AI app for it and development · no coding needed
Live web data access layer for AI agents
Give agents and applications one owned API for search, scraping, crawling and structured extraction.
Made for: Engineering teams building AI agents, RAG pipelines and data products that need live web data
What it does for you
The problem
Agents and pipelines need live web data, but teams rent several search, scraping, crawling and proxy subscriptions and still glue them together themselves.
What it gives you
A versioned live web data access layer for AI agents with source references and usage records
What you give it
Permitted target URLssearch queriesextraction schemascrawl scopes
Build your own version of Olostep, Context.dev and more
One app with what these 10 AI tools do, yours to keep and change: Olostep, Context.dev, TinyFish, Handinger, SCRAPR, Serpex.dev, AnySearch, Tabstack, Thordata, /search by Firecrawl.
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
How it works, step by step
- Search the live web and return structured results
- Scrape pages into Markdown, HTML or JSON
- Crawl sites and sitemaps for pipelines or training data
- Extract structured fields from pages
- Batch-process large URL lists in one request
- Monitor pages over time for changes
- Answer questions with web-grounded sources
- Extract brand assets and metadata
- Run managed browser sessions for JavaScript-heavy pages
- Handle anti-bot measures through proxies and rendering
- Search across multiple engines with fallback
- Run parallel searches with spam and duplicate filtering
- Return JSON matching a supplied schema
- Produce multi-source research with cited answers
- Route requests through global proxy networks
- Maintain sticky sessions for multi-step workflows
- Provide typed SDKs, CLI and documentation
- Connect to automation platforms and AI tools
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned live web data access layer for AI agents 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 Live web data access layer for AI agents 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 Live web data access layer for AI agents 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 links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data201 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
Give agents and applications one owned API for search, scraping, crawling and structured extraction. For engineering teams building AI agents, RAG pipelines and data products that need live web data, convert permitted target URLs, search queries, extraction schemas and crawl scopes into a versioned live web data access layer for AI agents with source references and usage records. The benefit is a testable hypothesis, measured through successful extraction rate per request and cost per accepted record; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted target URLs, search queries, extraction schemas and crawl scopes, then follow this sequence: 1. Search the live web and return structured results. 2. Scrape pages into Markdown, HTML or JSON. 3. Extract structured fields from pages. Resolve uncertain cases with qualified reviewers, approve a versioned live web data access layer for AI agents with source references and usage records, and measure successful extraction rate per request and cost per accepted record against a documented baseline.
How the AI works
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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
