Live web data access layer for AI agents

For

Engineering teams building AI agents, RAG pipelines and data products that need live web data

Solves

Agents and pipelines need live web data, but teams rent several search, scraping, crawling and proxy subscriptions and still glue them together themselves.

Delivers

A versioned live web data access layer for AI agents with source references and usage records

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

Give agents and applications one owned API for search, scraping, crawling and structured extraction.

1. Search the live web and return structured results.
2. Scrape pages into Markdown, HTML or JSON.
3. Crawl sites and sitemaps for pipelines or training data.
4. Extract structured fields from pages.
5. Batch-process large URL lists in one request.
6. Monitor pages over time for changes.
7. Answer questions with web-grounded sources.
8. Extract brand assets and metadata.
9. Run managed browser sessions for JavaScript-heavy pages.
10. Handle anti-bot measures through proxies and rendering.
11. Search across multiple engines with fallback.
12. Run parallel searches with spam and duplicate filtering.
13. Return JSON matching a supplied schema.
14. Produce multi-source research with cited answers.
15. Route requests through global proxy networks.
16. Maintain sticky sessions for multi-step workflows.
17. Provide typed SDKs, CLI and documentation.
18. Connect to automation platforms and AI tools.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned live web data access layer for AI agents with source references and unresolved questions.

Everything these tools do, in one app

• Web search Search the live web using queries and return structured results.Found in Olostep, TinyFish, Serpex.dev and 2 more
• Scrape pages Extract content from any URL into clean formats like Markdown, HTML, or JSON.Found in Olostep, Context.dev, TinyFish and 3 more
• Crawl websites Crawl entire websites or sitemaps to collect pages for pipelines or training data.Found in Olostep, Context.dev
• Structured data extraction Return cleaned, structured data such as JSON with specific fields from web pages.Found in Olostep, Context.dev, TinyFish and 4 more
• Batch processing Process large lists of URLs in a single request for production data pipelines.Found in Olostep, SCRAPR
• Page monitoring Monitor pages over time for changes like prices, content updates, or business signals.Found in Olostep
• Question answering Answer questions with web-grounded sources and structured output.Found in Olostep
• Brand extraction Extract brand assets and metadata like logos, colors, fonts, and social links.Found in Context.dev
• Managed browser automation Use remote browser sessions to interact with JavaScript-heavy pages and multi-step workflows.Found in TinyFish, Tabstack
• Anti-bot handling Bypass anti-scraping measures using proxies, fingerprinting, and rendering.Found in Context.dev, Handinger, Serpex.dev and 1 more
• Multi-engine search Search across multiple search engines with fallback strategies.Found in Serpex.dev
• Parallel search and deduplication Run parallel searches across trusted sources and filter out spam, ads, and duplicates.Found in AnySearch
• Schema-driven output Provide a schema and receive JSON that matches the requested shape.Found in Tabstack
• Research with citations Run multi-source research and get cited answers with source URLs.Found in Tabstack
• Proxy networks Access global proxy networks including residential, mobile, and data center IPs.Found in Thordata
• Session management Maintain sticky sessions for multi-step workflows like logins and checkout simulations.Found in Thordata
• SDKs and developer tooling Integrate using typed SDKs, CLI, documentation, and developer-friendly tools.Found in Olostep, Context.dev, Serpex.dev
• Platform integrations Connect with automation platforms and AI tools like Zapier, n8n, and LangChain.Found in Olostep, /search by Firecrawl

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted target URLs, search queries, extraction schemas and crawl scopes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted target URLs

4. 3

   Search queries

5. 4

   Extraction schemas and crawl scopes

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A versioned live web data access layer for AI agents with source references and usage records

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. Respect robots directives, terms of use and rate limits; final data-use and compliance checks remain with the buyer. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Request builder and schema editor, Run and job monitor, Delivery and usage console. Use a project list for API keys and jobs, a large central panel for request and schema definition, and a right-hand panel for run logs, source references and errors. Let users compare runs side by side. Display queued, running, needs review and delivered states. Provide a client preview link with comments anchored to the relevant record. Make the task-specific outcome a versioned live web data access layer for AI agents with source references and usage records visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, API keys, job versions, client comments, approval states, usage allowances, request 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 target lists, permitted data sources and existing pipelines. Cloud storage, automation platforms and AI tools such as Zapier, n8n and LangChain, plus publishing 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

   7 days

   One buyer segment, one recurring use case; first modules: search the live web and return structured results; scrape pages into Markdown, HTML or JSON. 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 engineering teams building AI agents, RAG pipelines and data products that need live web data use it to solve "agents and pipelines need live web data, but teams rent several search, scraping, crawling and proxy subscriptions and still glue them together themselves"?
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: Successful extraction rate per request and cost per accepted record.
4. Measure, then decide. Track successful extraction rate per request and cost per accepted record; 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 input format, a bounded representative case set and the first two task modules: search the live web and return structured results; scrape pages into Markdown, HTML or JSON. Support the third module with operator review: extract structured fields from 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.

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 a versioned live web data access layer for AI agents with source references and usage records. Retain the explicit scope boundary: Respect robots directives, terms of use and rate limits; final data-use and compliance checks remain with the buyer.

What the build depends on. Request builder and preview, asynchronous job processing, editable version history, reviewer access and tested export formats. High-fidelity extraction requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Respect robots directives, terms of use and rate limits; final data-use and compliance checks remain with the buyer.

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 request allowance after repeat demand. Quote complex browser automation, proxy volume or specialist extraction separately. These are test prices, not market benchmarks. Package the initial sale as one bounded live web data access layer for AI agents with source references and usage records. 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

Give agents and applications one owned API for search, scraping, crawling and structured extraction. Demonstrate a concrete live web data access layer for AI agents with source references and usage records using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams building AI agents, RAG pipelines and data products that need live web data professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample live web data access layer for AI agents with source references and usage records from a small authorized input set, with a transparent calculation of successful extraction rate per request and cost per accepted record and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams building AI agents, RAG pipelines and data products that need live web data and inspect a recent example of agents and pipelines need live web data, but teams rent several search, scraping, crawling and proxy subscriptions and still glue them together themselves.
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 successful extraction rate per request and cost per accepted record, 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: Successful extraction rate per request and cost per accepted record. 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

Successful extraction rate per request and cost per accepted record; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a live web data access layer for AI agents with source references and usage records. 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, extraction rules and review examples, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering teams building AI agents, RAG pipelines and data products that need live web data. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Olostep, Context.dev, TinyFish, Handinger, SCRAPR, Serpex.dev, AnySearch, Tabstack, Thordata and /search by Firecrawl, plus in-house glue scripts. Compare this product with the buyer's present method on successful extraction rate per request and cost per accepted record. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Search, scraping, crawling, proxy and rendering usage, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a live web data access layer for AI agents with source references and usage records. Track cost per accepted record, including correction work, unsuccessful cases and support.

Preserve source attribution, extraction accuracy and usage permissions. Buyers approve data-use scope and downstream actions. Respect robots directives, terms of use and rate limits; final data-use 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.