Seattle-based Supio announced a $60 million Series B on April 30, 2025, to expand an artificial-intelligence platform built for personal-injury and mass-tort law firms. Sapphire Ventures led the round, joined by Mayfield and Thomson Reuters Ventures, bringing Supio’s disclosed funding to $91 million at that date.
Supio is not primarily a general legal-research chatbot. Its platform is designed to turn medical records, bills, liens, discovery, depositions and other case documents into structured information that plaintiff lawyers can search, review and use in case preparation.
What the $60 million round means
| Detail | Reported information |
|---|---|
| Announcement date | April 30, 2025 |
| Round | $60 million Series B |
| Lead investor | Sapphire Ventures, an existing investor |
| Other named investors | Mayfield and Thomson Reuters Ventures |
| Total disclosed funding | $91 million as of the Series B announcement |
| Earlier financing | $25 million Series A announced when Supio emerged from stealth in August 2024 |
Supio said it would use the proceeds for engineering, artificial-intelligence research, product development and expanded nationwide sales and go-to-market operations. The company also announced leadership additions in sales, customer success and marketing.
The financing was reported by Supio in its April 30 announcement. Goodwin separately described its work advising on the financing in a May 2025 release.
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What Supio’s software actually analyzes
The company describes its product as legal AI and document intelligence for plaintiff-side practices. Its stated capabilities include:
- Case intake and organization.
- Medical chronologies that arrange treatment and provider information over time.
- Medical-bill and lien analysis.
- Case-economics analysis.
- Search, summarization and extraction across large document collections.
- Deposition and witness-statement analysis.
- Contradiction and discrepancy detection.
- Demand-letter drafting and litigation-preparation support.
- Connectors to case-management and document systems, plus an API for custom workflows.
In practical terms, a firm might connect or upload a claimant’s records, use the system to extract dates and treatment details, review a structured chronology or billing ledger, investigate flagged inconsistencies, and use validated information in a demand package or litigation plan. That sequence is an explanatory model of the workflow; the specific outputs and review procedures depend on the firm’s configuration.
Supio’s product site emphasizes converting unstructured records into lawyer-usable evidence. That is different from a general-purpose tool whose main job is legal research, citation retrieval or broad drafting.
Why personal-injury and mass-tort firms are the target
Plaintiff practices often handle thousands of pages per matter: hospital and physician records, invoices, liens, pharmacy data, discovery responses, depositions and expert reports. Mass-tort firms repeat similar review tasks across many claimants while still needing to identify the facts that make each case different.
That combination of volume, repetition and medical terminology gives a specialized platform a plausible advantage over a generic chatbot. Supio’s models and workflows are aimed at damages, treatment histories, liability facts and settlement preparation rather than every type of legal work.
The same specialization limits the addressable market. A criminal-defense, family-law, tax or transactional practice may gain little from a system optimized for medical-record-heavy plaintiff matters. A small firm with few documents may also find that implementation effort outweighs the time saved.
Founders, customers and reported traction
Supio was founded in 2021 by Jerry Zhou and Kyle Lam. GeekWire reported that both had worked at Microsoft and Avalara after previously co-founding a mobile-gaming business; their Microsoft experience included Office 365-related work. The background explains the founders’ software and cloud experience, but it is not independent evidence that the product is accurate.
GeekWire identified Seattle as the company’s base and reported about 100 employees around the financing. Named customers in Supio’s announcement and coverage include Hughes & Coleman, Daniel Stark, Thomas Law Offices, Whitley Law and TorHoerman Law. Supio’s announcement contains a spelling variation for Hughes & Coleman, so buyers should verify customer references directly.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Supio said annual recurring revenue had grown fourfold since its Series A. The company also cited customer-reported results, including:
- Firms reporting 20% to 30% gains per case in one example.
- Thomas Law Offices reporting a 62% increase in annual case volume after adoption.
- Supio saying it was involved in work connected with TorHoerman Law’s $495 million Abbott Labs verdict.
Those figures are company or customer claims. They do not establish that the software caused a particular verdict, settlement or revenue increase. Case outcomes also depend on evidence, attorneys, venue, opposing parties, strategy and timing.
What evidence exists for time savings and accuracy?
Supio later marketed reductions in case-preparation time of up to 75%, savings of $500 to $1,000 per case and hundreds of hours recovered. One cited customer example involved 437 hours saved across six cases. These are marketing or customer-reported figures, not independent benchmark results.
In a September 2025 announcement, Supio said its expanded platform had processed more than 27,000 cases and supported more than $1 billion in settlements. “Supported” does not mean the software caused those outcomes. The same announcement described accuracy of about 97% and used “human-level accuracy” positioning, but the available material does not specify the test set, task definition, error categories, baseline or independent evaluator.
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For a legal buyer, a meaningful accuracy review should ask whether the number measures extraction, classification, chronology construction, contradiction detection or another task; how difficult the documents were; and how often reviewers found material errors.
How human verification fits in
Supio says it combines specialized AI with human expert verification to address hallucinations and accuracy concerns. That hybrid approach may be useful for high-stakes records, but the public materials reviewed do not fully explain:
- Whether reviewers are lawyers, paralegals, medical specialists or other vendor personnel.
- Which outputs receive review and whether every result is checked.
- How disagreements between a model and reviewer are resolved.
- Whether verification changes price or turnaround time.
- What audit trail the customer receives.
Lawyers therefore remain responsible for checking facts, citations, calculations, timelines and generated language before using them in negotiations, advice or court filings.
How Supio compares with other legal-technology categories
| Category | Examples | Typical strength | How it differs from Supio’s stated focus |
|---|---|---|---|
| Specialized plaintiff-law AI | Supio; EvenUp | Medical records, damages and plaintiff workflows | Most directly aligned with personal-injury and mass-tort operations |
| General legal AI | Thomson Reuters CoCounsel; Lexis+ AI | Legal research, drafting and broad document work | Broader practice coverage, less specifically centered on medical-record analysis |
| Practice and case management | Clio; Filevine; Litify | Matter management, intake, billing, collaboration and reporting | Often an infrastructure layer that may complement document intelligence |
| Custom or in-house workflows | General-purpose models connected to firm systems | Control and flexibility | Requires the firm to build, secure, test and maintain its own workflow |
What changed after the funding announcement
By September 2025, Supio was presenting a broader case-lifecycle strategy, including a litigation-agent suite, case-intake suite, drafting tools, deposition analysis and CaseAware AI. It also announced a “risk-free pricing model.” The later announcements show product expansion beyond the original document-analysis framing, but they do not change the historical terms of the April 2025 financing.
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Supio’s website currently directs prospects to request a demo rather than publish a dollar rate card. No specific public price, per-user fee, per-case fee or minimum commitment is established in the available material.
Risks a law firm should test before buying
Accuracy and document quality
- Scanned records, handwriting, poor faxes, tables, stamps and redactions can reduce extraction quality.
- Chronologies may omit a record, misread a date or flatten an ambiguous diagnosis.
- Apparent contradictions may involve different dates, injuries or contexts rather than a true conflict.
Damages and economics
Medical bills, liens, duplicate invoices, write-offs and unrelated treatment can be categorized incorrectly. Case-economics outputs require accounting and legal review.
Confidentiality and privilege
Before uploading medical or litigation data, review retention, deletion, access controls, subprocessors, breach procedures, data residency and client-consent requirements. Supio’s site claims SOC 2 Type II and HIPAA, PHIPA and GDPR compliance, with U.S., U.K. and Canadian data centers; those are company website claims that should be checked against current security and audit documentation.
Integration and exit
Connectors may not preserve every permission, folder structure, version or metadata field. Ask how duplicates are prevented, how exports work at termination and whether the firm can retrieve a complete audit history.
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Compare the subscription or case cost with document volume, staff time, review requirements and implementation work. A firm seeking transparent self-serve pricing or handling only a small number of matters may not be a natural fit.
Questions to ask in a Supio demonstration
- Which case-management and document systems can be connected today, and what data is transferred in each direction?
- Which outputs are machine-generated, which receive human verification and what does that verification guarantee?
- What accuracy results are available by task, document type and error severity?
- How are privilege, HIPAA-related information, retention and deletion handled contractually?
- Is pricing per user, per case, per document or another model, and are there minimums or implementation fees?
- Can the firm export source documents, extracted data, drafts, annotations and audit logs?
- What training, support and escalation process is available when the system produces a material error?
The bottom line on Supio’s $60 million raise
Supio’s Series B is a substantial vote of investor confidence in vertical legal AI: $60 million from Sapphire Ventures, Mayfield and Thomson Reuters Ventures, bringing disclosed funding to $91 million as of April 30, 2025. The company’s bet is that plaintiff firms will gain more from domain-specific analysis of medical and litigation records than from a general legal chatbot.
That proposition is plausible for high-volume personal-injury and mass-tort practices, but the strongest productivity, accuracy and settlement claims remain company- or customer-reported. A careful buyer should evaluate representative documents, human-review procedures, security terms, integrations, export rights and total cost before treating Supio as a replacement for professional judgment.
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