Diting SI is being developed to coordinate specialized AI agents, review important conclusions, and link supported claims to source evidence — starting with legal work and complex knowledge workflows that require careful professional judgment.
Currently in internal development and validation. Public access is not yet available.
Legal research, contract review, due diligence — a single hallucinated citation can cost a case, a deal, or a license. Single-model AI is fluent, but fluency isn't evidence.
Confident-sounding but fabricated case law, clauses, and citations slip through single-pass generation.
When an answer matters, "the AI said so" is not a basis for professional judgment or liability.
Your firm's knowledge lives in specific databases and tools. AI that can't plug into them can't do real work.
Our design gives agents distinct roles in research, drafting, and review, with checks on important claims and explicit records of unresolved questions.
The architecture is designed to support different model providers. Each integration requires validation for its assigned tasks.
Planned integrations connect authorized legal databases, document repositories, and knowledge bases, with source coverage and access boundaries made explicit.
The design uses the Model Context Protocol for search, retrieval, document parsing, and computation, subject to tool permissions and integration checks.
Review agents are intended to challenge key conclusions and weak reasoning. Supported claims should link to source passages; missing evidence should remain visible.
Define the matter, available sources, and permissions. Break the work into reviewable subtasks such as issues, authorities, clauses, and deadlines.
Use authorized sources and tools to research and draft, then review important conclusions for errors, missing authorities, and weak inferences.
Present the work product alongside supporting sources, review findings, unresolved disagreements, and evidence gaps for professional assessment.
These are intended application areas. Multi-agent review can help surface errors, but its effectiveness must be tested for each workflow.
Our current focus is internal development and validation. If you work on document-heavy legal or knowledge tasks, we'd welcome a conversation about your needs. Contacting us does not provide access to a live service.
Contact the projectFor project questions and workflow feedback:
Please do not email confidential case files or sensitive personal information.