungrounded, and shown with the material it most likely leans on. You can see which parts of an answer rest on cited authority and which do not.
Against a legal AI tool, not a chatbot
Retrieval fixes the citation. It does not fix the characterisation.
A general-purpose legal AI already retrieves and cites. Four things separate that from what this field requires, and none of them are solved by better retrieval.
A corpus built for mature practice areas
General legal AI is built for high-volume, settled fields where the authorities are stable and plentiful. The instruments, regulator guidance and case law of digital asset, AI and fintech regulation are none of those things. Coverage here is a curation problem, not an indexing one, and a corpus assembled for conveyancing does not acquire MiCA by scale.
No taxonomy to resolve a characterisation against
This is the structural difference. Without a jurisdiction-aware map of what a concept legally is in each regime, a tool has nothing to resolve a term against, so it collapses divergent regimes into whichever meaning is most common in its training data. Retrieval cannot supply what the tool has no structure to represent.
See the taxonomy mapTuned to draft confidently, not to stop
General legal AI is optimised to produce usable drafting, which means resolving ambiguity rather than reporting it. In a field where a large share of questions have no settled answer, that is the wrong default. An evidence-bounded posture that degrades to an explicit insufficient answer is a design choice, and it has to be built in rather than prompted for.
No view on whether your own work is still current
Retrieval tells you what the law says today. It does not tell you that the memorandum your team relied on last year rests on guidance since replaced. Tracking the currency of a firm's own precedents against a moving body of law is a different capability, and it catches a class of error that is easy to miss.
See precedent currencySee DALI on your own matters
We run a walkthrough against a question your team is actually working on, so you can judge the citations rather than a demo script.