How to Build a Personal AI Research Assistant That Runs in Your Browser

Here is a routine that repeats dozens of times a day for researchers, journalists, analysts and knowledge workers: open a tab, find an article or report, read a section, switch to another tab to ask an AI assistant about it, copy and paste the text, wait for the answer, switch back, repeat. It’s fragmented, slow and tiring, not because any step is hard, but because the constant switching erodes focus and makes it hard to build a clear picture across several sources.

What if your AI research assistant lived in the browser, on the page you’re already reading: no tab switching, no copy-paste? That is what you can build with Agentic Workflow, a Chrome extension for visual AI automations that run in your browser. This guide shows you how, step by step.

What a browser-native research assistant can do

A browser-native research assistant differs from a standalone AI tool in one key way: it reads the page you’re on and works in that context, without you pasting it in. It can:

• Summarise any article or report in one click
• Extract key claims, statistics or names from a page
• Answer specific questions about the current page
• Compare information across several pages you’ve read
• Point out contradictions or gaps between sources
• Turn raw web content into structured notes or outlines
• Rewrite dense technical content in plain language

Because it runs in your browser, it works on pages behind a login, subscription content you have access to, internal documents and web apps: places a standalone chatbot can’t reach.

How it works

A research assistant workflow has four parts: a trigger (a hotkey, the right-click menu, or a button in the side panel) that starts it on the current page; a step that reads the page; an AI step that sends the text to a model (in the cloud, or on your device) with your research instruction; and an output that shows the answer next to the page or saves it to a note, a sheet or a file.

Everything runs in your browser. No data goes to an outside server unless you choose a cloud AI provider or an output that sends it.

Build your research assistant, step by step

The docs example Summarize page (LLM chain) is exactly this assistant in its simplest form. Open it, then make it yours.

Good research instructions are specific about what to pull out:

Advanced: research across several pages

For bigger projects, collect what you learn from each page, then combine it. Add a Data Store node to your assistant so each run saves its summary under a project name; a second workflow reads all the saved summaries and asks the model for one brief.

For example: read five papers, let the assistant summarise each one, then run the synthesis to get one research brief, without leaving your reading flow.

For a large body of documents you search often, a knowledge base in AWFlow can index them on your device so an agent answers from them.

Use a local model for privacy

If you research sensitive material (competitive intelligence, legal documents, medical information, confidential company data), you may not want it sent to a cloud provider. Agentic Workflow can keep the AI on your machine:

• Ollama: run Llama 3, Mistral, Gemma and others on your computer; Agentic Workflow connects to Ollama’s local endpoint.
• WebLLM: run smaller models inside the browser with WebGPU: nothing to install, no outside connection after the download.

Local models are slower than the biggest cloud models but keep your data private. For research summaries, a 7B–8B model is usually enough; see how to choose, install and compare a local AI model.

Real use cases

• Academics: pull abstracts, methods and conclusions from papers; build literature-review notes without another tool.
• Journalists: check claims in press releases against background material; extract quotes and facts from long transcripts.
• Market analysts: summarise earnings reports, launches and industry news in seconds; track competitors’ messaging.
• Sales: research prospects before calls: company news, product updates and talking points in one click.
• Legal professionals: summarise long documents, highlight obligations and defined terms, with a local model for confidentiality.

Try it

A browser-native research assistant is a basic productivity upgrade for anyone who reads and synthesises information from the web. Keeping the AI on the page, instead of in another tab, removes the switching that makes research feel slow.