Structured data extraction and stewardship console

For

Data engineers, product teams and operations staff who need clean structured data from websites and documents for apps and AI agents

Solves

Websites and documents must be turned into clean structured data for apps and AI agents, but manual scraping code, layout changes and unstructured files break pipelines.

Delivers

Reviewed structured JSON records with source citations

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce broken extractions and manual data preparation while keeping source traceability.

1. Describe wanted data in plain language.
2. Return structured JSON output.
3. Remove custom scraping code.
4. Render dynamic JavaScript-heavy pages.
5. Adapt to website layout changes.
6. Crawl and rank relevant pages automatically.
7. Enrich records with missing details.
8. Run scheduled extraction and push fresh results.
9. Export via CSV, API or platform integrations.
10. Extract from PDFs, images and unstructured documents.
11. Compress tokens sent to LLMs.
12. Attach source citations to records.
13. Deduplicate and validate records.
14. Provide hosted API endpoints.
15. Offer a point-and-click visual interface.
16. Clean and organize extracted data.
17. Convert pages to clean Markdown.
18. Audit web content for AI visibility and suitability.

Everything these tools do, in one app

• Prompt-based extraction Users describe the data they want in plain language and the tool returns structured output without writing scrapers.Found in /extract by Firecrawl, SingleAPI, apiJuice and 1 more
• Structured JSON output Delivers clean, structured JSON data ready for use in applications and pipelines.Found in /extract by Firecrawl, SingleAPI, apiJuice and 5 more
• No custom scraping code Eliminates the need for manual scraping selectors or coding.Found in /extract by Firecrawl, SingleAPI, apiJuice and 4 more
• Handles dynamic pages Renders JavaScript-heavy or dynamic websites so data can be extracted reliably.Found in SingleAPI, FlowScraper, Jsonify and 1 more
• Adapts to layout changes Automatically adjusts when website designs change, reducing broken extractions.Found in SingleAPI, Jsonify, ZooData
• Automated crawling and context gathering Crawls relevant pages and ranks content to find the needed information automatically.Found in /extract by Firecrawl, Jsonify, CatchAll by NewsCatcher
• Data enrichment Fills in missing details or adds extra context to extracted datasets.Found in SingleAPI, ZooData
• Scheduled monitoring Runs extraction tasks on a schedule and pushes fresh results automatically.Found in FlowScraper, CatchAll by NewsCatcher
• Export and integration options Exports data or connects to other tools via CSV, API, or platform integrations.Found in /extract by Firecrawl, apiJuice, ZooData and 5 more
• Document extraction Extracts structured data from PDFs, images, and other unstructured documents.Found in Jsonify, Tables by Playmaker
• Token compression Reduces the number of tokens sent to LLMs, lowering API costs.Found in ZooData, AgentReady
• Source citations Attaches source references to extracted records for traceability.Found in CatchAll by NewsCatcher
• Deduplication and validation Removes duplicate records and validates data to reduce noise.Found in CatchAll by NewsCatcher
• Hosted API endpoints Provides ready-to-use API endpoints that can be called programmatically.Found in apiJuice, Browser Use Skills
• Visual interface Offers a point-and-click or no-code interface for selecting data and building workflows.Found in FlowScraper, Tables by Playmaker
• Data cleaning tools Built-in tools to clean and organize extracted data.Found in FlowScraper
• Markdown conversion Converts web pages to clean Markdown for easier ingestion by AI models.Found in AgentReady
• LLM content auditing Checks whether web content is visible and suitable for AI agents.Found in AgentReady

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted web pages, documents, extraction prompts and target schemas

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted web pages

4. 3

   Documents

5. 4

   Extraction prompts and target schemas

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed structured JSON records with source citations

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Extraction scope, schema definitions and final data checks remain with the buyer's data owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source and prompt setup, Editable record preview, Review and export. Use a thumbnail gallery for extraction projects, a large central table for records, and a right-hand panel for sources, schemas, citations and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant record. Make the task-specific outcome reviewed structured JSON records with source citations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Buyer-owned websites, authorized documents and permitted data sources. Cloud storage, database import/export and application or agent 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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: describe wanted data in plain language; return structured JSON output. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will data engineers, product teams and operations staff who need clean structured data from websites and documents for apps and AI agents use it to solve "websites and documents must be turned into clean structured data for apps and AI agents, but manual scraping code, layout changes and unstructured files break pipelines"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted records per extraction hour and rework after layout or schema changes.
4. Measure, then decide. Track accepted records per extraction hour and rework after layout or schema changes; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One approved source type and target schema; extraction scope, schema definitions and final data checks remain with the buyer's data owner. Implement one approved input format, a bounded representative case set and the first two task modules: describe wanted data in plain language; return structured JSON output. Support the third module with operator review: remove custom scraping code. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed structured JSON records with source citations. Retain the explicit scope boundary: One approved source type and target schema; extraction scope, schema definitions and final data checks remain with the buyer's data owner.

What the build depends on. Source upload and preview, asynchronous extraction jobs, editable version history, reviewer access and tested export formats. High-fidelity extraction requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved source type and target schema; extraction scope, schema definitions and final data checks remain with the buyer's data owner.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined data package. Offer a monthly extraction allowance after repeat demand. Quote complex document, image or high-volume extraction separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed structured JSON records with source citations. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce broken extractions and manual data preparation while keeping source traceability. Demonstrate a concrete reviewed structured JSON records with source citations using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Data engineers, product teams and operations staff professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed structured JSON records with source citations from a small authorized input set, with a transparent calculation of accepted records per extraction hour and rework after layout or schema changes and no promised savings.

The first 30 days

1. Week 1: interview five data engineers, product teams and operations staff who need clean structured data from websites and documents for apps and AI agents and inspect a recent example of websites and documents must be turned into clean structured data for apps and AI agents, but manual scraping code, layout changes and unstructured files break pipelines.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted records per extraction hour and rework after layout or schema changes, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted records per extraction hour and rework after layout or schema changes. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted records per extraction hour and rework after layout or schema changes; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed structured JSON records with source citations. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved schemas, source constraints and review examples, together with reliable delivery for a narrow data niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for data engineers, product teams and operations staff who need clean structured data from websites and documents for apps and AI agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Buyers today use /extract by Firecrawl, SingleAPI, apiJuice, ZooData, Jsonify, FlowScraper, Tables by Playmaker, Browser Use Skills, CatchAll by NewsCatcher and AgentReady, or manual scraping scripts. Compare this product with the buyer's present method on accepted records per extraction hour and rework after layout or schema changes. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Extraction attempts, rendering or document processing, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed structured JSON records with source citations. Track cost per accepted record, including correction work, unsuccessful cases and support.

Preserve source attribution, extraction accuracy and usage permissions. Data owners approve substantive changes and export scope. One approved source type and target schema; extraction scope, schema definitions and final data checks remain with the buyer's data owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.