Managed web data extraction workspace

For

Operations and data teams that need recurring structured data from public websites

Solves

Collecting web data at scale requires stitching together separate scraping, rendering, proxy and scheduling tools, and each site change breaks the pipeline.

Delivers

Reviewed, export-ready datasets linked to source pages

Built in

about 6 weeks of creation time, MVP in 7 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 the number of rented scraping subscriptions and manual re-checks while keeping extracted data inside the client's own systems.

1. Configure target URLs and extraction fields without code.
2. Render JavaScript pages before extraction.
3. Crawl full sites by following internal links.
4. Handle pagination across result pages.
5. Extract Google search results programmatically.
6. Convert unstructured page content into structured records with AI.
7. Adjust extraction rules when site layouts change.
8. Process records through graph-based pipelines for context.
9. Clean, deduplicate and filter extracted content.
10. Route requests through built-in residential and mobile proxies.
11. Apply anti-detection browser fingerprints and patches.
12. Run scheduled extraction jobs in the cloud.
13. Manage multiple accounts and scale concurrent tasks.
14. Build end-to-end workflows that pass data to downstream steps.
15. Export datasets as CSV, Excel or JSON.
16. Provide Python, JavaScript and TypeScript SDKs for custom jobs.
17. Compare each run against the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed dataset with source references and unresolved questions.

Everything these tools do, in one app

• Web data extraction Automatically pulls data from websites.Found in ScrapeGraphAI, Scrapeless x n8n, No-Code Scraper and 2 more
• No-code interface Lets users set up scraping tasks without programming.Found in Scrapeless x n8n, No-Code Scraper, Crawl AI
• Export multiple formats Saves extracted data as CSV, Excel, or JSON.Found in No-Code Scraper, Crawl AI
• Scheduled scraping Runs data collection automatically at set intervals.Found in No-Code Scraper, Crawl AI
• JavaScript rendering Accesses data on sites that rely on JavaScript to load content.Found in Scrapeless x n8n
• Full-site crawling Follows links to collect data from an entire website.Found in Scrapeless x n8n
• Google search scraping Retrieves search results programmatically from Google.Found in Scrapeless x n8n
• Workflow automation Builds end-to-end automated processes for data handling.Found in Scrapeless x n8n, FIRE-1
• LLM-powered extraction Uses AI to convert unstructured web content into structured data.Found in ScrapeGraphAI
• Adaptive scraping Automatically adjusts to changes in website layouts.Found in ScrapeGraphAI
• Graph-based pipelines Processes data through graph structures for context-aware extraction.Found in ScrapeGraphAI
• SDKs for developers Provides official libraries for Python, JavaScript, and TypeScript.Found in ScrapeGraphAI
• Pagination handling Collects data across multiple pages of a website.Found in No-Code Scraper
• Cloud-based operation Runs scraping tasks in the cloud without local setup.Found in No-Code Scraper
• Data cleaning tools Organizes and filters extracted content to improve quality.Found in Crawl AI
• Anti-detection technology Avoids bot detection using real browser fingerprints and patches.Found in Legion AI
• Built-in proxies Uses a pool of residential and mobile proxies for secure scraping.Found in Legion AI
• Multi-account scalability Manages multiple accounts and scales automation tasks.Found in Legion AI

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted target URLs, extraction rules and delivery formats

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted target URLs

4. 3

   Extraction rules and delivery formats

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed, export-ready datasets linked to source pages

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 runs only on permitted public pages; final data-use and legal checks remain with the client. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Target and rules setup, Run monitor and review queue, Dataset export and delivery. Use a project list for scraping jobs, a central table of extracted records with source links, and a right-hand panel for selectors, schedules and permissions. Let users compare runs 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, export-ready datasets linked to source pages visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, target lists, run history, client comments, approval states, usage allowances, rate 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

Client-owned databases, spreadsheets and data warehouses. Cloud storage, scheduling services and downstream automation platforms. Start with file exchange and validate destination specifications before promising direct writes. 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

   7 days

   One buyer segment, one recurring use case; first modules: configure target URLs and extraction fields without code; render JavaScript pages before extraction. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

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

4. 4

   Full product

   3 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 operations and data teams that need recurring structured data from public websites use it to solve "collecting web data at scale requires stitching together separate scraping, rendering, proxy and scheduling tools, and each site change breaks the pipeline"?
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 corrections after delivery.
4. Measure, then decide. Track accepted records per extraction hour and corrections after delivery; 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 permitted target site class and one delivery format; final data-use and legal checks remain with the client. Implement one approved input format, a bounded representative case set and the first two task modules: configure target URLs and extraction fields without code; render JavaScript pages before extraction. Support the third module with operator review: crawl full sites by following internal links. 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, export-ready datasets linked to source pages. Retain the explicit scope boundary: One permitted target site class and one delivery format; final data-use and legal checks remain with the client.

What the build depends on. Target URL intake, run scheduling, asynchronous extraction jobs, editable rule history, reviewer access and tested export formats. High-fidelity extraction requires specialist QA on changing sites. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One permitted target site class and one delivery format; final data-use and legal checks remain with the client.

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 extraction package. Offer a monthly production allowance after repeat demand. Quote complex multi-site or high-volume work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, export-ready dataset linked to source pages. 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 the number of rented scraping subscriptions and manual re-checks while keeping extracted data inside the client's own systems. Demonstrate a concrete reviewed, export-ready dataset linked to source pages using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations and data teams that need recurring structured data from public websites professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, export-ready dataset linked to source pages from a small authorized input set, with a transparent calculation of accepted records per extraction hour and corrections after delivery and no promised savings.

The first 30 days

1. Week 1: interview five operations and data teams that need recurring structured data from public websites and inspect a recent example of collecting web data at scale requires stitching together separate scraping, rendering, proxy and scheduling tools, and each site change breaks the pipeline.
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 corrections after delivery, 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 corrections after delivery. 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 corrections after delivery; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, export-ready datasets linked to source pages. 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 extraction rules, site-change corrections 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 operations and data teams that need recurring structured data from public websites. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ScrapeGraphAI, Scrapeless x n8n, FIRE-1, No-Code Scraper, Crawl AI and Legion AI. Compare this product with the buyer's present method on accepted records per extraction hour and corrections after delivery. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Proxy bandwidth, rendering compute, 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, export-ready datasets linked to source pages. Track cost per accepted record, including correction work, unsuccessful cases and support.

Preserve source attribution, extraction permissions and usage limits. Clients approve data use and retention scope. One permitted target site class and one delivery format; final data-use and legal checks remain with the client. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.