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Depo Iq

Est. 2023 United States Updated 2026-02-10
ai
Unverified by r/legaltech members — this page is based on publicly available information, not hands-on testing or practitioner feedback. Verify your experience with Depo Iq

Depo IQ is a litigation-focused AI platform for deposition, medical-record, and claims analysis. The core product promise is narrower and more credible than a generic legal chatbot: turn transcripts, records, audio, video, EUOs, and supporting evidence into structured summaries, chronologies, contradictions, and behavioral insights that help legal and claims teams prepare cases faster. The current site has expanded beyond trial-lawyer messaging into ‘claims and litigation teams’ with end-to-end EUO workflow, scheduling, transcription, summarization, and review. Third-party corroboration is stronger than average for a batch-32 vendor: ABA Journal discusses Depo IQ in litigation AI, Attorney at Law Magazine mentions it in a legal-AI roundup, PRWeb covers the ‘Deep Thinking’ deposition agent, and SoftwareWorld/SoftwareWorld-style review shells exist. Community validation is still thin and pricing remains largely opaque, but the workflow itself is clear and well evidenced: deposition summarization, medical-record chronologies, contradiction spotting, and cross-evidence analysis.

Company Info

  • Founded: 2023
  • Team size: 1-10 employees
  • HQ: United States
  • Sector: Gen, AI

What We Haven’t Verified

This page was assembled from publicly available information. Feature claims and workflow mappings are based on what the vendor and third-party listings publish — not hands-on testing or practitioner feedback.

Workflows

Based on practitioner evidence, Depo Iq is used in these workflows:

What practitioners struggle with

Real frustrations from legal professionals — the problems Depo Iq addresses (or should address). Sourced from practitioner reviews, Reddit threads, and case studies.

Medical records arrive as 500-2,000 page PDFs that a paralegal spends 8-20 hours manually reading and summarising into a chronology — the bottleneck that delays every PI demand

Document Review & Management 28 vendors affected Solo practitioner · Small firm (2–10) · Mid-size firm (11–50) · Large firm (51–200)

Senior associate preparing for a 3-week commercial fraud trial has 200,000 documents in the review platform but no way to automatically identify where Witness A's account of a meeting contradicts the email chain from that same day — the team manually cross-references depositions against contemporaneous documents, and a critical inconsistency in the opposing party's timeline only surfaces during cross-examination when it's too late to build the impeachment narrative

Document Review & Management 4 vendors affected BigLaw (200+) · large-firm · litigation-partner · senior-associate

Litigator has 200 pages of deposition transcripts and needs to extract the 15 key facts that matter for the motion — but reading and manually tagging each relevant passage takes an entire weekend, and there's no way to link those facts back to the specific transcript page when writing the brief

Research & Analysis 21 vendors affected Solo practitioner · small-firm · mid-firm · junior-associate

Where it fits in your workflow

Before Depo Iq

Litigator, claims reviewer, or PI team receives deposition transcripts, EUO recordings, and large medical-record sets that need to become usable facts before a motion, demand, settlement decision, or witness exam.

After Depo Iq

After Depo IQ generates summaries and flags contradictions or behavioral signals, the legal team uses the output to draft demands, prep cross-examination, challenge credibility, or coordinate claims resolution. The work then feeds back into case strategy and settlement posture.

Integrations & hand-offs

Transcripts, audio/video, medical records, and EUOs -> Depo IQ analysis layer -> attorney/paralegal/claims reviewer -> briefing, demand drafting, witness prep, or insurer claims decisions. No clear DMS or case-management integrations were found.

Also used by similar teams

Community Data

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