InFactIQ is an evidence intelligence platform for claims and litigation teams that reads and grounds every document in a case file, including depositions, EUOs, recorded statements, medical records, bills, policies, and correspondence, rather than retrieving only the passages a prompt ranks highest, connecting them as one body of evidence. InFactIQ produces signals and drafts for review rather than conclusions, and is not a substitute for reading the underlying record or exercising professional judgment.
Key features and functions include:
Case Intelligence
Reads depositions, medical records, bills, and other case documents together as a single body of evidence, building chronologies and surfacing gaps and connections across the whole file, with findings shown at both the case level and the individual file level.
Contradictions
Surfaces where the case doesn't hold together: testimony against records, billing against treatment, the demand against the file. Each identified conflict links back to its source.
Parties & Issues
Extracts every person, organization, and issue in the case file, organizes them by type, and links each back to its source page and line.
Playbooks
Applies a customer's own review standards consistently across every file, directing the platform's analysis and returning drafted work product from the case record.
Depositions & EUOs
Drafts the examination outline in advance of depositions and examinations under oath, then tracks admissions and contradictions in real time as testimony happens.
Witness Behavior
Scores witness consistency topic by topic across every statement in the file, functioning as an internal strategy tool rather than a final determination.
Ask & Build
Answers case-specific questions or generates requested documents drawn from the full case file rather than only the highest-scoring pages.
Security & data controls
Maintains SOC 2 Type II, GDPR, and HIPAA compliance, with 256-bit AES encryption, zero data retention, and customer-controlled data removal at any level; customer data is never used to train a model.
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