A research assistant that collects, summarises, and prioritises information sounds like a custom software project. It's not. With the right combination of no-code tools, you can have a working AI research workflow running in an afternoon — one that handles the volume and produces output you can act on.
What "AI Research Assistant" Actually Means
For most knowledge workers, a research assistant needs to do three things:
- Collect: pull in information from relevant sources
- Summarise: reduce long documents to what matters
- Prioritise: surface what's relevant to your specific context
You don't need a single tool that does all three. You need a workflow where three simpler tools hand off to each other.
The Tools
NotebookLM (Google) — for document synthesis
Upload PDFs, research papers, reports, or web pages. Ask questions across all of them. Get answers with citations to the source material. Best for: synthesising a corpus of documents you've already collected, understanding complex topics from primary sources, generating summaries grounded in specific texts.
Perplexity — for live research
Ask questions and get answers with web citations in real time. More reliable than asking a base language model for current information. Best for: "what's happening in X right now", "what are the current options for Y", "who are the key people in Z". Verify important claims from the original sources, not just the summary.
ChatGPT or Claude Projects — for context-aware conversation
Both allow you to maintain a persistent context window with uploaded documents and ongoing instructions. Best for: working with a consistent set of materials over time, getting answers that account for your specific situation, and iterating on analysis across multiple sessions without re-briefing from scratch.
A No-Code Research Workflow
Step 1: Define your research question precisely
Before you open any tool: write one sentence stating what you need to know and why. "What are the main challenges in deploying large language models in regulated industries, for a briefing note to a non-technical executive." Specificity shapes every step downstream.
Step 2: Collect with Perplexity
Use your research question to search Perplexity. Ask for an overview, then ask targeted follow-up questions. Save useful results as links or paste them into a working document. Time box this at 15–20 minutes. You're looking for orientation, not exhaustive coverage.
Step 3: Deepen with NotebookLM
For any source documents (reports, papers, whitepapers) that came up in step 2, upload them to NotebookLM. Ask the questions from your brief. Get cited answers. This is where you move from "generally true" to "specifically true and sourced."
Step 4: Synthesise with a chat model
Paste your collected notes, links, and key findings into a chat session. Prompt: "Based on the following notes, write a [FORMAT] for [AUDIENCE] covering [KEY POINTS]. Use the facts from the notes — don't add information not present in the source material."
Step 5: Verify before using
Spot-check three to five specific claims against the original sources. Check that citations exist and say what the summary claims they say. Then your output is ready to use.
Organising Output Over Time
If you're researching a topic over weeks, not just once: keep a living document with dated entries. Paste key findings in chronologically. When you need a synthesis, prompt against the whole document. Over time, this becomes an invaluable reference — one that's genuinely personalised to your context and verified by you along the way.