ChatEQ is an AI-assisted tool for legal eDiscovery scenarios, primarily serving producing counsel. Its core purpose is to help lawyers compare and review client files against served responses in order to determine which files should be produced, redacted, withheld, or excluded.
Main capabilities:
- Code client files based on the scope defined by the served responses, rather than based on all opposing requests or other transactions.
- Flag privileged files and mixed files before production for lawyer handling.
- Keep original files unchanged, review the same file only once, and keep emails and attachments together.
- Before the full set of files is considered final, surface questions that only the lawyer can answer, such as someone's identity, whether other transactions are involved, and what the response actually promised.
- Provide an assistant tied to a single case; lawyers can ask about case progress, pending items, or the timeline; the assistant is limited to that case.
- The final production can be loaded into the review tools already used by the opposing party or by the firm.
How to use it:
- The lawyer provides the served responses, privilege log, and client files.
- The lawyer edits and signs off on the scope.
- Answer the questions only the lawyer can answer, such as someone's identity, whether other transactions are involved, and what the response actually promised.
- Handle the remaining privilege, redaction, and uncertain content.
- Produce files that meet the case requirements and can be loaded into review tools.
Use cases:
- Lawyers in eDiscovery who need to compare client files against served responses.
- Scenarios that require handling large volumes of files, identifying privileged files, mixed files, or content that needs redaction.
- Workflows that aim to preserve lawyer decision-making while using AI for preliminary coding and issue identification.
Limitations:
- Lawyers still need to make the final decisions on privilege, redaction, and uncertain content.
- Lawyers still need to sign the privilege log and cover letter.
- Client files are not used to train models and will not be sent to the model without approval.
- Each case is isolated, and file transfer and storage are encrypted.
- Specific pricing, supported platforms, model providers, and similar information are not yet available.
