This is a real research project template built for personal injury attorneys. Walk through the entire pipeline — from uploading medical records to AI-generated settlement demand letters and trial strategy. Everything you see here was created by the platform, not by a paralegal.
The AI wrote these. Every word.
Read what the platform actually produces.
10 medical records in. Two complete attorney-ready reports out. No human editing. No templates. Every citation traced to its source document.
The comprehensive report is 100,000+ characters of medico-legal analysis, trial strategy, and case precedent — generated in minutes.
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Morrison v. [Defendant] — PI Medical Records
Personal Injury | Motor Vehicle Collision | T-bone Impact | Cervical Disc Herniation + Rotator Cuff Tear
10
Source Documents
4
Analysis Scenarios
11
Syntheses
2
Full Reports
This template demonstrates a complete PI medical records analysis for a motor vehicle collision case. The corpus includes EMS run sheets, emergency room records, diagnostic imaging reports, surgical notes, physical therapy records, and specialist follow-ups — the standard record set for a PI case.
Every document was OCR'd and classified automatically. The AI read all 10 documents, then ran 4 targeted analysis scenarios and 11 synthesis operations to produce strategic case intelligence — from a medical chronology table to a draft settlement demand letter to trial exhibit strategy.
No coding or technical knowledge was used at any point. Every scenario, synthesis, and report was created using plain English instructions.
Analysis scenarios
Four targeted analysis scenarios examine the medical records from different angles. Each one reads the entire corpus and extracts specific intelligence. Written in plain English — no code, no query language.
1
Record Completeness Check
Before any analysis begins, the AI inventories what you have and flags what's missing. A PI case lives or dies on its records — if the EMS run sheet is missing or the surgical report was never obtained, you need to know before the defense does. This scenario cross-references every uploaded document against the standard record set for a PI case and tells you exactly where the gaps are.
Review all uploaded documents and identify which of the following record types are PRESENT and which are MISSING. Be specific — cite the document title where found.
Required record types:
- EMS/Ambulance logs (run sheets, arrival timestamps, vitals, narrative)
- Emergency Room records (triage notes, admitting complaints, physician exam notes, nursing logs, discharge instructions)
- Diagnostic imaging REPORTS (X-ray, MRI, CT scan, EMG text reports)
- Operative/surgical reports (surgeon logs, anesthesia records, implant hardware logs)
- Physical therapy logs (initial evaluation, daily SOAP notes, discharge summary)
- Specialist consultation notes (neurologist, orthopedist, pain management)
- Prescription/pharmacy records
- Itemized billing statements (UB-04 or CMS-1500 with CPT/ICD-10 codes)
Output a two-column table: Record Type | Status (PRESENT — document name, or MISSING). End with a summary of critical gaps the attorney needs to address before proceeding.
Actual AI Analysis Output (anonymized)
Finding 1.1 — EMS Documentation Is Complete and Evidentiary Grade
Confidence Level: HIGH
The EMS Patient Care Report [01_EMS_RunSheet_20260312] is the single most evidentiary document in this file, and it is complete. The PCR contains the full chain of timestamps required to establish an unbroken record from incident to hospital: dispatch at 14:22, scene arrival at 14:29, on-scene vitals at 14:31, transport departure at 14:41, and hospital arrival at 14:58. The mechanism narrative documents right-side impact, estimated vehicle speed of 40 miles per hour, significant right-door intrusion, airbag deployment, and patient restraint by seatbelt. The patient's own statement — "A truck ran the red light and hit me on the right side" — is captured verbatim at 14:29, nine minutes post-impact. The document bears crew signatures and supervisor review notation, conferring additional credibility as a business record.
This document's evidentiary value cannot be overstated. It was created before any medical record, before any attorney-client relationship, before any litigation posture was adopted by any party. It reflects raw, contemporaneous observation by a trained first responder who had no stake in the outcome. Every subsequent provider document in the file — four in total that describe the mechanism of injury — corroborates this PCR without variation. For trial purposes, this is the anchor exhibit. There is no counter-evidence to this finding and no caveat of significance. The only minor gap is the EMS notation that the patient "denied loss of consciousness" at scene, which later conflicted with the ER physician's documentation — a discrepancy addressed in detail in Section 2. [01_EMS_RunSheet_20260312; confirmed across Scenarios 5, 6, 9, 10, 11, 13, 16]
Finding 1.2 — Emergency Room Records Are Present but Incomplete: Three Components Missing
Confidence Level: HIGH (for what is present); HIGH (for identification of gaps)
The ER physician note [02_ER_PhysicianNote_20260312] by Dr. [ER Physician] is present and substantively complete in terms of clinical content. It documents the HPI with mechanism corroborating EMS, the attending physician's physical examination, the results of cervical spine X-ray, right shoulder X-ray, and CT head (summarized within the note text), IV ketorolac administration, discharge prescriptions for cyclobenzaprine and ibuprofen, and referral orders for MRI of the cervical spine and right shoulder with orthopedic referral. The note was completed at 17:34, documenting a nearly three-hour ER course.
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AI AnalysisPlain English
2
Causation & Injury Onset
The most critical question in any PI case: did this incident cause these injuries? The AI extracts every piece of evidence connecting the collision to the claimed injuries — exact timestamps, mechanism of injury statements, first complaints across multiple providers, and any gaps or conflicts in the timeline. If the ER notes say one thing and the EMS report says another, this scenario catches it.
Extract all evidence establishing that the incident directly caused the claimed injuries. Focus on the immediate aftermath only.
Extract and cite:
- Exact timestamps: EMS arrival, ER triage, first physician contact
- Mechanism of injury statements: any quote from provider notes describing HOW the injury occurred (e.g. 'patient states they were T-boned'). Flag if these match or conflict with each other.
- First complaints: what the patient reported to EMS vs. the ER vs. first specialist — note any differences
- Objective diagnostic findings: all imaging results (X-ray, MRI, CT, EMG) with specific findings — use the radiologist's exact language. Flag 'acute', 'traumatic', 'herniation' language.
- Neurological baselines: Glasgow Coma Scale scores, cranial nerve exams, loss of consciousness notations
- Radiculopathy indicators: any documented radiating pain, numbness, tingling, or shooting sensations
- Seatbelt/airbag marks or contusion documentation within 48 hours (if auto accident)
Output chronologically by date/time. Cite document and page for every finding.
Actual AI Analysis Output (anonymized)
Finding 2.1 — The Mechanism of Injury Narrative Is Internally Consistent Across All Four Independent Provider Accounts With Zero Conflicts
Confidence Level: HIGH — This is the strongest finding in the entire file
This finding deserves extended analysis because it is the evidentiary centerpiece of the liability case. Four independent provider documents — the EMS run sheet [01_EMS_RunSheet_20260312], the ER physician note [02_ER_PhysicianNote_20260312], the orthopedic consultation note [05_Orthopedist_ConsultNote_20260318], and the PT initial evaluation [06_PhysicalTherapy_InitialEval_20260326] — all describe the same mechanism of injury. These documents were created by four different healthcare providers across a span of 14 days, at four different institutions (EMS field setting, [Hospital] Hospital Emergency Department, Central Ohio Orthopedic Specialists, and Buckeye PT & Rehab), with no opportunity for cross-provider coordination of narrative. The mechanism elements documented across these four accounts are fully consistent: right-side impact, patient as driver, patient restrained by seatbelt, opposing vehicle described as a pickup truck, opponent ran a red light, and immediate onset of neck and right shoulder pain. No element of the mechanism narrative is contradicted or varied across any of the four accounts.
The particular evidentiary power of this consistency derives from the timing of the first account. The patient's own words — "A truck ran the red light and hit me on the right side" — were captured by EMS at 14:29, nine minutes after the collision. At that moment, no attorney had been consulted, no insurance claim had been filed, and no litigation strategy had been formed. This statement is as close to pure, unmediated truth as any personal injury case will ever produce. The fact that every subsequent medical provider, over a span of 14 days and across multiple independent practice settings, documented the same mechanism without variation is not coincidental. It is corroboration of the highest order.
For litigation purposes, the consistency matrix in Appendix C demonstrates this point graphically. Not a single element of the mechanism narrative is contradicted across the four accounts. The element of door intrusion appears only in the EMS record because ER and specialist providers typically document mechanism from patient history rather than scene observation — this is not a conflict, it is simply a difference in information source. Similarly, the specific intersection location (Hamilton Road and Broad Street, Columbus, Ohio) is documented explicitly in the ER note [02_ER_PhysicianNote_20260312] and confirmed by date in all other records. [01_EMS_RunSheet_20260312; 02_ER_PhysicianNote_20260312; 05_Orthopedist_ConsultNote_20260318; 06_PhysicalTherapy_InitialEval_20260326; confirmed across Scenarios 5, 7, 10, 11, 13]
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AI AnalysisCross-validation
3
Damages & Treatment Trajectory
How bad are the injuries, what did treatment cost, and what's the long-term outlook? This scenario extracts every data point that quantifies damages — surgical procedures, specialist prognoses, physical therapy progress, functional limitations, and out-of-pocket costs. The AI builds the evidentiary foundation for your damages argument by pulling directly from the medical records.
Extract all evidence quantifying the physical, functional, and financial impact of the injuries.
Extract and cite:
- Surgical/operative reports: procedures performed, implants or hardware used, post-op restrictions and limitations
- Specialist prognosis statements: any written opinion on long-term outlook, permanency, future treatment needs — quote directly
- Physical therapy records: initial functional baseline scores, pain scale ratings, range-of-motion measurements, documented restrictions on daily activities, discharge functional scores (compare to baseline)
- All dollar amounts: every billing line item, total balances, pharmacy costs. Calculate running total of documented expenses.
- Future medical cost projections: any provider estimate of ongoing or future care costs
- Lost wages or work restrictions documented by providers
Output:
1. Treatment timeline (chronological list of all procedures and visits with dates)
2. Functional impact summary (what the patient cannot do, per provider notes)
3. Financial damages table: Provider | Date | Service | Amount
4. Total documented expenses to date
5. Projected future costs (if documented)
Actual AI Analysis Output (anonymized)
Finding 3: The Right Shoulder Injury Is Surgically Confirmed and Radiologically Characterized as Acute
The right shoulder MRI performed on March 16, 2026 by Dr. Abramowitz identified a full-thickness supraspinatus tear measuring 1.8 centimeters with 0.8 centimeters of retraction. Critically, the radiologist documented no significant fatty atrophy of the supraspinatus muscle belly — a finding that radiologists use to distinguish acute traumatic tears from chronic degenerative tears, as fatty atrophy develops over time in the setting of a long-standing rotator cuff tear. The radiologist's characterization was explicit: *"consistent with acute rather than chronic injury"* [04_RightShoulder_MRI_Report_20260316].
Treating orthopedic surgeon Dr. [Orthopedist], after reviewing the imaging and examining the plaintiff on March 18, 2026, documented that Ms. Doe was *"completely asymptomatic in the right shoulder prior to this accident"* and characterized the tear as *"acute and traumatic,"* directly addressing and dismissing the 2023 PCP visit as *"self-limited and fully resolved"* [05_Orthopedist_ConsultNote_20260318]. Arthroscopic rotator cuff repair was performed on April 2, 2026 at Grant Medical Center (CPT 29827), confirmed by both the billing summary and the physical therapy SOAP notes [09_ItemizedBilling_Summary; 07_PhysicalTherapy_SOAP_Visit3_20260402].
The physical therapy record from May 6, 2026 — eight weeks post-surgery — documents shoulder range of motion below post-operative protocol goals, with the physical therapist noting risk of adhesive capsulitis if the motion deficit is not resolved. The treating surgeon's documentation projects permanent functional limitations [08_PhysicalTherapy_SOAP_Visit8_20260506].
Counter-evidence and caveats: The August 2023 PCP note documenting right shoulder complaints with tenderness at the supraspinatus insertion is the primary counter-evidence and is addressed in detail under Finding 4 below. The operative report from the April 2, 2026 surgery is absent from the record set — this is the single most important document for damages quantification and is a critical gap. The surgical findings as documented by the operating surgeon in that report would provide the definitive intraoperative characterization of the tear and any additional pathology identified. Confidence level: High as to the existence and acute characterization of the tear; assessment of full damages remains incomplete pending operative report production.
Finding 5: Confirmed Economic Damages Total $51,742.84 Through May 15, 2026, with Significant Unquantified Future Damages
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AI AnalysisEvidence Extraction
4
Pre-Existing Conditions & Red Flags
What will the defense use against you? This scenario is deliberately adversarial — it audits the records for every vulnerability that opposing counsel will exploit. Pre-existing conditions, prior injuries to the same body part, gaps in treatment, inconsistent statements, non-compliance with medical advice. The attorney needs to know all of this before the defense does, and the AI finds it faster and more thoroughly than manual review.
Audit the records for vulnerabilities that opposing counsel will exploit. Flag everything — the attorney needs to know this before the defense does.
Check for and cite each:
PRE-EXISTING CONDITIONS:
- Any prior injury, chronic condition, or prior treatment affecting the same body part
- Prior imaging of the same body part — compare radiologist language (degenerative, osteophytes, spondylosis, chronic, wear-and-tear vs. acute, traumatic, herniation)
- Any provider noting the patient was asymptomatic or stable before the incident (Eggshell Skull doctrine support)
- Primary care records showing baseline health status
GAPS IN CARE:
- Any period of 10 or more days with no documented medical visit or PT appointment
- List each gap: From [date] to [date] = [X days]. Note what appointment was before and after.
INCONSISTENT STATEMENTS:
- Compare mechanism of injury descriptions across EMS, ER, specialist, and PT records — flag any discrepancies
- Compare body parts or symptoms reported at different encounters — flag contradictions
NON-COMPLIANCE:
- Any provider note stating patient missed appointments, skipped PT, refused medication, or ignored restrictions
- Quote directly from the record
OTHER RED FLAGS:
- Toxicology results or substance mentions near time of incident
- Other life stressors mentioned (divorce, strenuous job, home renovations) that could explain pain
- Alternative injury events mentioned
Output each category as a separate section. Rate overall vulnerability: LOW / MEDIUM / HIGH with brief justification.
Actual AI Analysis Output (anonymized)
Finding 4: The Pre-Existing Condition Vulnerability Is Substantial but Substantially Mitigated Within the Existing Record
The August 14, 2023 PCP note from Dr. Whitmore at Westerville Family Medicine documents a right shoulder ache of approximately three weeks' duration attributed by the patient to a home painting project involving sustained overhead work. The physical examination documented mild tenderness at the supraspinatus insertion — the precise anatomical location of the 2026 full-thickness tear. This is the most significant vulnerability in the case and will be the centerpiece of any defense strategy [10_PCP_PriorHistory_Note_2023].
The strength of this vulnerability cannot be minimized: defense counsel will argue that the supraspinatus was pre-symptomatic and structurally compromised before the accident, that the 2026 tear represents degenerative progression rather than acute trauma, and that the plaintiff failed to disclose a relevant prior complaint to multiple providers. These arguments have surface-level credibility because the 2023 complaint involved the identical anatomical structure.
However, the mitigating evidence already present in the record is unusually strong. The 2023 clinical examination was negative for rotator cuff pathology on every provocative test performed — negative Neer sign, negative empty can test, full 5/5 bilateral strength — and no imaging was ordered [10_PCP_PriorHistory_Note_2023]. The PCP's own notation states the complaint was "presumed resolved" with no follow-up treatment and no subsequent visits for that complaint. The 2.5-year gap between that single visit and the 2026 accident, during which no right shoulder treatment of any kind is documented, substantially undercuts any argument of an ongoing symptomatic condition. Most significantly, the treating orthopedic surgeon and the radiologist — both independent clinicians with professional reputations to maintain — each independently characterized the 2026 injury as acute and distinguished it from the 2023 history in explicit written documentation [04_RightShoulder_MRI_Report_20260316; 05_Orthopedist_ConsultNote_20260318].
The cervical degenerative changes at C5-C6 constitute a secondary pre-existing condition vulnerability. These changes are documented in the ER X-ray and acknowledged in the MRI report. No prior cervical imaging or cervical complaints are documented anywhere in the record set, so the extent of pre-existing cervical degeneration before the accident cannot be fully quantified from available records [02_ER_PhysicianNote_20260312; 03_CervicalSpine_MRI_Report_20260315].
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AI AnalysisAdversarial Audit
Synthesis operations
Syntheses connect findings across all scenarios to produce strategic intelligence. Where scenarios examine individual data points, syntheses produce actionable work product — the documents an attorney actually uses to work the case.
S
Medical Chronology
The backbone of every PI case. The AI compiles a complete chronological table of every medical encounter — dates, providers, document types, subjective complaints, objective findings, and case impact flags. This goes directly into the settlement demand package. What traditionally takes a paralegal days to compile, the AI produces in minutes with every citation traced to its source.
Using the findings from all scenario analyses, compile the complete Medical Chronology for this personal injury case. This document will be used directly by the attorney for the settlement demand package.
Produce four sections:
SECTION 1 — MEDICAL CHRONOLOGY TABLE
Order all dates of service chronologically. For each entry:
| Date of Service | Provider / Facility | Document Type | Subjective Complaints & Objective Findings | Case Impact / Red Flags |
Include every medical encounter documented across all records.
SECTION 2 — DAMAGES SUMMARY
- Total documented medical expenses (itemized by provider)
- Projected future costs (if any documented)
- Functional limitations with supporting provider quotes
- Lost wage documentation (if present)
SECTION 3 — CASE STRENGTHS
Top 3-5 strongest pieces of evidence for causation and damages. Quote directly from records.
SECTION 4 — VULNERABILITIES & RECOMMENDED ACTIONS
List each red flag identified, its potential impact on settlement value, and the recommended action for the attorney.
End with: OVERALL CASE ASSESSMENT — one paragraph summary of case strength, key damages figure, and top 2 risks.
AI SynthesisCross-scenario
S
Settlement vs. Litigation Assessment
The attorney's first strategic decision: settle, negotiate, or litigate? The AI analyzes all findings and produces a clear recommendation with a viability scorecard — liability clarity, injury severity, documentation quality, pre-existing vulnerability, and jury appeal. It rates each factor 1-5 with specific citations. This isn't a guess — it's a data-driven assessment built from the actual medical record analysis.
Based on all scenario findings and the medical chronology, make the attorney's first strategic decision: recommend the optimal case trajectory.
SECTION 1 — CASE TRAJECTORY RECOMMENDATION
State clearly: SETTLE EARLY / NEGOTIATE (demand then litigate if needed) / LITIGATE IMMEDIATELY
Provide the top 3 reasons supporting this recommendation with specific citations.
SECTION 2 — SETTLEMENT VIABILITY SCORECARD
Rate each factor 1-5:
- Liability clarity
- Injury severity and documentation quality
- Treatment completeness (is the client at maximum medical improvement?)
- Damages total vs. likely policy limits
- Red flag exposure
- Overall settlement viability score (average)
SECTION 3 — KEY RISKS BY PATH
If settling: minimum acceptable demand, what the adjuster attacks first.
If litigating: top 3 defense vulnerabilities, what needs shoring up.
Close with: RECOMMENDED NEXT ACTION — exactly what to do in the next 48 hours.
AI SynthesisStrategic Analysis
S
Settlement Demand Letter Draft
A complete, ready-for-review settlement demand letter. The AI pulls the most compelling quotes directly from the medical records — the EMS narrative, the ER notes, the surgeon's language, the PT's functional limitations. It structures the letter with a humanizing opening, medical narrative, damages calculation, and a demand. The attorney personalizes and sends. Not a template — a case-specific draft built from the evidence the AI already analyzed.
Using all medical findings, damages figures, and case strengths — draft a complete Settlement Demand Letter ready for attorney review.
Structure:
[DATE]
[Insurance Company Name / Adjuster Name]
Re: Our Client: Jane A. Morrison | Date of Loss: March 12, 2026
OPENING — THE HOOK
One powerful paragraph humanizing the client. Pull the most visceral, emotionally compelling quotes directly from the medical records.
LIABILITY
Concise statement of fault with police report reference and mechanism of injury documentation.
INJURIES AND MEDICAL TREATMENT
Full treatment trajectory in chronological order. Highlight surgery, radiculopathy, functional limitations.
SPECIAL DAMAGES (ECONOMIC)
Itemized table: Provider | Dates of Service | Amount
TOTAL PAST MEDICAL EXPENSES: $X
FUTURE MEDICAL EXPENSES: $X
GENERAL DAMAGES (NON-ECONOMIC)
Pain and suffering narrative using PT notes and functional limitations. Apply reasonable multiplier (3-5x specials).
TOTAL DEMAND: $[AMOUNT]
DEADLINE: 30 days. Failure to respond = immediate filing.
Tone: Professional, firm, factual. Every dollar figure sourced from records.
AI SynthesisDocument Generation
S
Deposition Preparation Outline
Using the red flags identified in the Pre-Existing Conditions scenario, the AI builds a complete deposition prep package. For each vulnerability: the exact attack question the defense attorney will ask, the honest but defensively framed answer to coach the client on, and the medical record that backs up the client's position. Also includes a defense expert attack strategy — anticipating what their IME doctor will say and how to counter it.
PART 1 — CLIENT DEPOSITION PREPARATION
Top 5 vulnerabilities the defense will attack:
1. The 2023 right shoulder complaint at the same anatomical site
2. The LOC discrepancy (EMS "denies LOC" vs ER "brief LOC reported")
3. The 18-day gap in PT (April 22 and April 29 missed appointments)
4. Non-compliance with home exercise program
5. Any gaps between the accident and first treatment
For each: the attack question (verbatim), the coached answer, and the medical record support.
PART 2 — DEFENSE MEDICAL EXPERT ATTACK OUTLINE
For each likely defense expert claim:
- The expected defense opinion
- The objective record evidence that contradicts it
- The cross-examination question to nail the contradiction under oath
Focus: degenerative vs. acute herniation, supraspinatus tear chronicity, disputed LOC, treatment compliance.
Close with: THREE QUESTIONS NEVER TO ASK the defense expert.
AI SynthesisTrial Prep
S
Expert Witness Engagement Brief
A concise brief for the retained medical expert — orthopedic surgeon or life-care planner. Includes a case summary written for a medical professional (not a layperson), a complete list of records provided, the specific opinions sought, and a candid section on defense arguments the expert will need to address. Saves hours of attorney time preparing expert materials.
SECTION 1 — CASE SUMMARY FOR EXPERT REVIEW
One-page summary written for a medical professional.
SECTION 2 — RECORDS PROVIDED
All 10 documents with date, provider, type. Note missing records.
SECTION 3 — OPINIONS SOUGHT
1. Causation: MVC caused C5-C6 herniation with C6 radiculopathy
2. Causation: MVC caused full-thickness supraspinatus tear
3. Pre-existing: 2023 shoulder complaint does not represent pre-existing tear
4. Prognosis: long-term functional limitations expected
5. Future care: estimated future medical costs
SECTION 4 — THE MAGIC WORDS REQUIREMENT
Expert MUST use "to a reasonable degree of medical certainty" for each causation opinion.
SECTION 5 — RED FLAGS TO ADDRESS
Vulnerabilities the defense will attack — address proactively in expert report.
AI SynthesisExpert Materials
S
Trial Exhibit & Witness Strategy
If the case can't settle, here's the trial plan. The AI identifies the 5-7 most compelling exhibits from the records, explains what each proves in plain English a juror can understand, and recommends presentation format (enlarged print, side-by-side comparison, demonstrative). Includes direct examination questions for treating physicians and cross-examination strategy for the defense IME doctor. Complete witness order recommendation with strategic reasoning.
SECTION 1 — POWER EXHIBITS (Jury-Ready Evidence)
Top 5-7 compelling exhibits. For each:
- Exhibit label (e.g., Plaintiff's Exhibit A)
- Document source
- What it shows in plain English a juror can understand
- Recommended presentation format (enlarged print, callout, side-by-side, demonstrative)
- The one sentence the attorney says to the jury when showing it
SECTION 2 — TREATING PHYSICIAN WITNESS LIST
Which providers to subpoena for live testimony vs. deposition transcript. Key direct examination questions. Anticipated defense cross topics.
SECTION 3 — DEMONSTRATIVE EVIDENCE
- Which imaging warrants a medical illustrator (3D surgical illustration, herniation diagram)
- Day-in-the-life video recommendations (based on PT functional limitations)
- Timeline demonstrative for opening statement
SECTION 4 — JURY NARRATIVE
One-paragraph opening statement theme in plain human terms. Every fact supported by a specific record.
SECTION 5 — VERDICT RANGE ESTIMATE
- Conservative (defense-friendly jury): $X
- Likely (neutral jury): $X
- Optimistic (plaintiff-friendly jury): $X
AI SynthesisTrial Strategy
S
Applicable Law & Case Precedent
Legal research specific to this case — Ohio negligence standards, proximate cause for auto accidents, Eggshell Skull doctrine for pre-existing conditions, cervical disc herniation verdict ranges, and statutory caps on non-economic damages. The AI searches for applicable case law, cites relevant precedent, and summarizes how each applies to this specific fact pattern. This isn't generic legal research — it's targeted to the exact injuries and jurisdiction.
Research applicable law and legal precedent for this PI case: T-bone MVA → acute cervical disc herniation (C5-C6) with C6 radiculopathy + full-thickness rotator cuff tear requiring surgical repair.
1. CAUSATION STANDARDS
- Ohio negligence and proximate cause standards for auto accident PI
- Eggshell Skull / Thin Skull doctrine in Ohio
- Case precedent: pre-existing degenerative spine + acute traumatic event
2. DAMAGES STANDARDS
- Ohio economic damages: medical expenses (past and future), lost wages
- Ohio non-economic damages: pain and suffering caps, calculation methods
- Rotator cuff repair and cervical disc herniation verdict/settlement ranges in Ohio
3. DEFENSE VULNERABILITIES
- Ohio case law on treatment gaps
- Patient non-compliance effect on damages
- Pre-existing condition defense limitations
4. EXPERT WITNESS REQUIREMENTS
- Ohio rules on medical expert testimony
- "Reasonable degree of medical certainty" standard
- Treating physician vs. retained expert admissibility
For each area: cite specific cases with full legal citation, summarize holding, explain application to Morrison facts.
AI SynthesisLegal Research
AI-generated reports
Reports aren't just a dump of scenario results. The AI performs its own additional analysis and synthesis on all collected data to produce coherent, professional documents. Reports can be regenerated as new data is added, or saved as-is for historical reference.
R
Executive Summary Report
A concise, attorney-ready report covering the key findings, case strengths and vulnerabilities, strategic recommendation, and damages overview. Designed for the attorney who needs to understand the case in 10 minutes. Approximately 17,000 characters of focused analysis with citations to source documents throughout.
Classification: Attorney-Client Privileged / Attorney Work Product Sections: Abstract → Record Inventory → Causation Analysis → Damages Summary → Red Flags → Strategic Recommendation
The full analysis — a publication-ready, comprehensive medico-legal report covering every aspect of the case in detail. Over 100,000 characters of deep analysis including complete medical chronology, causation evidence chain, damages quantification, pre-existing condition defense strategy, settlement demand framework, deposition preparation, expert witness requirements, trial exhibit strategy, and applicable Ohio case law.
This report is generated by the AI reading all scenarios, all syntheses, all findings, and all source documents — then producing its own integrated analysis with additional cross-referencing and strategic insights not found in any individual scenario. It's not a concatenation of results. It's a new analytical product.
Legal research can't afford hallucinations. AI models make things up — invented case citations, swapped plaintiff and defendant names, fabricated holdings. We built a multi-layered verification system specifically to eliminate this.
3x
Triple Verification — Pro, Antagonist, Neutral
Every critical analysis runs three times through three different lenses. The Pro pass builds the strongest case for the finding. The Antagonist pass tries to tear it apart — actively looking for contradictions, weak sourcing, and logical gaps. The Neutral pass evaluates both positions without advocacy. Only findings that survive all three passes are marked as verified.
This isn't just running the same query three times. Each pass has different instructions, different evaluation criteria, and different objectives. The system detects hallucinations by cross-checking claims across passes — if the Pro pass cites a case that the Antagonist pass can't find in the corpus, it's flagged immediately.
The verification framework is fully customizable in plain English. Adjust the lenses, add domain-specific checks, or create entirely new verification patterns — all through natural language instructions.
AI VerificationHallucination DetectionCustomizable
⚠
Legal-Specific Error Prevention
General-purpose AI models make errors that are catastrophic in legal work. Our system has built-in safeguards for the errors that matter most in legal research:
✗ Swapped parties — AI confuses plaintiff and defendant names, attributes claims to the wrong side. ✓ Our system validates party attribution across all outputs.
✗ Invented citations — AI generates case names, docket numbers, or holdings that don't exist. ✓ Triple verification cross-checks every citation against the actual corpus.
✗ Merged case facts — AI blends facts from different cases or different parties into one narrative. ✓ Source attribution traces every claim back to a specific document and page.
✗ Fabricated holdings — AI states a court ruled one way when it ruled the opposite (or never ruled at all). ✓ Adversarial pass specifically challenges legal conclusions.
✗ Confident nonsense — AI presents fabricated information with high confidence and no hedging. ✓ Confidence scoring + bias detection flags unsupported certainty.
⚡
Multi-Model Intelligence
Different tasks need different levels of AI capability. We use multiple AI models strategically — matching the right model to the right task to optimize both accuracy and cost.
You choose the model tier per scenario, or let the system select automatically based on task complexity. We're adding specialized models frequently — including specialized models optimized for sensitive data. Cost management is built in: routine work runs on efficient models, critical analysis gets the deep thinkers.
Multi-ModelCost Optimization
⚡ Highly Recommended Reading
How we combine triple verification, multi-model intelligence, bias detection, hallucination prevention, and cost management into a unified AI reliability system — and how it compares to LexisNexis, Westlaw, and other legal research platforms.
Everything you just read — every scenario, every synthesis, every finding, every source citation — the AI chat knows all of it. It's not a search bar. It's a research partner that has read every document in your project, understands every conclusion it drew, and can take it further on demand.
The chat takes the research further
The reports and scenarios are the foundation. The chat is where research becomes interactive — where you push deeper, challenge assumptions, and produce work product you didn't even plan for.
💬 Build and refine the project through conversation
Don't know where to start? Tell the AI what you're working on and it helps you design the research — what scenarios to run, what to look for, what questions to ask. It suggests angles you haven't considered based on what it's already found in the documents.
I just uploaded 10 medical records for a PI case. Where should I start?
I've already scanned all 10 documents. You have strong EMS and ER documentation, but I noticed you're missing the operative report for the April 2 surgery. Before we run a full analysis, I'd recommend requesting that — it's critical for damages quantification. Want me to run a completeness audit first, then build the analysis scenarios?
Yeah, run the audit. Then give me the full workup.
Done. Completeness audit found 3 missing record types. I've queued 4 analysis scenarios and 11 synthesis operations. Running now — I'll have everything ready in about 8 minutes.
🎯 Create unbiased scenarios and synthesis
The AI doesn't advocate for your position — it finds the truth in the data. Ask it to run an adversarial analysis and it will find every weakness the defense will exploit. Ask it to build valuation scenarios and it gives you conservative, moderate, and aggressive ranges with evidence for each. No confirmation bias. No selective reading.
The defense is going to argue degenerative changes. What's our strongest counter from the imaging?
The MRI from 3/15 shows an acute disc herniation at C5-C6 with nerve root compression — the radiologist specifically used "acute" and "traumatic" language, distinguishing it from degenerative changes. Cross-reference with the ER notes from 3/12 showing no prior cervical complaints. I'd lead with the radiologist's own language — it's hard to argue "degenerative" when their own expert wrote "acute."
Now play devil's advocate. What's the defense's best argument and how strong is it?
Their best shot is the August 2023 PCP note — right shoulder tenderness at the supraspinatus insertion, same location as the 2026 tear. Surface-level it's compelling. But it's substantially mitigated: all provocative tests were negative, no imaging was ordered, the complaint self-resolved with no follow-up, and there's a 2.5-year gap with zero shoulder treatment. I'd rate it medium vulnerability — annoying but defensible. The bigger risk is the missing operative report.
📝 Generate reports and documents on demand
Need a settlement demand letter? A case memo for a partner? An expert witness outline? A client status update? The AI drafts it using everything it knows about the case — every medical finding, every dollar amount, every citation. Not a template. A document written from the actual evidence.
Draft me a one-paragraph response for the demand letter on the degenerative changes argument.
Done. I've also flagged two additional imaging references from the orthopedic follow-up that support this position — want me to add those?
Yes. And draft the full demand letter while you're at it — use the $51,742 documented specials and the $325K-$425K range from the valuation synthesis.
Full demand letter drafted — 8 pages with citations to specific medical records, itemized damages table, and three settlement scenarios. I've included the pre-existing condition rebuttal section since the defense will raise it. Ready for your review.
Not just legal. Any research, any field.
This example shows PI medical research, but the platform works for any kind of research project. The same pipeline — upload documents, AI analysis, interactive chat, comprehensive reports — adapts to any discipline.
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Legal Research
Case law analysis, contract review, regulatory compliance, discovery processing, deposition prep, workers' comp medical records — any legal research project with documents to analyze.
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Medical & Clinical
Literature reviews, clinical trial analysis, treatment comparisons, diagnostic research. The AI reads medical literature and synthesizes findings across hundreds of papers.
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Academic & Scientific
Thesis research, systematic literature reviews, citation analysis, methodology documentation. Academic-ready output with proper source attribution.
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Business Intelligence
Market research, competitive analysis, due diligence, vendor evaluation, M&A analysis. Feed it data and get structured, actionable conclusions.
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Investigative
Journalism, fraud investigation, compliance audits, whistleblower cases. Organize scattered sources into coherent intelligence with full audit trails.
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Literary & Creative
Novel research, historical accuracy, world-building, biographical research. From a personal passion project to a professional publication — the same tools apply.
From one page to thousands
This example uses 10 source documents. The same platform handles projects with hundreds or thousands of documents — years of medical records, boxes of discovery materials, entire research libraries. The OCR pipeline processes everything automatically, and the AI scales its analysis to match the corpus size. And the chat is always there — your always-on research assistant that has read everything and remembers every detail.
Want to see the full demo?
We'll walk you through a live research project with your own data.