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Reviewed case facts are matter-specific facts and findings that lawyers have checked against the underlying documents and witness accounts, while preserving qualifications, contradictions, missing information, and a record of who reviewed what. The phrase is a practical workflow description, not a formal legal doctrine or universally defined industry standard.
That distinction matters because an AI system can produce a fluent summary from incomplete or misleading inputs. A reviewed factual record gives the system traceable evidence and the team’s documented judgment to work from; it does not verify legal authorities, guarantee accuracy, or replace a lawyer’s responsibility to read and check the law.
What does “reviewed case facts” mean in a legal-AI workflow?
In this context, a reviewed case-fact record is an organized, source-linked account of what the team currently believes happened in a matter and why. Each material finding should point back to documents, testimony, records, or another identified source. The record should also show uncertainty instead of presenting every proposition as settled.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →“Reviewed” means that a qualified member of the legal team examined the source material and confirmed, qualified, or disputed the extracted fact. It does not mean that an AI output is presumed correct because a lawyer glanced at it. Nor does it mean that a fact is true merely because it appears in a pleading or a witness statement.
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The wording is useful for describing a disciplined process, but the available authorities do not establish “reviewed case facts” as a defined doctrine, evidentiary category, or binding technical standard.
Why do reviewed facts matter when lawyers use AI?
AI systems are highly dependent on the material and instructions they receive. If the input is an unverified summary, the system may repeat an allegation as a fact, miss a contradiction, or draw a confident conclusion from a document taken out of context. A reviewed record makes the factual foundation inspectable.
- Traceability: A lawyer can move from a finding to the document page, transaction, transcript passage, or other source supporting it.
- Context: Dates, speakers, document versions, conditions, and qualifications stay attached to the proposition instead of being stripped out during summarization.
- Conflict visibility: Competing accounts and evidence against an allegation remain available for analysis rather than being silently averaged into one narrative.
- Review accountability: The team can see who confirmed a finding, when it was reviewed, and what changed after new material arrived.
- Safer drafting: A drafting assistant can distinguish an established fact from an allegation, inference, open question, or disputed account.
These benefits improve the quality of the information supplied to an AI tool. They do not turn generated text into evidence or remove the need for professional judgment.
What should a reliable reviewed-fact record contain?
A useful record is more than a list of extracted sentences. It combines the proposition, its provenance, its status, and the history of the team’s review.
Source documents and precise references
Store the original files, stable identifiers, relevant page or paragraph locations, and document dates. If a fact comes from a transcript, record the witness, session date, and page or line reference. If it comes from a spreadsheet or database, preserve the file version and the field or calculation used.
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Findings with status labels
Use labels that reflect the state of the evidence, such as confirmed, supported but qualified, alleged, disputed, inferred, or unresolved. A status label should not substitute for explanation: identify what supports the label and what could change it.
Qualifications and scope
Capture limitations that affect interpretation. Examples include an incomplete accounting period, a witness who lacked personal knowledge, a translation issue, an estimate rather than a recorded amount, or a document whose authenticity has not been established.
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Contradictions and evidence for and against
Keep competing accounts side by side. For an allegation of nonpayment, the record might include the invoice, bank entries, correspondence, ledger treatment, and the parties’ different explanations. Suppressing the inconvenient material makes later AI analysis less reliable.
Review history
Record the reviewer, date, action taken, and reason for any change. A later document may alter an earlier conclusion; the system should show both the prior state and the update rather than silently overwriting the history.
How does a team create reviewed case facts step by step?
The exact software will vary, but the control points are consistent. The following sequence keeps extraction, legal judgment, and drafting distinct.
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- Define the matter and question. Set the date range, parties, issues, custodians, and the factual questions the team needs answered. A narrow question produces a more auditable record than an instruction to “summarize the case.”
- Collect and preserve source material. Import the relevant pleadings, contracts, emails, financial records, transcripts, images, and metadata. Preserve originals and note missing or inaccessible items.
- Extract candidate facts. Use search, coding, or AI assistance to identify dates, people, amounts, events, and statements. Treat every extraction as a candidate for review, not a final fact.
- Attach provenance. Link each candidate to the exact source location and retain surrounding context. A sentence copied without its preceding qualification can change the meaning.
- Have a lawyer or designated reviewer assess it. Confirm the proposition, narrow it, mark it as disputed, or reject it. Document the reason, especially when the source is ambiguous or conflicts with another record.
- Map contradictions and gaps. Add the evidence supporting and undermining each material proposition. Create an open question for missing records instead of filling the gap with an assumption.
- Version the record. When new documents arrive, show which findings are affected, who rechecked them, and what changed. Keep an audit trail that can be understood by someone joining the matter later.
- Use the record in AI prompts and drafts. Instruct the system to cite the supplied source, distinguish fact from allegation, and flag unsupported conclusions. Route the resulting work product back through human review.
How are reviewed facts different from AI-generated legal research?
Reviewed facts answer “what does the evidence in this matter show, and how certain is that conclusion?” Legal research answers “what law applies, and what do authoritative sources say?” They interact, but one cannot stand in for the other.
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|---|---|---|
| Primary material | Case documents, testimony, records, and other matter evidence | Statutes, regulations, rules, judicial opinions, and official guidance |
| Main purpose | Build a traceable account of events, positions, and evidentiary support | Identify governing law, tests, elements, and controlling or persuasive authority |
| Typical uncertainty | Conflicting accounts, incomplete records, authenticity, timing, and credibility | Jurisdiction, precedential status, amendments, quotation accuracy, and applicability |
| Required check | Compare the finding with the underlying matter source and record the review | Read and verify the actual authority and confirm that it supports the proposition |
| What AI must not do | Convert an allegation or inference into an established fact | Invent a citation, holding, quotation, or procedural history |
A factual record can help an AI system apply a legal test to the right events, but the team still has to retrieve and read the governing authorities. A citation generated by a model is a lead to verify, not proof that the cited law exists or says what the draft claims.
What does the Mary–Harvey connector illustrate?
On October 21, 2026, Mary Technology announced a connector with Harvey. According to the company announcement, Mary reads and maps uploaded matter documents before a user’s first question, carries accumulated findings and review into Harvey, and lets lawyers confirm findings while preserving qualifications, conflicting accounts, and audit history. The announcement also says the workflow can suggest updates when new documents affect existing work.
Those are vendor descriptions, not independent performance findings. They show the kinds of controls a reviewed-fact workflow may aim to provide: source mapping, continuity of team knowledge, explicit qualification, contradiction handling, and change tracking. They do not establish that the connector is more accurate, faster, or better at predicting case outcomes than another system.
Mary reported that its software was used by more than 4,000 lawyers across more than 150 firms. That is company-reported adoption context from 2026; it is not a measurement of factual accuracy, legal research quality, efficiency, or litigation results.
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What did the Louisiana sanctions case teach about verification?
In In re: Sanctions Order of Kenney, Kerry, decided by the Louisiana Fifth Circuit on October 23, 2025, the court considered filings containing fabricated case citations, a fabricated case summary, misrepresented holdings, and an altered quotation. The opinion recounts that counsel relied on AI-generated summaries and failed to make an objectively reasonable inquiry into the cited law. The appellate court affirmed sanctions under Louisiana Code of Civil Procedure article 863.
The court’s warning is narrow but important: “Reviewing AI-generated case summaries is not the same as reading legal cases.” It also stated, “The lawyer’s obligation under Article 863 is crystal clear: read the law you cite. Read the Code. Read the statutes. Read the cases.”
This is a Louisiana decision based on its record and governing law. It did not test Mary, Harvey, or any reviewed-fact product, and it should not be treated as a universal rule governing every jurisdiction. Its practical lesson is that a carefully reviewed factual record cannot replace direct verification of statutes, cases, quotations, or legal propositions.
How can a firm preserve review quality as a matter changes?
Technology helps only when the team defines ownership and review triggers. Establish a written protocol before a large document set is analyzed.
- Assign responsibility: Name the lawyer or senior reviewer responsible for each issue, not just the person who ran the AI extraction.
- Separate evidence from interpretation: Store the source statement, the team’s factual finding, and any legal inference as distinct fields or notes.
- Require source-linked outputs: Drafts should point back to the reviewed record and identify unsupported assertions for human resolution.
- Log material changes: New testimony, amended pleadings, corrected records, or newly discovered documents should trigger review of affected findings.
- Preserve disagreement: If lawyers differ about a fact or its significance, record the competing views and their support instead of forcing premature consensus.
- Control access and confidentiality: Apply the matter’s security, retention, and privilege rules to uploaded documents, generated outputs, and audit logs.
- Test before reliance: Sample findings against source documents, including difficult scans, tables, contradictory accounts, and documents with handwritten or unusual formatting.
How should you evaluate a legal-AI system’s reviewed-fact capability?
Ask for demonstrations and documentation against concrete controls rather than accepting a general claim that the system “understands the case.”
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| Evaluation question | Evidence to request |
|---|---|
| Can users trace a finding to its source? | Stable document and page references, context around the excerpt, and exportable provenance |
| Are contradictions visible? | Side-by-side accounts, evidence-for-and-against views, and unresolved-issue tracking |
| Can lawyers qualify or reject an extraction? | Explicit status fields, reviewer identity, comments, and approval history |
| What happens when new documents arrive? | Change detection, affected-finding alerts, and versioned updates rather than silent replacement |
| Can the team audit activity? | Timestamped logs showing imports, edits, confirmations, and generated outputs |
| What has been independently measured? | Clearly scoped, reproducible results with population, method, date, and limitations; vendor adoption counts alone are not performance evidence |
Also test failure behavior. A system that clearly labels “not found,” “conflicting,” or “needs review” is safer than one that supplies a plausible sentence without showing its support.
What should lawyers remember before relying on an AI-generated case narrative?
Use the narrative as a working aid, never as the evidentiary or legal source itself. Check every material factual assertion against the reviewed record, then check every legal proposition against the actual authority. Keep allegations, disputed testimony, inferences, and established findings visibly distinct. If a proposition cannot be traced or verified, remove it, qualify it, or assign it for investigation rather than allowing fluent wording to conceal the gap.
Frequently Asked Questions
Is “reviewed case facts” a recognized legal term of art?
No. The phrase is a practical description of a documented review process, not a formally defined doctrine shown in the cited materials. Treat it as workflow language and define it for your team or matter.
Can a lawyer rely on an AI summary if another lawyer reviewed it?
Only as a starting point, subject to the firm’s normal verification duties. The reviewer should check the underlying documents and independently read every statute, case, quotation, and rule used in the final work.
What is the best way to handle a witness account that conflicts with documents?
Keep both accounts, identify the documents that support or undermine each one, and label the issue as disputed or unresolved. Do not let an AI system collapse the conflict into a single unqualified statement.
Does a vendor’s user-count figure prove its legal AI is accurate?
No. A reported number of lawyers or firms indicates claimed adoption, not correctness, efficiency, or improved outcomes. Ask for independently described methods and results before drawing performance conclusions.
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