In-depth review: Doctly.ai
Doctly.ai positions itself as a precision tool for developers and analysts who need to extract structured data from complex PDFs, converting them into clean markdown or JSON for downstream AI workflows. Its core value proposition is accuracy on tables, figures, and charts, preserving original formatting in a way that many general-purpose parsers fail to achieve. For data scientists building training datasets from financial reports or researchers pulling measurements from multi-column papers, Doctly.ai offers a focused solution that prioritizes fidelity over speed or volume. The tool supports PDF, Docx, and image inputs, though it does not explicitly advertise OCR for scanned documents, which may limit its utility for fully paper-based workflows. Output options are limited to markdown and JSON, but these are well-chosen for integration into LLM pipelines or data analysis scripts. The API and Python SDK enable embedding into existing applications, though the documentation quality and rate limits will be critical for developers evaluating onboarding friction. Pricing is per page—$0.02 for the Flexible Precision tier and $0.05 for Precision Ultra—which can scale quickly for high-volume use, making it more suitable for targeted extraction tasks rather than bulk processing. The custom workflow feature, available via enterprise contact, suggests a capability for rule-based or AI-driven extraction of specific fields, but its actual flexibility and setup effort remain opaque without a trial. Doctly.ai is best suited for users who value extraction accuracy over cost and who need structured output that retains document hierarchy, such as financial analysts parsing quarterly reports with dense tables or researchers converting scientific papers into machine-readable formats. However, potential buyers should verify performance on their specific document types, especially those with rotated figures, merged cells, or complex layouts, as the tool's claimed accuracy may vary. For teams already using markdown or JSON in their data pipelines, Doctly.ai offers a streamlined path from PDF to structured data, but the per-page cost and lack of a free tier mean it is a considered purchase rather than an impulse adoption.
Who it's built for
Data scientists
Why it fits
Doctly.ai extracts structured data from PDFs with high accuracy, reducing the need for manual cleaning before model training.
Best value
Converting dense tables and figures into clean markdown or JSON for direct use in data pipelines.
Caution
Pricing per page can be costly for large datasets; evaluate volume against budget.
AI developers
Why it fits
REST API and Python SDK allow seamless integration of PDF parsing into AI applications with minimal overhead.
Best value
Automated extraction of text and structure from documents, enabling scalable document processing in apps.
Caution
Custom workflows may require additional setup and contact for pricing; not fully self-serve.
Financial analysts
Why it fits
Accurately extracts tables and figures from financial reports, preserving layout and numerical data.
Best value
Quickly pulling data from quarterly reports into structured formats for analysis and modeling.
Caution
May struggle with highly complex or poorly scanned documents; test on your specific report types.
Researchers
Why it fits
Converts research papers with scientific measurements and multi-column layouts into markdown for further processing.
Best value
Extracting measurements and data from PDFs into machine-readable formats for meta-analysis or AI training.
Caution
Equations and figures may not be perfectly captured; verify extracted content for critical data.
Key features
Accurate extraction of text, tables, figures, and charts
Doctly.ai claims high accuracy on complex layouts including merged cells, multi-column text, and rotated figures.
Benefit
Reduces manual data cleaning and ensures reliable downstream use of extracted information.
Limitation
Accuracy may vary on extremely complex or low-quality scans; not guaranteed for all edge cases.
Conversion to structured markdown or JSON
Outputs preserve document hierarchy in markdown and data types in JSON, facilitating integration.
Benefit
Enables direct feeding into AI models or data pipelines without additional transformation.
Limitation
Markdown may not preserve all formatting (e.g., exact fonts, colors); JSON structure depends on document complexity.
Custom data extraction workflows
Allows defining specific extraction rules for targeted information, such as clauses or fields.
Benefit
Tailors extraction to unique document types, improving relevance and reducing noise.
Limitation
Custom workflows likely require manual setup and may involve additional costs; not fully automated.
API and Python SDK integration
Provides REST API and Python SDK for embedding parsing into applications with documentation.
Benefit
Simplifies integration for developers, enabling automated document processing at scale.
Limitation
API latency and rate limits may affect real-time applications; SDK documentation quality not assessed.
Preservation of original document formatting
Maintains layout elements like alignment, indentation, and table structure in the output.
Benefit
Retains readability and context, making output suitable for human review or further processing.
Limitation
Some formatting elements (e.g., complex graphics, exact colors) may not be preserved; best for text-heavy documents.
Real-world use cases
Extracting data from financial documents
Financial analystScenario
A financial analyst needs to extract tables and footnotes from quarterly reports in PDF format for analysis.
Solution
Use Doctly.ai's API to parse the PDFs, outputting tables as JSON arrays with numerical values and preserving table structure.
Outcome
Eliminates manual data entry, reduces errors, and speeds up the analysis cycle.
Extracting scientific measurements from research papers
ResearcherScenario
A researcher needs to collect measurement data from multiple multi-column research papers for a meta-analysis.
Solution
Feed PDFs into Doctly.ai, which extracts tables and figures into markdown, preserving column structure and numerical values.
Outcome
Automates data collection, allowing the researcher to focus on analysis rather than manual extraction.
Converting complex documents to markdown for AI applications
AI developerScenario
An AI developer wants to feed parsed content from technical manuals into a language model for question answering.
Solution
Use Doctly.ai to convert PDFs to markdown, retaining headings, lists, and code blocks for structured input.
Outcome
Improves model comprehension by providing well-structured text, leading to better response accuracy.
Custom extraction for legal contracts
Legal professionalScenario
A legal professional needs to extract specific clauses (e.g., termination, liability) from a set of contracts in PDF.
Solution
Set up a custom extraction workflow in Doctly.ai to target those clauses, outputting them in JSON format.
Outcome
Saves hours of manual review and ensures consistent extraction across documents.
Pros & cons
Pros
- High accuracy in extracting data from complex documents
- Easy integration with existing systems via API and SDK
- Customizable data extraction workflows
- Preserves original document formatting
- Scalable system
Cons
- Pricing based on page count may be expensive for high-volume users
- Requires an API key for usage
Pricing
Parsed from stored tiers (HTML or plain text). If a line is missing, check the notes below — confirm on the vendor site before purchasing.
Flexible
$0.02/page,
Pay/page Precision: $0.02/page, Precision Ultra: $0.05/page
Enterprise
Custom
Custom Contact us for pricing
Company information
Parsed from directory fields (lists, definition lists, or plain lines). Keys with 「: / ：」 show as cards when most lines match; otherwise as a list. Confirm on official sources.
- Doctly.ai Company Doctly.ai Company name
- Doctly.ai .
- Doctly.ai Login Doctly.ai Login Link
- https://doctly.ai/login
- Doctly.ai Sign up Doctly.ai Sign up Link
- https://doctly.ai/signup
- Doctly.ai Pricing Doctly.ai Pricing Link
- https://doctly.ai/pricing
- Doctly.ai Github Doctly.ai Github Link
- https://github.com/doctly/doctly
- Doctly.ai Support Email & Customer service contact & Refund contact etc. Here is the Doctly.ai support email for customer service: [email protected] . More Contact, visit the contact us page(mailto:[email protected])
Frequently asked questions
What file formats does Doctly.ai support?Workflow
Doctly.ai supports PDF, Docx, and image files. Scanned PDFs are handled as images, but OCR accuracy depends on image quality.
How does Doctly.ai pricing work?Pricing
Pricing is per page: Flexible Precision at $0.02/page and Precision Ultra at $0.05/page. Custom enterprise pricing is available on contact. There is no mention of a free tier or trial.
Can Doctly.ai handle scanned PDFs or images?Limitations
Yes, Doctly.ai can process images and scanned PDFs, but accuracy depends on image resolution and clarity. It is not specifically advertised as an OCR tool, so results may vary for poor-quality scans.
What integrations are available?Integration
Doctly.ai offers a REST API and a Python SDK for integration into custom workflows. No native integrations with platforms like Zapier or cloud storage are mentioned.
Is there a free trial or demo?Pricing
Doctly.ai does not explicitly mention a free trial or demo on the provided information. Pricing starts at $0.02/page, so you can test with a small number of pages at low cost.
How accurate is the extraction compared to other tools?Comparison
Doctly.ai emphasizes high accuracy on tables, figures, and charts, but independent benchmarks are not provided. Accuracy depends on document complexity and quality. It is best to test on your own documents.
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