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InFactIQ InFactIQ Launches Cross-Document Contradiction Detection for Claims and Litigation Teams


News provided by

InFactIQ

Sep 17, 2026, 12:00 ET

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Built for adjusters, SIUs, TPAs and litigators, the platform cross-references testimony, medical records, billing records, and claim files at scale.

LAS VEGAS, Sept. 17, 2026 /PRNewswire-PRWeb/ -- InFactIQ today announced expanded capabilities in its claims and litigation intelligence platform, with cross-document contradiction and gap detection across all major case file types as the platform's defining analytical capability. The system reads testimony transcripts, medical records, billing records, and claim and litigation files as a connected evidentiary record, identifies where those sources are in conflict, and cites every finding to its document of origin.

The expansion addresses a structural gap in how case files are currently analyzed. Most legal AI tools examine documents in isolation: a transcript tool reads the transcript, a records tool reads the records. Contradictions, when they exist, live in the space between documents. A medical bill references treatment dates the claimant did not acknowledge under oath. A prior statement describes the mechanism of injury differently than the deposition testimony. A claims file records a prior condition absent from any medical record produced in discovery. InFactIQ reads across all of these sources simultaneously and surfaces where they diverge.

"The contradiction is almost never inside a single document," said John Sanchez, CEO of InFactIQ. "It's between them. The deposition says one thing, the billing says another, and the claim file from three years ago says something different again. That gap is where the case actually lives. We built the platform to find it."

One connected record across every case file type

InFactIQ ingests testimony transcripts, medical records, billing records, and claim and litigation files as a unified evidence surface. Each document type carries its own format, terminology, and structure. The platform normalizes them into a single fact model, allowing it to trace a treatment event, a date, an injury description, or a prior statement across every source in which it appears.

When sources conflict, InFactIQ identifies which documents are in tension, describes the nature of the inconsistency, and links each finding to the specific underlying record. Claims and litigation teams can verify every result before acting on it.

The platform adds a behavioral dimension to that cross-document analysis. InFactIQ's Behavioral AI model extracts more than 30,000 signals from recorded testimony, including speech patterns, response timing, hesitation, and vocal and visual indicators, scored against a witness-specific baseline and evaluated by topic.

When a witness's behavioral signals shift between topics, for example, answering questions about the accident differently than questions about treatment history, the platform flags those variances and cross-references them against the documentary record. These findings are assessment signals intended for defense team review; they are not a determination of truthfulness.

"Document analysis is becoming table stakes in this market," said Sasi Myadam, CTO and Head of AI Transformation at InFactIQ. "The differentiated signal is whether you can connect what a witness says, how they say it, and what the rest of the file shows, at the scale of a full case docket. We score by topic, cite every finding to its source, and show our work. An answer you can't trace to the record isn't useful to a claims professional or litigator."

Live contradiction detection during testimony

InFactIQ also operates during live depositions and examinations under oath. Ahead of each session, the platform prepares an examination outline from the case file, which the user can review and adjust before testimony begins. During the deposition, InFactIQ surfaces contradictions in real time, allowing defense teams to follow up while the witness is still under oath. Inconsistencies that would otherwise surface in post-deposition review can be addressed before the record closes.

Every finding cited to its source

InFactIQ generates source-linked citations for every extracted fact, flagged inconsistency, and behavioral finding. Claims and litigation teams can verify each result against the underlying document without leaving the platform. The system is designed for work-product and investigative use, supporting claim evaluation, case strategy, and deposition preparation.

About InFactIQ

InFactIQ is a claims and litigation intelligence platform that reads and connects the complete case file, testing the story against the evidence and citing every finding to its source. The platform ingests testimony, medical records, billing records, claim files, and exhibits, and surfaces contradictions and gaps across the full evidentiary record, including during live testimony. InFactIQ serves insurance carriers, third-party administrators, insurance defense teams, and litigation firms nationwide. Learn more at infactiq.com.

Media Contact

Alex Casanova, InFactIQ, 1 4158462501, [email protected], InFactIQ.com

SOURCE InFactIQ

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