# AI does the work, the LNC approves: the new reality of legal nurse consulting in 2026

> When AI does the reading, what is left for the LNC to do, and what does the job look like in 2027?

By Ahmed Jemaa · Medrecords AI · 2026-09-07

Canonical page (draft): https://medrecords.ai/reports/lnc-01-ai-does-the-work-the-lnc-approves-the-new-reality-of-legal-nurse-consu/

**Not published yet.** Built from the dataset, waiting on founder review.

## What the data says

- 17 vendors name legal nurse consultants on their own commercial pages. 13 of them say the output cites back to the source page.
- 9 of 17 publish a price a buyer can act on without a sales call.
- Of 74 public reviews of AI vendors in this category, 2 mention accuracy or error at all.

## At a glance

| Figure | Value |
| --- | --- |
| Vendors counted for legal nurse consultants | 17 |
| Vendors in the full pool | 364 |
| Coded evidence rows | 5677 |
| Pages captured | 1094 |
| Reviews coded | 172 |
| Dated timeline events | 28 |

## What vendors selling to legal nurse consultants say their product does

| Claim | Vendors stating it | On a product or pricing page |
| --- | --- | --- |
| Says it drafts the report itself | 1 of 17 | 0 |
| Says it redacts protected health information | 3 of 17 | 1 |
| Says it reads handwritten notes | 6 of 17 | 4 |
| Says it reviews medical bills or charges | 7 of 17 | 5 |
| Says a human reviews the output | 7 of 17 | 5 |
| Says it flags records that are missing | 8 of 17 | 6 |
| Says it removes duplicate pages | 9 of 17 | 6 |
| Says it addresses causation or apportionment | 9 of 17 | 8 |
| Says it flags contradictions inside the file | 10 of 17 | 8 |
| Says it writes a summary | 10 of 17 | 8 |
| Says it builds a dated chronology | 11 of 17 | 10 |
| Says the output cites back to the source page | 13 of 17 | 13 |

## Security and data handling

| Claim | Vendors stating it |
| --- | --- |
| States a data retention period | 2 of 17 |
| Names a SOC 2 report | 5 of 17 |
| Offers a business associate agreement | 5 of 17 |
| Says it does not train on customer data | 7 of 17 |
| Names HIPAA on the site | 11 of 17 |

## Ranking for legal nurse consultants

| # | Vendor | Score | Traceability | Audience fit | Transparency | Reviews | Velocity | Openness |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | [LezDo TechMed](https://www.lezdotechmed.com/) | 81.0 | 25.0 | 20.0 | 14.0 | 10.0 | 10.0 | 2.0 |
| 2 | [Supio](https://www.supio.com) | 77.4 | 25.0 | 12.0 | 9.0 | 11.5 | 9.9 | 10.0 |
| 3 | [CaseFleet](https://casefleet.com) | 75.8 | 20.1 | 20.0 | 14.2 | 11.5 | 10.0 | 0.0 |
| 4 | [precedent.com](https://precedent.com) | 64.4 | 25.0 | 12.0 | 19.6 | 0.0 | 2.8 | 5.0 |
| 5 | [OctopusLM](https://octopuslm.co) | 62.7 | 20.6 | 20.0 | 12.1 | 0.0 | 10.0 | 0.0 |
| 6 | [EvenUp Inc. (MedChrons product)](https://www.evenuplaw.com/products/medchrons/) | 54.5 | 25.0 | 6.0 | 4.0 | 9.5 | 10.0 | 0.0 |
| 7 | [Managed Outsource Solutions (MOS)](https://www.mosmedicalrecordreview.com/) | 51.8 | 25.0 | 12.0 | 13.3 | 0.0 | 1.5 | 0.0 |
| 8 | [MedLegal AI (Medicolegal Intelligence LLC)](https://medicalai.law) | 50.2 | 16.2 | 12.0 | 10.0 | 0.0 | 10.0 | 2.0 |
| 9 | [CaseFrame AI](https://caseframeai.com) | 48.2 | 16.2 | 12.0 | 10.0 | 0.0 | 10.0 | 0.0 |
| 10 | [CorMetrix (VerixAi)](https://cormetrix.com) | 48.2 | 16.2 | 20.0 | 0.0 | 0.0 | 10.0 | 2.0 |
| 11 | [Parambil](https://parambil.com) | 45.3 | 23.8 | 20.0 | 1.5 | 0.0 | 0.0 | 0.0 |
| 12 | [Superinsight.ai](https://superinsight.ai) | 41.3 | 20.1 | 6.0 | 10.6 | 0.0 | 2.6 | 2.0 |
| 13 | [SecondLook Health](https://secondlookhealth.ai) | 36.2 | 16.2 | 20.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 14 | [EZ-Medical AI](https://ezmedical.ai) | 22.0 | 0.0 | 20.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 15 | [trialline.net](https://trialline.net) | 19.0 | 3.0 | 6.0 | 10.0 | 0.0 | 0.0 | 0.0 |

Weights: traceability 25, audience fit 20, transparency 20, review evidence 15, update velocity 10, openness 10


## Published prices

| Vendor | What the page says | Captured |
| --- | --- | --- |
| [CaseFleet](https://www.casefleet.com/pricing) | an add-on. View comparison table ↓ Starter $30/month Advanced AI $140/month Enterprise Custom quote Case | 2026-08-13 |
| [CaseFrame AI](https://caseframeai.com/pricing.html) | $200/mo or $2,200/yr | 2026-09-07 |
| [LezDo TechMed](https://www.lezdotechmed.com/medical-record-review-pricing) | our pricing options Monthly Casewise 10001-50000 Pages/Month $0.40/ Page Start Free TrialIncludes • Indexing • | 2026-08-13 |
| [Managed Outsource Solutions (MOS)](https://www.mosmedicalrecordreview.com/reviewgenx-free-trial/) | Pricing After Trial (for as low as $0.10/page)  #### Full Platform Access Included ## | 2026-08-13 |
| [MedLegal AI (Medicolegal Intelligence LLC)](https://medicalai.law/pricing) | $49/$249/$499 per side, dual $79/$399/$799, $19 pay-per-case | 2026-09-07 |
| [OctopusLM](https://octopuslm.co/) | Try It Free Watch the 3-min demo $0.10/page · or $250/month unlimited · HIPAA compliant | 2026-08-13 |
| [precedent.com](https://precedent.com/solutions/demand-letters-v2/) | your firm. Volume discounts kick in automatically. Starting at $275 /demand Get Started "It has | 2026-08-13 |
| [Superinsight.ai](https://www.superinsight.ai/pricing.html) | For advocates handling 1-2 cases per month $ 250 /month $62.50 /credit - 4 credits | 2026-08-13 |
| [trialline.net](https://trialline.net) | PRICING & PLANS - Monthly Plan ("Flexible"): $59/user, billed monthly. No commitments. AI Timeline Builder | 2026-08-13 |
| [CaseFrame AI](https://caseframeai.com/pricing.html) | $200/mo or $2,200/yr | 2026-09-07 |
| [MedLegal AI (Medicolegal Intelligence LLC)](https://medicalai.law/pricing) | $49/$249/$499 per side, dual $79/$399/$799, $19 pay-per-case | 2026-09-07 |

## What the reviews say

- Of 74 reviews of AI or software vendors, 2 mention accuracy or error at all and 2 mention citations or source links.
- 62 of the 63 one-star reviews in the corpus are of records-retrieval or independent-medical-examination services. The one one-star review of a software product complains about cost, support and the contract, and calls the AI feature barely usable rather than inaccurate.
- Support is mentioned in 33 of 74 AI-vendor reviews and speed in 32.
- Across 30 Reddit threads, price appears in 22 and hallucination in 2.

| Vendor or thread | Excerpt | Link |
| --- | --- | --- |
| Supio | "when you ask a follow-up question, it may acknowledge that it missed something significant" | https://www.g2.com/products/supio/reviews |
| Supio | "Handwritten notes tucked into medical records can get missed from time to time" | https://www.g2.com/products/supio/reviews |
| EvenUp | "it's always good to double-check the demands received" | https://www.g2.com/products/evenup/reviews |
| EvenUp | "identifying the pros I missed and, often most importantly, the traps" | https://www.g2.com/products/evenup/reviews |
| Filevine | "barely usable and not ready for prime time" | https://www.capterra.com/p/140815/Filevine/reviews/ |
| Filevine | "cautiously exploring their AI tool, Lois, to see how well it will work" | https://www.g2.com/products/filevine/reviews |
| Filevine | "take a couple of days for us is now completed in around 2 hours" | https://www.g2.com/products/filevine/reviews |
| CaseFleet | "its transcription could be better for audio files" | https://www.capterra.com/p/155618/CaseFleet/reviews |
| CaseFleet | "we have control over reviewing and creating a chronology vs other software" | https://www.casefleet.com/use-cases/medical-chronology-software |
| Ontellus | "they did not even send out the subpoenas to the medical providers as requested" | https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChdDSUhNMG9nS0VJQ0FnSUNsbHQzRS1nRRAB!2m1!1s0x0:0xf0e877add3e574c7!3m1!1s2@1:CIHM0ogKEICAgICllt3E-gE%7C%7C?hl=en |
| Compex Legal Services | "asking for the same records that have been sent on time" | https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChZDSUhNMG9nS0VJQ0FnSURCNWFuN1VREAE!2m1!1s0x0:0x9cdfd235b5e05317!3m1!1s2@1:CIHM0ogKEICAgIDB5an7UQ%7C%7C?hl=en |
| ChartSquad | "They took so long to get records that I did not need them anymore" | https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChZDSUhNMG9nS0VJQ0FnSUNsdDVLcWN3EAE!2m1!1s0x0:0x61ca8fc687098919!3m1!1s2@1:CIHM0ogKEICAgIClt5Kqcw%7C%7C?hl=en |
| Dane Street | "Mouthpiece for the insurance companies they contract with to review appealed claims" | https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sCi9DQUlRQUNvZENodHljRjlvT2s5eFpGUkxWekpOWDE5WU5qSXdZMDFvYmpZeVNGRRAB!2m1!1s0x0:0xd6c62dacef9d0086!3m1!1s2@1:CAIQACodChtycF9oOk9xZFRLVzJNX19YNjIwY01objYySFE%7C%7C?hl=en |
| Dane Street | "The person that did my evaluation apparently did not read my medical records" | https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChZDSUhNMG9nS0VJQ0FnSUNKdElDOGVnEAE!2m1!1s0x0:0xd6c62dacef9d0086!3m1!1s2@1:CIHM0ogKEICAgICJtIC8eg%7C%7C?hl=en |
| ExamWorks | "the appointment would take 1-2 hours. I was seen for 17 minutes" | https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChZDSUhNMG9nS0VJQ0FnSURVcFluX2FnEAE!2m1!1s0x0:0x3a52724bcb18b2fb!3m1!1s2@1:CIHM0ogKEICAgIDUpYn_ag%7C%7C?hl=en |
| US Legal Support | "we did not receive rough transcripts until AFTER out trial date" | https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChdDSUhNMG9nS0VJQ0FnTUNJMVk3UzNnRRAB!2m1!1s0x0:0x19aae9bc84ba38e3!3m1!1s2@1:CIHM0ogKEICAgMCI1Y7S3gE%7C%7C?hl=en |
| r/legaltech | "maintaining consistency, traceability, and contradiction detection across large multi-source records" | https://www.reddit.com/r/legaltech/comments/1qomvsi/what_actually_works_once_medical_records_hit/ |
| r/legaltech | "work well up to maybe 200 pages, after that the context gets too long" | https://www.reddit.com/r/legaltech/comments/1qomvsi/what_actually_works_once_medical_records_hit/ |
| r/LawFirm | "Usual caveats about hallucinations though" | https://www.reddit.com/r/LawFirm/comments/1i6raqy/ai_medical_summaries/ |
| r/WorkersComp | "the ime just made up non existing symptoms and evidence" | https://www.reddit.com/r/WorkersComp/comments/1qqnpnk/ime_result_0_impairment_beware_prepared/ |

## Timeline

| Date | Vendor | Event | Source |
| --- | --- | --- | --- |
| 2021 | Wisedocs | Company launched | https://www.wisedocs.ai/press-main-page |
| 2021-02 | DigitalOwl | Seed round, $6.5M | https://www.insightpartners.com/ideas/digitalowl-raises-20m-series-a-led-by-insight-partners-to-support-innovation-and-growth/ |
| 2022 | DigitalOwl | Strategic investment, $12M from RGA, with a commercial partnership | https://coverager.com/datavant-to-acquire-digitalowl/ |
| 2022 | Wisedocs | Oversubscribed seed round, $4.1M | https://www.wisedocs.ai/company/about-us |
| 2022-01 | HealthMark Group | HealthMark acquired MedRequest Solutions and RRS Medical | https://healthmark-group.com/healthmark-celebrates-20-years-on-record-from-paper-charts-to-releasing-medical-records-digitally/ |
| 2022-01-26 | DigitalOwl | Series A, $20M led by Insight Partners | https://www.businesswire.com/news/home/20220126005204/en/DigitalOwl-Raises-20M-Series-A-Led-by-Insight-Partners-to-Support-Innovation-and-Growth |
| 2022-06 | HealthMark Group | HealthMark acquired Acton Corporation | https://healthmark-group.com/healthmark-celebrates-20-years-on-record-from-paper-charts-to-releasing-medical-records-digitally/ |
| 2023-10-25 | Eve | Eve introduced as legal AI purpose-built for plaintiff law firms | https://www.eve.legal/blogs/introducing-eve |
| 2024 | Hona | Series A, $9.5M led by Costanoa Ventures | https://www.hona.com/about |
| 2024-01-30 | Wisedocs | Series A, $12.7M CAD, led by Information Venture Partners | https://betakit.com/ai-powered-medical-record-reviewer-wisedocs-closes-12-7-million-cad-series-a-round/ |
| 2024-08-01 | Wisedocs | Growth debt facility, $4.5M CAD from CIBC Innovation Banking | https://www.businesswire.com/news/home/20240801433147/en/CIBC-Innovation-Banking-Provides-Growth-Capital-Financing-to-Wisedocs-Inc. |
| 2025-06-12 | Datavant | Datavant agreed to acquire ChartSwap from Aquiline and Capstreet | https://chartswap.com/datavant-to-acquire-chartswap/ |
| 2025-09-25 | Datavant | Datavant signed an agreement to acquire DigitalOwl, estimated at just over $200M mostly cash | https://coverager.com/datavant-to-acquire-digitalowl/ |
| 2025-10 | Wisedocs | Enterprise-ready claims intelligence unveiled at ITC Vegas 2025 | https://www.wisedocs.ai/press-main-page |
| 2025-10-08 | Datavant | Datavant completed the acquisition of DigitalOwl | https://www.digitalowl.com/press-releases/datavant-completes-acquisition-of-digitalowl |
| 2025-12 | Bevaya (Roots Automation) | Claims 93 percent loss-run accuracy on a self-run December 2025 benchmark | https://www.bevaya.ai/platform |
| 2025-12-31 | RiskAngle | RiskAngle acquired by Hiren Patel and Rewisoft | https://www.riskangle.com/about |
| 2026-02-12 | HealthMark Group | HealthMark acquired Purview, expanding into digital medical imaging delivery | https://healthmark-group.com/news/ |
| 2026-02-25 | ExlService Holdings (EXLS) | Introduced the exldata.ai agentic data solutions suite in Q4, with named client wins in fintech and healthcare | 02-earnings-calls/EXLS-2025-q4.md |
| 2026-04-29 | Verisk Analytics (VRSK) | Described momentum in commercializing multi-year investments in agentic technologies | 02-earnings-calls/VRSK-2026-q1.md |
| 2026-04-30 | CCC Intelligent Solutions (CCC) | Said AI solutions are the fastest-growing part of the portfolio | 02-earnings-calls/CCC-2026-q1.md |
| 2026-06-04 | Guidewire Software (GWRE) | Described agentic capabilities embedded in the claims adjuster workflow | 02-earnings-calls/GWRE-2026-q3.md |
| 2026-06-25 | Wisedocs | Jenna Earnshaw named CEO, co-founder Connor Atchison moves to President | https://www.wisedocs.ai/press-main-page |
| 2026-07-21 | Marsh & McLennan (MMC) | Introduced Atlas, an AI-enabled platform, and Lenwork, an agentic assistant | 02-earnings-calls/MMC-2026-q2.md |
| 2026-07-29 | ExlService Holdings (EXLS) | Went live at a large national health plan with its first customer-facing agentic AI module | 02-earnings-calls/EXLS-2026-q2.md |
| 2026-07-29 | Verisk Analytics (VRSK) | Closed the acquisition of McKenzie Intelligence Services and moved property and casualty data sets into AI-ready environments | 02-earnings-calls/VRSK-2026-q2.md |
| 2026-07-30 | CCC Intelligent Solutions (CCC) | Said carriers are moving AI from pilots to full deployments in casualty and subrogation | 02-earnings-calls/CCC-2026-q2.md |
| 2027-early | Datavant | DigitalOwl and ChartSwap to merge under the ChartSwap Insights brand | https://digitalowl.com/digitalowl-becoming-chartswap-insights |

## Methodology

This is a secondary-research study, not a survey. Every number is a count over a named set of vendors, taken from what those vendors publish on their own sites, with a source URL and a capture date.

1. We built a vendor pool from two owned corpora: a 55-site competitor scrape captured on 2026-08-13, and 328 live company profiles from the Medrecords AI content hub.
2. We ran four targeted discovery passes for the audiences the pool covered thinly: legal nurse consultants, medical expert witnesses, life care planners, and California QMEs. Those passes added 36 vendors that name those buyers on their own commercial pages.
3. We coded a fixed rubric against every page we hold. A rubric field is marked yes only when the vendor's own copy says it, and each yes carries the page URL, the capture date, and a verbatim excerpt of fifteen words or fewer.
4. We pulled each vendor's sitemap, llms.txt, and robots.txt live on 2026-09-06 and 2026-09-07 to measure how often the site changes and whether it invites or blocks AI crawlers.
5. We pulled Wayback Machine snapshots near 1 September of 2024, 2025, and 2026 and counted seven marketing terms in each, to show how the category's own language moved.
6. We read 174 third-party reviews and 30 Reddit threads and coded them into themes. Only hand-verified quotes are published.
7. We scored each vendor for each audience on six weighted components and published the component scores next to the total, so a reader can disagree with a weight and re-rank.

### Weights

| Component | Weight | What it measures |
| --- | --- | --- |
| Traceability | 25 | Does the vendor say its output points back to the source page, and does it name contradictions, missing records, or duplicates? |
| Audience fit | 20 | Does the vendor name this audience on its own commercial pages, and has it built a real page for them rather than a menu item? |
| Transparency | 20 | Is there a published price, a named security posture, a business associate agreement, a no-training-on-your-data statement, and a stated retention period? |
| Review evidence | 15 | How many third-party reviews exist, and how do they split? |
| Update velocity | 10 | What share of dated sitemap URLs changed in the last 180 days? |
| Openness | 10 | Is there a public API, an MCP server, an llms.txt file? Blocking AI crawlers subtracts. |

### Capture dates

| What | When |
| --- | --- |
| Competitor site scrape | 2026-08-13 |
| Content hub profiles | 2026-09-01 |
| Reviews and reddit | 2026-08-13 |
| Discovery passes | 2026-09-06 |
| Sitemap llms robots pull | 2026-09-06 to 2026-09-07 |
| Wayback targets | 2024-09-01, 2025-09-01, 2026-09-01 |
| Rankings computed | 2026-09-07 |

### Limitations

- This measures what vendors publish, not what their software does. A vendor that cites to the source page but never says so scores low here. That is the method working as intended: the study is about the public record, and a buyer reading a website sees the same thing.
- Coverage is uneven by design. Fifty-five vendors have a deep page-level scrape. Thirty-five more were found by targeted discovery and scraped shallowly. The rest appear in the pool for completeness and are never ranked.
- Update velocity can only be measured where a sitemap publishes lastmod dates. 60 vendors do. For the others the field is recorded as not measured, never as zero.
- Wayback coverage is thin. The Internet Archive holds snapshots near all three target dates for 35 of the 55 deep-corpus vendors. Term trends drawn from that subset are directional and are labeled as such wherever they appear.
- Review counts favor vendors that sell to law firms, because those are the vendors with public review profiles. A physician-facing product with no G2 listing is not worse, it is unlisted.
- Prices are list prices published on a pricing page. Negotiated and enterprise pricing is invisible to this method, and most enterprise vendors publish no price at all.
- Every count is a count over this vendor set on these dates. It is not a market share estimate and it is not a projection.

### Gates skipped and tools unavailable

- **openseo organic metrics.** Dataset-build step 13 is optional and costs credits. It was not run, because a batch over 2,000 credits needs founder approval and none was given. No organic-traffic or keyword figure appears anywhere in this study.
- **Full six-page scrape of discovery vendors.** Dataset-build step 7 was completed for the 55 deep-corpus vendors and only partly for the 35 discovery vendors. Those vendors carry audience, pricing, and citation evidence from the pages actually fetched, and are marked discovery_pass so a reader can see the difference.
- **LibreOffice recalculation.** The source checklist is an xlsx workbook. LibreOffice is not installed on the build machine, so the workbook was read with openpyxl and never recalculated. No figure in this study comes from a workbook formula.
- **Reddit direct access.** Reddit blocks direct fetching from this machine. The 30 threads were captured through a third-party scraping API on 2026-08-13 and are quoted only where hand-verified.
- **Regulatory text verification.** Statutory and regulatory citations marked verify in the source checklist have not been checked against current text. None of them is used as a number in this study.
- Wayback CDX queries rate-limit hard under parallel load. The first pass lost 28 vendors to connection refusals. Those were retried serially with four-second spacing, which recovered most of them. The count above (122 snapshots) is what actually returned, not what was requested.
- Exactly 5 vendor in the pool blocks AI crawlers in robots.txt: mosmedicalrecordreview.com, ecsper.com, provalens.ai, doqtor.io, smartmd.com.

Where this study describes a buyer situation rather than a vendor claim, it is a composite drawn from published sources, not a survey. Composites are labeled at the point of use with the number of sources behind them. No customer, prospect, or call participant is identified.

Review quotes are one excerpt per review, under fifteen words, in straight quotes, linked to the review. Everything else is paraphrased and aggregated into coded themes.

## Full agent brief

Write "AI does the work, the LNC approves: the new reality of legal nurse consulting in 2026" for Legal nurse consultants as part of the hub "State of AI for legal nurse consultants, 2026". Question it answers: When AI does the reading, what is left for the LNC to do, and what does the job look like in 2027? Data: timeline.json (dated launches, 24 months); vendors.json delivery-model and audience flags; Wayback term-adoption 2024/2025/2026; audience-specific regulatory and professional-duty sources. Vendor set: InPractice, Case Chronology, MediSums, OctopusLM, Quench/SmartChart, Superinsight, Parambil, Wisedocs, DigitalOwl, plus 24 corpus profiles in US Med-Legal Review / Legal Nurse Consulting; discovery pass: 'legal nurse consultant AI software', 'medical chronology software for nurses', 'LNC chronology tool'. Floor: 15 named vendors. Audience-specific sources: AALNC and NACLNC guidance; legalnurse.com content and owned research at ~/Desktop/lnc-research/deliverables/; LNC rate surveys (verify). Must include: Cover with thesis headline and method subtitle; founder note; five stat-led sections: (1) what AI now reads and produces, with a dated launch timeline; (2) what stays with the human by rule or professional duty; (3) the role shift or substitution risk, quantified from vendor audience flags; (4) the economics (AI per-page pricing vs the audience's hourly or fee model); (5) the 2027 workflow stage map; named vendor table; methodology; CTA. Audience-specific instructions: Section 3 quantifies substitution risk: vendors naming LNCs vs vendors selling chronology directly to attorneys. Section 4: AI per-page bands vs LNC hourly rates and the arithmetic of 'AI plus LNC review' pricing. Section 5: the 2027 LNC as reviewer, verifier, and expert on what the record does not say. Fold in 'Where LNCs fit after 2026'. Visuals: Launch timeline; role diagram (AI does / human approves); cost curve; 2027 workflow stage map; big-number blocks. Gate before publishing: At least 10 dated launches in the last 24 months from vendors that name this audience; vendor floor met. CTA: Chronology generator (generate_chronology): generate one, then check it the way you would check a junior's. Until the free tool ships, use the product CTA. Rules for every piece: secondary research only, every number is a count over named vendors with a capture date and source URL; name every vendor counted, with URL; write 'vendors claim X', never 'tools do X'; review quotes under 15 words, straight quotes, linked, otherwise paraphrase and aggregate into coded themes; no prospect or customer identities (anonymized composites only); unslop style (no em dashes, sentence case headings, straight quotes, no puffery, active voice); say de-identification or redaction, never de-anonymization; deliver as one HTML page with inline SVG charts on Medrecords tokens (paper #F7F8F3, green #2F6B50, slate #3D5A73, Newsreader / Public Sans / IBM Plex Mono); include a methodology section (vendor list, capture dates, rubric, limitations); review as an artifact before any hand-off to ~/Desktop/medrecords-site/reports/; never deploy without authorization.