AI app for it and development · no coding needed
Document data extraction and workflow preparation workspace
Reduce manual re-keying and tool sprawl while keeping extracted data auditable.
Made for: Operations and data teams extracting structured data from mixed document sets for downstream workflows
What it does for you
The problem
Document data is trapped in mixed formats, and teams rent several parsing, cleaning and workflow tools that do not share structure, provenance or review.
What it gives you
Reviewer-approved structured records with source-linked provenance
What you give it
Licensed documentslayout templatesvalidation rulesdestination schemas
Build your own version of AnyParser Pro, AnyParser API and more
One app with what these 9 AI tools do, yours to keep and change: AnyParser Pro, AnyParser API, Monkt, Document Parser by Contextual AI, Tensorlake, Trellis AI, Document AI by Playmaker, Parsewise API, Preprocess.
Everything these tools do, in one app
- Multi-format document support Extracts data from various document types such as PDFs, images, Word files, spreadsheets, and emails.Found in AnyParser Pro, AnyParser API, Document AI by Playmaker and 1 more
- Structured data extraction Extracts structured data like key-value pairs, tables, and charts from documents.Found in AnyParser Pro, AnyParser API, Document Parser by Contextual AI and 2 more
- Multi-language support Handles documents in multiple languages, including less commonly supported ones like Arabic.Found in AnyParser Pro
- Privacy protection Automatically redacts personally identifiable information (PII) to safeguard sensitive data.Found in AnyParser Pro
- Fast processing Processes data at speeds significantly faster than comparable AI models.Found in AnyParser Pro
- Batch processing Handles thousands of mixed-format documents in a single operation.Found in AnyParser Pro
- API integration Allows integration into existing workflows through an API.Found in AnyParser API, Document Parser by Contextual AI, Parsewise API
- Customizable parsing templates Enables users to create templates to fit different document layouts.Found in AnyParser API
- Document hierarchy inference Maintains structure and relationships across pages in a document.Found in Document Parser by Contextual AI
- Hallucination reduction Uses a multi-stage pipeline to minimize hallucinations for more accurate data extraction.Found in Document Parser by Contextual AI
- Bounding boxes and confidence scores Provides bounding boxes and confidence scores for easier output auditing.Found in Document Parser by Contextual AI, Parsewise API
- Layout-aware segmentation Applies specialized models to different document regions rather than the entire page uniformly.Found in Tensorlake
- Python-based workflow builder Allows users to automate processing pipelines at scale using Python.Found in Tensorlake
- Serverless orchestration Automatically scales and keeps data pipelines up to date.Found in Tensorlake
- Managed GPU infrastructure Provides efficient and production-ready deployment on managed GPU infrastructure.Found in Tensorlake
- Data validation rules Allows users to set customizable rules for validating extracted data before triggering workflows.Found in Document AI by Playmaker
- Third-party integrations Integrates with popular data sources and third-party applications to streamline workflows.Found in Document AI by Playmaker, Trellis AI
- Secure processing Uses encrypted environments, multi-factor authentication, and strict data handling policies to protect sensitive information.Found in Document AI by Playmaker
- Multi-document processing Processes multiple documents in a single call, replacing an end-to-end pipeline.Found in Parsewise API
- Contradiction detection Detects contradictions across all provided documents and pages.Found in Parsewise API
- Provenance and lineage Provides full lineage and provenance down to source words, pages, and documents.Found in Parsewise API
- Automated data cleaning Automatically handles missing values and outliers in data.Found in Preprocess
- Data transformation Offers options such as normalization, encoding, and scaling for data.Found in Preprocess
- Visual data profiling Quickly identifies data quality issues through visual profiling.Found in Preprocess
- Customizable workflows Allows users to create workflows tailored to different project requirements.Found in Preprocess
- Task prioritization Uses AI to help identify important deadlines and tasks.Found in Monkt
- Automated reminders Sends automated reminders and notifications to keep users on track.Found in Monkt
- Collaboration tools Facilitates seamless communication and collaboration within teams.Found in Monkt, Trellis AI
- Customizable project templates Provides templates for different workflow needs.Found in Monkt
- Calendar and email integration Integrates with popular calendar and email applications for centralized management.Found in Monkt
- Automated data sorting Automatically sorts and categorizes data to improve workflow efficiency.Found in Trellis AI
- Customizable dashboards Provides tailored data visualization through customizable dashboards.Found in Trellis AI
- Real-time analytics Delivers real-time analytics with alert notifications for critical data changes.Found in Trellis AI
How it works, step by step
- Ingest PDFs, images, Word files, spreadsheets and emails
- Extract key-value pairs, tables and charts
- Handle multi-language documents including Arabic
- Redact personally identifiable information before storage
- Process batches of mixed-format documents
- Expose an API for existing workflows
- Apply customizable parsing templates per layout
- Infer document hierarchy across pages
- Reduce hallucinations through a multi-stage extraction pipeline
- Show bounding boxes and confidence scores for auditing
- Segment document regions with layout-aware models
- Build processing pipelines in Python
- Run pipelines on serverless orchestration with managed GPU infrastructure
- Apply validation rules before triggering workflows
- Connect third-party data sources and applications
- Process multiple documents in a single call
- Detect contradictions across documents and pages
- Track provenance down to source words, pages and documents
- Clean missing values and outliers
- Normalize, encode and scale extracted data
- Profile data quality visually
- Sort and categorize records automatically
- Show customizable dashboards with real-time analytics and alerts
- Send reminders and notifications on deadlines
- Support team collaboration and project templates
- Sync with calendar and email applications
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewer-approved structured records with source-linked provenance with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Document data extraction and workflow preparation workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Document data extraction and workflow preparation workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links7 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data199 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce manual re-keying and tool sprawl while keeping extracted data auditable. For operations and data teams extracting structured data from mixed document sets for downstream workflows, convert licensed documents, layout templates, validation rules and destination schemas into reviewer-approved structured records with source-linked provenance. The benefit is a testable hypothesis, measured through accepted records per operator hour and downstream corrections after load; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect licensed documents, layout templates, validation rules and destination schemas, then follow this sequence: 1. Ingest PDFs, images, Word files, spreadsheets and emails. 2. Extract key-value pairs, tables and charts. 3. Apply customizable parsing templates per layout. 4. Validate extracted data against rules. 5. Detect contradictions across documents and pages. Resolve uncertain cases with qualified reviewers, approve reviewer-approved structured records with source-linked provenance, and measure accepted records per operator hour and downstream corrections after load against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated extraction, validation and preparation modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved document set and destination schema; final data-quality and compliance checks remain with the buyer. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, redaction accuracy and usage permissions. Buyers approve substantive changes and destination scope. One approved document set and destination schema; final data-quality and compliance checks remain with the buyer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One approved document set and destination schema; final data-quality and compliance checks remain with the buyer. Implement one approved input format, a bounded representative case set and the first three task modules: ingest PDFs, images, Word files, spreadsheets and emails; extract key-value pairs, tables and charts; apply customizable parsing templates per layout. Support the remaining modules with operator review: validate extracted data against rules; detect contradictions across documents and pages. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Buyer-owned document stores, authorized source systems and permitted destination applications. Cloud storage, design-file import/export and workflow destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Source intake and template setup, Editable extraction preview, Validation and delivery. Use a thumbnail gallery for document batches, a large central preview with bounding boxes and confidence scores, and a right-hand panel for templates, validation rules and comments. Let users compare extracted values against source regions side by side. Display draft, changes requested and approved states. Provide a destination preview link with comments anchored to the relevant field. Make the task-specific outcome reviewer-approved structured records with source-linked provenance visible beside its evidence, review state and value baseline.
