PDFs weren’t designed for structure—they were designed for print. That’s why trying to extract tables, forms, and other structured data from PDFs often feels like untangling a knot made of numbers and labels. What should be a simple process ends up costing hours of manual copying, formatting, and frustration.
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The good news? AI is finally smart enough to help. With the right tools, you can now convert messy, unstructured PDFs into clean, structured data formats like CSV, JSON, or databases—automatically.
This post explores how to use AI to extract structured data from PDFs faster, cleaner, and with far less cognitive overhead. If your goal is to spend less time organizing and more time analyzing, you’re in the right place.
📊 Why Structured Data from PDFs Matters
PDFs are everywhere—from business contracts and tax forms to medical research and shipping invoices. They’re the universal document format of the digital age. But here’s the catch: they weren’t built for data work. Most PDFs were designed for presentation, not computation. This creates a disconnect between what we need from a document (structured data) and how that document was built (static layout).
Every time you try to extract data manually from a PDF, you're essentially fighting its original purpose. Highlighting rows in a table, copying them into Excel, fixing the formatting—it’s a slow, error-prone process. And in industries like logistics, finance, or law, where hundreds of documents pile up daily, manual extraction just doesn’t scale.
That’s where structured data becomes essential. Structured data is machine-readable, searchable, and ready for analysis. It allows systems to process information programmatically—feeding dashboards, triggering automations, or supporting real-time decision-making. Without structure, data is just noise.
Imagine a scenario in which a supply chain analyst needs to pull shipment dates and quantities from 500 freight invoices. With structured data, this becomes a 10-minute job. Without it, they’re stuck in a two-day slog of Ctrl+C and Ctrl+V. The difference isn’t just speed—it’s sanity.
This isn’t just a corporate issue. Researchers, journalists, educators—all of them depend on structured content to analyze patterns, cross-reference sources, or build narratives. PDFs filled with tables, figures, or bibliographic data hold valuable insights that remain locked away unless properly extracted.
In my experience, once you start treating PDFs as raw input for a structured data pipeline, your workflow transforms. You stop reading everything manually and start orchestrating processes. Your brain moves from reading to reasoning.
There’s also an accessibility benefit. When data is structured, it can be more easily converted into accessible formats for people with visual impairments, or used in multilingual translation pipelines. What seems like a technical convenience is also a bridge to inclusivity.
This shift aligns perfectly with the RoutineOS philosophy: reduce mental clutter, design intentional systems, and focus only on high-value thinking. Extracting structured data from PDFs isn’t just a tech skill—it’s a strategic upgrade to your digital routine.
When your files are structured, tools like Zapier, Airtable, Notion, and Python scripts become exponentially more powerful. They can do the work you used to do manually—only faster, cheaper, and without burnout.
Ultimately, structured data unlocks leverage. Whether you’re automating a report, analyzing survey responses, or building a product database, the structure is what lets you scale. Without structure, every PDF is a dead end. With it, every PDF becomes a launchpad.
🧮 Manual Extraction vs Structured Data Workflow
| Process | Manual Extraction | AI-Powered Structured Data |
|---|---|---|
| Speed | Slow and repetitive | Fast and automated |
| Accuracy | Prone to human error | Consistent and reliable |
| Scalability | Limited by human effort | Processes thousands of files |
| Focus | Wasted on formatting | Spent on analysis |
| Energy Cost | High cognitive load | Low mental overhead |
As the table shows, structured data is a strategic multiplier. It doesn’t just make tasks faster—it changes what’s possible in your routine. When your files flow through a reliable, automated system, you reclaim your attention for things that actually require thought.
🤖 How AI Understands and Structures PDF Data
At first glance, a PDF looks like a simple file. But beneath the surface, it’s a jungle of page coordinates, font tags, and scattered layout elements. Unlike HTML or JSON, PDFs don't naturally expose relationships between data points. They're more like photographs of documents than databases. That’s what makes PDF extraction such a complex task for both humans and machines.
So how does AI make sense of it? The secret lies in how modern language models and machine learning systems parse context. Tools like ChatGPT, Claude, and specialized platforms like Tabula or Docparser don’t just read line by line—they analyze the visual and structural layout of the document.
Many AI tools now combine natural language processing (NLP) with layout recognition. This means they consider font size, table alignment, header patterns, whitespace, and even line spacing to detect structure. They don’t just read words—they interpret intent and layout.
For example, an AI might recognize a shipping invoice as having a table at the bottom, addresses at the top, and notes in a footer. It doesn't see this the way a human does visually—but through patterns in positioning and repetition. This structural intuition is what allows AI to convert free-form documents into usable data formats.
In technical terms, tools use models like X-Y coordinate extraction, entity recognition, and sometimes even OCR (optical character recognition) to handle scanned or image-based PDFs. Hybrid tools like Rossum or Nanonets blend these approaches for more complex use cases like medical billing or multi-language contracts.
Once the layout is understood, the next step is normalization. That’s when raw data is converted into consistent formats: dates into ISO format, prices into decimals, names into proper casing, etc. This cleanup step is what turns messy into meaningful.
What’s remarkable is how fast this can happen. A document that would take a person 45 minutes to comb through can be processed by an AI tool in seconds. And with each iteration, these tools get better—not just at accuracy, but at adapting to new document styles.
There’s also the advantage of scale. AI doesn’t get tired, distracted, or inconsistent. Once a rule or pattern is learned, it can be applied across thousands of files with identical precision. This consistency is what makes AI ideal for routine data workflows.
Behind every successful AI PDF tool is a combination of layout intelligence, language modeling, and a bit of human feedback. Most platforms allow users to correct mistakes, which in turn refines the algorithm over time. The result? A smarter system that actually learns your structure preferences.
Whether you're working with receipts, invoices, research papers, or legal briefs, understanding how AI reads documents helps you get better results. Knowing what the AI is “looking for” lets you prompt it more effectively—and trust it more confidently.
🔍 How AI Analyzes PDF Structure
| AI Technique | What It Does | Common Use |
|---|---|---|
| Layout Analysis | Detects tables, sections, headers, footers | Invoices, forms |
| NLP (Natural Language Processing) | Understands sentence meaning and context | Reports, summaries |
| OCR (Optical Character Recognition) | Reads text from scanned images | Receipts, scanned contracts |
| Entity Extraction | Identifies names, dates, prices, addresses | Financial docs, resumes |
| Normalization | Converts data into consistent formats | Databases, CSV exports |
As this table shows, AI doesn't just "read" PDFs—it decodes them on multiple levels. When these layers work together, even the most chaotic documents become sources of clean, actionable information.
🛠️ Best AI Tools for Structured PDF Extraction
Choosing the right AI tool for structured PDF extraction depends on your specific needs—volume, document type, export format, and workflow integration. The market is growing fast, but a few tools consistently stand out for their accuracy, flexibility, and automation capabilities.
1. Tabula is a long-time favorite among data journalists and analysts. It’s open-source, fast, and works best with native (non-scanned) PDFs that include clean tables. Although it lacks OCR and cloud features, it’s a reliable local solution for simple tabular extraction.
2. Docparser brings more sophistication to the table. It allows users to define parsing rules using visual templates, which makes it ideal for businesses processing standard forms like purchase orders, shipping documents, or utility bills. Its integration with Google Sheets, Dropbox, and Zapier makes it a strong candidate for routine automation.
3. Rossum targets enterprise-scale document automation with AI models trained specifically for invoice extraction, procurement, and finance. It also includes user correction workflows—so if the model makes a mistake, you can teach it to improve next time. This feedback loop is what makes Rossum popular in corporate environments.
4. Nanonets is a versatile, API-first platform that supports both image and text-based PDFs. It offers strong OCR capabilities and pre-trained models for receipts, IDs, and logistics data. Nanonets can be deployed via API or web dashboard, which makes it a flexible choice for developers and non-technical teams alike.
5. Adobe Acrobat AI Assistant—yes, even Adobe is getting in the game. Their newer AI-powered search and summary feature can help extract key facts and phrases, though it’s still limited in data formatting and automation.
6. ChatGPT + Code Interpreter (Advanced Data Analysis) is perfect for custom, conversational PDF extraction. You can upload a PDF directly, ask for structured data, and get tables in return. While not scalable for batch processing, it’s powerful for one-off insights and personalized data interactions.
Each of these tools serves a different user type. Tabula is best for analysts; Rossum for finance teams; ChatGPT for knowledge workers. What unites them is the goal: to transform static documents into dynamic, usable data.
What I’ve found most effective is combining tools. For example, use Adobe Scan or Nanonets for OCR, then route output to ChatGPT or Docparser for formatting. Think of your AI toolset as a pipeline—not a single product.
And don't overlook usability. Some tools have steeper learning curves but deliver higher accuracy. Others are more user-friendly but offer less control. Testing with your actual documents is the best way to know which tool fits your workflow.
📌 Comparison of Top AI PDF Extraction Tools
| Tool | Key Strength | Best For | OCR Support | Automation |
|---|---|---|---|---|
| Tabula | Clean table extraction | Analysts, data journalists | No | Limited |
| Docparser | Rule-based parsing | SMBs, back-office ops | Yes | Zapier, webhook |
| Rossum | AI with feedback loop | Finance, enterprise | Yes | High (API) |
| Nanonets | Versatile + OCR | Developers, logistics | Yes | Webhook, API |
| ChatGPT + Code Interpreter | Conversational extraction | Researchers, knowledge workers | Partial (via plugin) | Manual |
As shown above, each tool has a sweet spot. The key is to match the tool to your routine—not force your routine to adapt to the tool. For complex pipelines, combining tools often gives you the best of both speed and precision.
🔄 Building a Repeatable Extraction Workflow with AI
A one-time data extraction is helpful, but the real power of AI comes when you set up a repeatable workflow. In most organizations or personal systems, PDF-based data flows are not one-offs—they’re recurring. Whether you're dealing with monthly invoices, regular lab results, weekly delivery sheets, or student reports, repeatability is what separates busywork from smart work.
The first step is source consistency. Ensure that your incoming PDFs follow a somewhat stable layout. Even small layout consistency helps AI tools map and extract data accurately. This doesn’t mean you need perfect templates—but recurring invoices from one vendor or statements from a single platform often follow a predictable format that’s ideal for automation.
Next, choose a tool that allows for rule-setting or template learning. Tools like Docparser or Rossum let you set parsing zones, keyword-based triggers, and formatting templates. Once defined, these rules don’t just extract data—they normalize it. A date becomes ISO format. A currency string becomes a float. This is where automation begins to think like you.
Then comes integration. The biggest productivity gain happens when your extracted data doesn’t just sit in a spreadsheet—but flows somewhere useful. Connect your AI tool to Airtable, Notion, Google Sheets, or Zapier workflows. For example, extracted sales data can trigger an email report or auto-update a dashboard. It’s not about extraction—it’s about flow.
Testing and feedback is the next vital stage. Don’t assume your setup is perfect after one test. Use a small batch of 5–10 documents, then review for false positives or errors. Most AI tools allow you to fine-tune parsing rules or retrain layouts. This feedback loop makes your system smarter over time.
From a cultural perspective, creating a repeatable workflow shifts how you think about documents. You stop seeing PDFs as something to “handle” and instead view them as structured resources. This mindset shift is powerful. It enables scale, reduces friction, and aligns with intentional digital living.
My own RoutineOS workflow involves uploading PDFs to a Google Drive folder, where Nanonets processes them and pushes the structured data to Notion. I’ve set up daily syncs using Make.com, and the system runs itself. My job is no longer data entry—my job is insight.
The final step? Documentation. Document your own workflow—what tools are used, what formats are needed, what triggers what. This makes handover easier, improves debugging, and allows future scaling. A well-documented AI workflow is a scalable asset.
RoutineOS isn't just about minimalism—it’s about deliberate structure. And building a PDF extraction workflow is a prime example. You’re not just automating data—you’re designing a smarter, calmer way to handle information. That’s the core philosophy.
📊 AI-Powered PDF Extraction Workflow Stages
| Stage | Action | Recommended Tool | Purpose |
|---|---|---|---|
| 1. Capture | Upload or auto-save PDFs from source | Google Drive, Dropbox | Centralize input files |
| 2. Recognition | Scan layout, detect tables, apply OCR | Nanonets, Adobe OCR | Identify structure |
| 3. Parsing | Define data zones and extraction rules | Docparser, Rossum | Extract targeted fields |
| 4. Normalization | Standardize formats (dates, currency, text) | Custom scripts, built-in logic | Ensure clean, usable data |
| 5. Export | Send data to spreadsheet, database, or dashboard | Airtable, Notion, Zapier | Make results actionable |
| 6. Feedback | Correct errors and refine extraction logic | Rossum, ChatGPT | Improve future accuracy |
| 7. Documentation | Record system setup, steps, tools, and owners | Notion, Coda | Ensure repeatability and scaling |
When you break it down into these stages, an automated workflow becomes manageable, modular, and powerful. You can replace or upgrade one part without disrupting the rest—like a well-oiled machine for your data routines.
📚 Use Cases — From Finance to Research to Daily Life
AI-powered PDF extraction tools are reshaping workflows across industries. What used to take hours of manual effort—finding numbers in a report, extracting addresses from scanned files, or pulling names from forms—can now be done in seconds. But how exactly does this look in real-world scenarios?
In the finance sector, the biggest win is invoice automation. Accounting teams use tools like Rossum to parse thousands of invoices every month. These documents often follow varied formats from different vendors, but with rule-based learning and OCR, AI systems can standardize line items, amounts, and payment terms. The result? Faster payments, cleaner books, and fewer human errors.
Legal firms use AI to comb through contracts and case files. Rather than manually reading each clause, systems can flag key legal terms, extract party names, and compare versions of agreements. This speeds up document review and allows lawyers to focus on negotiation, not formatting.
Researchers and academics often work with long PDF reports, white papers, and historical documents. Tools like ChatGPT's Advanced Data Analysis can summarize key points, extract tables, and turn dense scientific prose into digestible data. This is especially helpful when comparing datasets across studies or compiling literature reviews quickly.
For HR departments, automation means faster onboarding. Employment applications, tax forms, ID scans—all can be parsed and categorized with AI. Once a PDF is uploaded, systems can extract the employee’s name, address, bank info, and emergency contact and route that data to the right place. No more copy-paste from form to form.
Even individuals use PDF extraction in daily life. Think of receipts for reimbursements, scanned health reports, rental agreements, or travel itineraries. A parent managing school forms or a freelancer organizing client invoices can set up a small workflow using Nanonets or Zapier to capture key fields and log them automatically.
Nonprofits and NGOs often rely on grant applications and partner reports. These typically arrive in inconsistent PDF formats, but AI tools can extract budget lines, KPI summaries, and project descriptions into structured reports. This reduces the burden on lean teams and ensures better donor reporting.
In education, teachers can extract grades, attendance records, or feedback from student PDFs into a shared dashboard. This helps educators track progress and personalize interventions—without spending hours on data entry.
The key pattern here is flexibility. Whether the document is a scanned letter or a structured lab report, AI tools can adjust to context. It’s less about the file type and more about how predictable the content inside is.
From small businesses to massive enterprises, anyone who handles recurring documents can benefit from structured PDF extraction. It’s no longer just a technical hack—it’s a daily productivity upgrade.
Knowing these use cases helps you imagine what’s possible in your own life. Once you experience the freedom of not having to manually hunt for data in a document, you won’t want to go back.
📌 PDF Extraction Use Case Matrix
| Industry | Use Case | AI Tool | Data Extracted |
|---|---|---|---|
| Finance | Automated invoice processing | Rossum, Nanonets | Line items, amounts, dates |
| Legal | Contract clause extraction | ChatGPT, Kira Systems | Parties, obligations, terms |
| Healthcare | Scan lab results | Adobe OCR, FormX | Patient info, test values |
| Education | Student report automation | ChatGPT, Notion API | Grades, feedback, attendance |
| Research | Extract tables from studies | Tabula, ChatGPT | Data points, citations |
| Personal | Organize receipts or IDs | Nanonets, PDF.co | Names, totals, categories |
This matrix shows how structured PDF extraction is not tied to one industry, but can empower workflows from banking to biology. Once the pattern of data is defined, the same principles scale across use cases with minimal friction.
🔐 Ethical and Privacy Considerations When Using AI on PDFs
As powerful as AI-based PDF extraction tools are, their use raises critical ethical and privacy concerns. Especially when handling documents that contain personal data—like contracts, medical records, or invoices—users must tread carefully. The convenience of automation does not exempt you from responsibility.
The first issue is data consent. If you're processing PDFs that include someone else's information, have you received permission to do so? This applies not only to businesses but also to individuals handling clients’, patients’, or users’ data. Consent must be informed, explicit, and recorded—especially in regions governed by regulations like GDPR or HIPAA.
Second, there's the matter of data minimization. Just because AI can extract every detail doesn't mean it should. Ethical data use means extracting only what's necessary. For example, a school automating grade sheets should avoid pulling out birthdates or medical info unless absolutely relevant. More data is not always better—it’s often riskier.
Security is another major concern. Many popular AI tools are cloud-based, meaning your PDFs are uploaded to external servers. Even if the tool says it encrypts or deletes files after use, you are still exposing potentially sensitive content during transit. Whenever possible, opt for tools with on-device processing, or encrypted upload options.
Another overlooked challenge is algorithmic bias. AI tools trained on limited datasets might misinterpret names, dates, or terms in ways that reflect cultural or regional biases. This can affect extraction accuracy and introduce systemic errors. It’s crucial to test your workflows with diverse samples and monitor for inconsistent behavior.
Transparency matters, too. Can the tool explain why it extracted what it did? If a number is missing or a label misread, are you able to audit that decision? Black-box AI can be efficient, but in regulated environments, you need auditability. Choose tools that allow logs, rules, or exportable logic maps.
There’s also the issue of third-party data sharing. Some AI providers may use uploaded PDFs to improve their models, unless you explicitly opt out. Read the privacy policy carefully. If you're using AI for client work, you could unintentionally breach a contract by exposing their data for model training.
Ethical AI use also means disclosure. If you're sending extracted data to someone else—a client, student, or team member—let them know it was AI-processed. This builds trust and allows them to validate accuracy if needed.
Finally, remember that compliance is a moving target. Privacy laws are evolving rapidly, and what’s acceptable today may not be tomorrow. It’s smart to have a human-in-the-loop for final review, especially in high-stakes sectors like finance or healthcare.
I think one of the most important shifts we need is from “can we automate this?” to “should we?” That question helps anchor every automation decision in ethics. Good automation is not only efficient, but also respectful and intentional.
As RoutineOS encourages intentional digital habits, ethical automation should be part of the conversation. You’re not just processing PDFs—you’re handling trust, data, and privacy. Design your workflows like someone is watching—because they might be.
📌 PDF AI Usage Risk vs Mitigation Strategies Table
| Potential Risk | Description | Mitigation Strategy |
|---|---|---|
| Lack of Consent | Processing PDFs with personal data without user permission | Obtain explicit, documented consent from all data subjects |
| Over-Extraction | Extracting more information than needed from documents | Apply strict field filters; practice data minimization |
| Cloud Exposure | Sensitive PDFs uploaded to external AI servers | Use on-device tools or encrypted upload options |
| Black-box Decisions | AI tools extracting data without transparent logic | Choose tools with auditable logs and rule-based options |
| Bias in Extraction | Misreading cultural terms or names due to training bias | Test with diverse datasets; include human feedback |
| Unintentional Data Sharing | Vendors using uploaded data to train their models | Review terms of service and opt-out options carefully |
Understanding the risks allows you to build systems that are not only efficient, but also ethically sound and legally compliant. As automation becomes routine, trust becomes the most important currency in digital workflows.
🙋♂️ FAQ – Extracting Structured Data from PDFs with AI
Q1. Can I extract tables from scanned PDFs using AI?
Yes, with OCR-enabled tools like Nanonets or Adobe OCR, you can identify and extract tables even from image-based PDF scans.
Q2. What is the best AI tool for extracting structured data from invoices?
Tools like Rossum, Docparser, and Veryfi specialize in extracting line items, totals, and metadata from invoice PDFs with high accuracy.
Q3. How can I ensure privacy when uploading PDFs to AI tools?
Always use services that offer encryption, no-data-retention policies, or allow local/on-premise processing. Review their privacy terms.
Q4. Can I automate the extraction and send it to Google Sheets?
Yes! Most AI extraction tools can be integrated with Google Sheets using APIs or tools like Zapier and Make.com for automation.
Q5. Do AI tools work with handwritten PDFs?
Some advanced tools support handwriting recognition, but accuracy is lower. Structured handwritten forms work better than cursive notes.
Q6. Can I extract data from password-protected PDFs?
Only if you have permission and the password. Most tools require unlocking the file first for legal and technical reasons.
Q7. What file types can AI extract from besides PDF?
Many tools support images (JPG, PNG), DOCX, or scanned TIFFs. But PDF is the most standardized and widely supported format.
Q8. How do I train AI to extract from custom templates?
Tools like Docparser let you define zones, keywords, or anchor points to create reusable templates for specific PDF layouts.
Q9. What happens if my PDF layout changes slightly each time?
You can use rule-based extraction with flexible anchors. Some AI tools also support machine learning to adapt to layout changes.
Q10. Can ChatGPT summarize a PDF file and extract data?
Yes, with Advanced Data Analysis (ADA) or plugins enabled, ChatGPT can read, summarize, and even convert structured data from PDF text.
Q11. Is there a way to batch process multiple PDFs at once?
Absolutely. Most enterprise-grade AI tools allow batch uploads and rule-based bulk processing. You can process hundreds of PDFs in one go.
Q12. Can AI extract both text and images from a PDF?
Yes. Text extraction is standard, and many tools can identify and extract embedded images, logos, or charts as separate objects.
Q13. What is the difference between OCR and parsing?
OCR (Optical Character Recognition) converts images of text into machine-readable text. Parsing then identifies structured elements like names, prices, or dates.
Q14. Do I need coding skills to use AI PDF extractors?
Not necessarily. Many tools offer drag-and-drop interfaces. However, integrating with APIs or custom logic may require some scripting knowledge.
Q15. Can I use AI PDF extraction offline?
Some desktop-based tools or open-source libraries like Tesseract or PDFMiner allow local extraction without cloud dependency.
Q16. How accurate is AI extraction compared to humans?
Accuracy varies by tool and document type. On clean, structured documents, AI can reach 95–99% accuracy. Human review is still useful for edge cases.
Q17. Can AI tools identify sensitive data like SSNs or emails?
Yes. With entity recognition, AI can flag or extract patterns like Social Security Numbers, emails, and phone numbers for compliance filtering.
Q18. What are the risks of using free PDF extraction tools?
Free tools may lack encryption, store your data, or inject ads. For confidential documents, use trusted, privacy-compliant platforms only.
Q19. How can I extract tables that are split across multiple pages?
Some tools allow multi-page table tracking. You may need to adjust settings or manually merge rows if the layout shifts.
Q20. Can AI extract data from fillable form fields inside PDFs?
Yes. Interactive form fields are easier to extract because they’re already structured. Most tools detect them with high accuracy.
Q21. Can I extract data from PDFs written in different languages?
Yes. Many AI tools support multilingual OCR and parsing, especially for widely used languages like English, Spanish, French, and Korean.
Q22. How do I make sure extracted data is legally compliant?
Follow data protection laws (like GDPR or HIPAA), minimize unnecessary data collection, and document user consent or contracts.
Q23. What is the best tool for extracting data from academic research PDFs?
Tools like ChatGPT with Advanced Data Analysis, Grobid, or Scholarcy can summarize and extract citations, tables, and findings effectively.
Q24. Can I automatically label and tag PDFs after extraction?
Yes. Tools like Airtable or Notion can auto-tag PDFs based on content rules, extracted keywords, or metadata values.
Q25. Do these AI tools work with mobile PDFs or scans from phones?
Yes, as long as the scan quality is decent. Clean lighting and flat alignment improve OCR results significantly.
Q26. Is there a way to track errors or missed fields in the extraction?
Many tools offer extraction logs or confidence scores. You can review them and adjust the rules or provide manual corrections.
Q27. Can I create templates for recurring document types?
Definitely. Most advanced tools allow reusable templates, saving time for common formats like receipts, resumes, or certificates.
Q28. How do I deal with PDFs that include embedded signatures or stamps?
Some tools treat these as images, but you can extract them as binary objects or identify presence for verification steps.
Q29. Can I restrict access to extracted data within my team?
Yes. Set up user roles, access controls, and audit trails in platforms like Docparser, Airtable, or Notion to control visibility.
Q30. What’s the best way to get started with AI PDF extraction as a beginner?
Start with drag-and-drop platforms like Nanonets or Rossum, watch beginner tutorials, and experiment with small sample PDFs before scaling.
Disclaimer: The information provided in this article is for educational and informational purposes only. It does not constitute legal, financial, or technical advice. Readers are advised to consult with a qualified professional or service provider before implementing any tools or workflows mentioned. Data privacy and compliance obligations vary by jurisdiction and must be reviewed independently before use.
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