Problem

InQuery targets document-heavy medical-legal claims work, where insurance adjusters, carriers, TPAs, self-insured employers, and law firms must manually read, organize, and cross-reference large medical files. The pain is both operational and financial: manual review creates bottlenecks, slows case preparation, increases costs, and creates the risk of missed evidence. The problem is intensified by an insurance labor shortage that overloads existing staff, while rising claim and medical costs pressure carriers’ expense ratios.

The core use case is turning a complex claim file into a decision-ready record for a team managing a large caseload, including personal-injury, workers’ compensation, and insurance-defense matters. InQuery’s published economics illustrate the opportunity: a 300-page record set can require six to twelve hours of manual summarization, while a 40-case-per-month firm could spend about $17,600 monthly on summarization labor alone. For claims organizations, the intended payoff is faster closure, fewer avoidable investigations and disputes, and less litigation.

Product / Service

InQuery combines an AI medical-record review platform with human-reviewed, workflow-specific services. Teams upload PDFs or send records through secure transfer; InQuery then structures messy inputs, detects document boundaries, collapses duplicates, and tags records by provider, date, facility, and type. It produces executive summaries, source-linked chronologies, and other case-ready work products, with automated processing and human quality checks where appropriate.

The product goes beyond summarization by identifying missing information, inconsistencies, prior injuries, causation issues, and potential cost anomalies. Its services include medical-record indexing, summarization, SIU investigation triage, demand review, independent-medical-exam preparation, and record retrieval. Outputs such as referral memos, red-flag matrices, lead sheets, and audit-ready chronologies are designed to reduce pre-review preparation from hours to minutes while preserving page-level traceability; the company positions the offering as HIPAA-compliant.

Market

InQuery competes at the intersection of AI medical-record review, medical-legal case intelligence, and insurance claims processing. Its primary buyers are carriers, TPAs, claims teams, and law firms handling personal-injury, medical-malpractice, workers’ compensation, and insurance-defense files. Relevant competitors and substitutes include Casefleet, which offers document upload, search, and analysis for legal medical chronologies, and Superinsight, which markets AI-driven medical-record review and chronology software. InQuery differentiates around regulated-market workflows, human-reviewed outputs, source-linked evidence, and claims-specific investigation and cost-containment use cases.

The public evidence points to an early-stage but operating company rather than a clearly established scale vendor: InQuery was founded in 2023, joined Y Combinator’s Winter 2024 batch, is listed as active, and has a publicly described four-person team. It has a live service catalog and publishes case-study-style results showing claimed time savings and workflow improvements, but the reviewed sources do not disclose a verified customer count or revenue figure. Accordingly, it is best characterized as an active, early commercial-stage company; the available evidence is insufficient to label it definitively pre-revenue.