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10 AI projects you can build with Firecrawl

Firecrawl takes content from websites and turns it into clean text or structured data that an AI app can use.

10 AI projects you can build with Firecrawl

For example, you could give it five competitors’ pricing pages, collect their plans and prices, then pass that information to an AI to create a comparison table. Firecrawl handles retrieving the web content; the AI handles the analysis.

If your AI app wants to answer questions about last week’s news, price changes by a competitor, or a new law, it needs up-to-date sources. While a well-designed interface and a good system prompt can make the model easier to use, you still need a way to access data that has been published after the model’s training cutoff.

Firecrawl lets you fetch web content and pass it to an LLM. I have used it across client projects and my own experiments, including the kinds of workflows covered here. The ten projects below range from twenty-minute prototypes in visual workflow builders to SaaS products that require ongoing development and maintenance.

How Firecrawl works

Firecrawl is a web data API that accepts a URL or search query and returns markdown, structured JSON, or screenshots. You can use it to handle JavaScript rendering, proxies, rate limits, and browser automation while you work on the application that uses the data.

Four endpoints cover most use cases:

Jina AI, Diffbot, and Browse AI offer overlapping capabilities. Firecrawl’s speed, free prototyping allowance, and support for LLM tool-calling formats have made it a common choice among AI developers.

Pricing starts free at 1,000 credits a month, equivalent to about 1,000 scraped pages. Paid allowances include Hobby at 5,000 credits, Standard at 100,000 credits for $83/month, Growth at 500,000 credits, and Scale at 1,000,000 credits for $599/month. Paid plans also support pay-as-you-go top-ups in $5 increments, with custom pricing for Enterprise.

You should test the sites your project depends on before committing to a scraper. Sites with aggressive blocking, paywalls, or login requirements can return partial or empty results, as can some JavaScript-heavy single-page applications. Your choice of API cannot guarantee access to those pages.

Level 1: No code or low code

The first three projects use visual workflow builders, pre-built templates, or a small amount of JavaScript. Familiarity with n8n or LangFlow will help with the first two.

1. Market intelligence Slack bot

If you run a consultancy, you may need to follow a dozen news sites and competitor blogs to keep up with your clients’ industries. You can automate the first pass through those sources and send the relevant stories to a shared Slack channel.

Build an n8n workflow that runs on a schedule, retrieves pages through Firecrawl, and filters articles by keyword and basic sentiment scoring. Pass the selected articles to an LLM for a two- or three-sentence summary, with a source link attached to each one.

n8n has a native Firecrawl node. Configure your API key, add a scheduled trigger, and connect the output to an OpenAI node for summarisation. If you know which URLs you want to monitor, you can assemble the workflow in about thirty minutes.

Keyword filtering will include some articles that mention your topic in passing, and basic sentiment scoring can misread their meaning. As you review the alerts, you may need to add a classification step with a few lines of JavaScript to improve relevance.

Consultants and small agency owners can use this workflow for lightweight competitive monitoring without subscribing to a dedicated monitoring service.

2. Documentation Q&A chatbot

A team using three SaaS products can spend a surprising amount of time searching for settings across separate documentation sites. A shared chatbot lets colleagues ask a question and follow a link to the instructions they need.

Build a LangFlow pipeline that crawls a vendor’s documentation with Firecrawl, splits the pages into chunks, and stores their embeddings in a vector database. Connect a chat interface so someone can ask “How do I set up SSO in Product X?” and receive an answer with a link to the relevant documentation.

LangFlow includes a Vector Store RAG template that you can connect to Firecrawl for ingestion and ChromaDB for storage. Its default chunking and metadata handling give you a starting point for a working system in under an hour. Dify is another visual builder to consider if you outgrow that setup.

Test a small section of the documentation before crawling 500 pages. Firecrawl’s /crawl can produce inconsistent results on documentation sites built as single-page apps; you may need to retrieve a URL list with /map and then call /scrape for each page.

This makes a useful internal tool for small companies whose staff spend too much time searching old Slack conversations for product instructions.

3. Search-to-presentation generator

For a client briefing with a deadline tomorrow, you can build an app that gathers introductory material on a topic and turns it into a draft presentation.

The user enters a topic, Firecrawl searches the web and retrieves the top results, and an LLM creates slides with speaker notes. An interactive HTML slideshow gives you an output you can present in a browser.

Use /search to retrieve the search results and page content in one call, then write the code to normalise the output and remove overlapping points. A structured slide-generation prompt completes a prototype of roughly 200 lines of JavaScript.

Expect a surface-level presentation until you refine the prompt, and check the sources before presenting. The model can repeat errors from the retrieved pages, so the generated slides still need editorial review. Genspark has a similar workflow if you prefer an existing product.

Researchers working to short deadlines can use the draft to organise a briefing while reserving time to check the material they intend to present.

Level 2: Projects that require some code

These four projects require Python or JavaScript and a few hours of development. You should be comfortable making API calls, writing prompts, and debugging HTTP responses.

4. Competitor pricing tracker

A daily pricing check can help you catch a competitor’s new plan or feature restriction before you discover it weeks later in a sales conversation.

Write a Python script that scrapes competitor pricing pages each day and extracts plan names, feature lists, and dollar amounts into JSON. Compare the results with the previous day’s snapshot and send an email when a field changes. A Streamlit dashboard can display the history.

With Firecrawl’s /extract endpoint, you define fields such as plan_name, price_monthly, feature_list, and free_trial in a schema and receive structured results. The comparison logic can fit in about thirty lines of Python, with roughly a hundred more for the Streamlit dashboard.

You will need to log extraction failures and review them as competitors change their page layouts. A schema that captured the right fields last month may miss them after a redesign, and prices behind a login or “contact sales” form will remain inaccessible through a public-page scrape.

B2B SaaS founders can use this project to follow pricing changes without paying $500/month for a competitive monitoring platform.

5. Local business lead finder

A web design agency can use public website checks to narrow its prospect list to businesses with signs of an outdated site.

Build a script that accepts a city and business category, finds local businesses through Firecrawl’s /search, and scrapes their websites. Rank the results using indicators such as a missing viewport meta tag, page-speed clues in the HTML, or a copyright year older than 2020. Export a CSV with the weakest web presences at the top.

You can implement the scoring with rules in a single Python script. Check the viewport tag and copyright text, then examine the ratio of inline styles to external CSS. These are heuristic signals associated with older websites; use them to select sites for review before deciding whom to contact.

Set rate limits so your checks avoid putting unnecessary load on small business sites. Scraping dozens of websites per search will also consume the free allowance, so budget for at least the Hobby plan if you run this at volume.

Freelancers and small agencies can use this approach to build a focused list of 50 prospects they want to call, with information about each website to support the conversation, rather than buying a list of 500 unqualified contacts.

6. Regulatory change monitor

Compliance and operations teams in finance, healthcare, or education may need to check several regulator websites for updates. A scheduled monitor can collect changed passages and prepare them for review.

Create a job that scrapes selected regulator pages, extracts their text, and compares it with the previous day’s snapshot. Send the old and new passages to an LLM with a prompt such as “What changed, and does this affect a company that does X?” and email the resulting analysis to the team.

You can build the initial workflow with Firecrawl’s /scrape, text comparison logic, and a prompt explaining the company’s activities. The model interprets the changed passages as part of that workflow.

Access can require more work than the comparison logic. Government sites may use CAPTCHAs or rate limits, and some return a 403 response after a few requests. Configure the available proxy settings and leave conservative intervals between checks, then test whether you can retrieve each required source. Some regulator sites may remain inaccessible through the scraper.

This project suits teams that assign someone to check four websites every Monday and want to automate the collection of material for that review.

7. Research assistant for content teams

A comparison article can require twenty browser tabs and three hours of research before you start writing. You need product features and pricing alongside user reviews, with enough context to identify disagreements between them.

Build a tool that accepts competing product URLs and uses Firecrawl to gather features, prices, limitations, and user sentiment indicators. Ask an LLM to produce a comparison table, flag contradictions between vendor statements and reviews, and select three points that could inform the article’s angle. With this workflow, the research phase drops from three hours to twenty minutes, leaving you to write the article.

The prompt needs to explain what a useful research brief looks like for your publication. Specify the information you need and how the model should organise it, so you can use the extracted material without repeating the research.

Review promotional statements with care. A model may repeat “trusted by Fortune 500 companies” without identifying the companies or their use of the product. Include instructions to question such statements, and check the brief yourself before using them in an article.

Content marketers and journalists can use this project to reduce the time they spend collecting and comparing pages during research.

Level 3: Production SaaS products

The next three projects require infrastructure, ongoing maintenance, and support for users. Each has an open-source reference implementation you can study and extend into a product.

8. Open-source deep research platform

A self-hosted research app gives you control over branding, model selection, and data sources. The $200/month OpenAI Deep Research offering inside ChatGPT provides a point of comparison for the kind of experience you might build.

Create a Next.js app where users submit a question and the system plans a multi-step search. Firecrawl gathers the relevant pages, and an LLM combines the material into a report with inline citations and hover previews for checking sources.

Firecrawl’s /search, /scrape, and /extract endpoints cover source discovery, full-text retrieval, and structured extraction. The open-deep-research reference implementation has 8,000+ stars and runs locally with Docker.

Self-hosting means taking responsibility for Postgres, Redis, and Minio. Docker Compose handles much of the setup, but your team needs to diagnose failures and maintain the services. Model choice also affects report quality: GPT-5 or Claude Opus 4.7 produce noticeably better reports than GPT-4o-mini.

Teams that need custom data sources and control over their research software can use this approach without per-seat licensing fees.

9. GEO-first brand monitoring SaaS

Generative engine optimisation, or GEO, concerns how AI search engines describe and recommend brands. Marketing teams need a way to track those responses because the results vary across queries and over time.

Build a dashboard that checks a fixed set of brand-related queries at regular intervals and records the descriptions and competitors that appear. You could send an alert when a competitor appears in results for your brand name, or when an AI service describes your company as a “basic antivirus vendor” after describing it as a “cloud security platform.”

The FireGEO starter kit includes a SaaS template with multi-provider AI analysis through OpenAI, Anthropic, and Perplexity. Use Firecrawl for web data ingestion, then customise the queries and alert thresholds for your customers.

GEO practices remain unsettled, and developers lack a definitive playbook for improving AI search visibility. Expect to revisit your monitoring methods as providers change their products and response behaviour; that maintenance needs to form part of your service costs.

Marketing agencies can use the project to offer AI search monitoring as a premium service while the market develops.

10. Vertical answer engine

A real estate investor asking which Austin neighbourhoods saw the biggest price drops last quarter needs local context that a general search summary may omit. A domain-specific answer engine lets you choose the sources and terminology relevant to that work.

Build a Perplexity-style interface for a field such as real estate, medical literature, legal precedents, or VC deal flow. Define the sites, databases, and forums you want to use, collect their content with Firecrawl, and write prompts that produce cited answers using the field’s terminology.

The Fireplexity project provides a starting point. MIAPI sells a web-grounded answers API with citations if you prefer to buy that part of the system.

A selected set of authoritative sources reduces the irrelevant material you retrieve and gives you more control over the evidence used in an answer. You will need to audit that list as domains go offline, change their URL structures, or publish weaker material.

Serving fifty users requires less infrastructure than serving five thousand. Plan for caching, rate limiting, and a deployment pipeline as usage grows, alongside the editorial work of maintaining the sources.

Domain experts can use this project to answer the questions that general AI search tools handle poorly in their field.

Choosing a web data API

After six months of using these tools in production, I would assess an API against the pages and workloads the project needs:

Choosing a first project

Choose a project whose source pages you can test before building the rest of the application. Retrieve a small sample, inspect the output, and check whether the model can answer the questions your users will ask. Firecrawl supplies the web content for that workflow; you still need to connect it to useful questions and check the resulting answers.

If you have built something with web data and an LLM, share it in the comments or in the AITD community.