Analyze, Research, Generate Ideas with Wity.ai

The research problem most teams face isn't a lack of information. It's that information exists in seventeen different places, in seventeen different formats, and the work of synthesizing it into something actionable falls on whoever has time — which is usually no one.

You have browser tabs with competitor pages, a Google Doc with customer interview notes, a Notion page with market data, a Slack thread with someone's off-the-cuff analysis, and three voice memos you recorded on a walk last week and haven't listened to. The raw material for a sharp insight is probably in there somewhere. The synthesis never happens because the friction of assembling it is too high.

Wity is built around a different model: one place where research is captured, connected, expanded, and acted on — without the assembly problem.

Starting with a Seed: Visual Brainstorming

The Visual Brainstorming canvas at app.wity.ai starts with an assumption that most thinking tools get wrong: the best place to begin is not a blank page, but a single, half-formed idea.

You drop in a seed — a question, a hypothesis, a problem statement — and Wity's AI expands it into a structured mind map. Not a list. A map with branches, sub-branches, and connections between ideas that you wouldn't have drawn yourself. The AI surfaces angles you didn't consider, assumptions embedded in your framing, and adjacent questions worth investigating.

This is different from asking an AI to "research X." When you prompt a chat model, you get the AI's best answer to your question. When you use visual brainstorming, you get a structured map of the problem space — which reveals what you don't yet know and where research effort should go. That's a fundamentally better starting point for any investigation.

A Concrete Example

A product team at a B2B SaaS company wants to evaluate adding an async video messaging feature. The PM plants the seed: "async video for team communication." Wity expands this into branches covering: existing player dynamics (Loom, Notion, Slack clips), use cases where async video outperforms text, the integration points with their current product, adoption risks, the infrastructure cost implications, and a set of open questions about user behavior. In ten minutes, the team has a map of the territory — not answers, but a clear picture of what needs to be answered before a decision can be made.

Going Deeper: AI Chat with Your Own Documents

The visual map tells you what questions to pursue. The AI Chat at chat.wity.ai is where you pursue them — with a critical difference from standard ChatGPT or Claude use: your own documents become part of the context.

Upload your customer interview transcripts, your internal product specs, your competitive research PDFs, and your analytics exports. Now when you ask the AI to analyze customer sentiment around a specific feature request, it's reasoning over your actual data — not generic training knowledge. The output is specific to your situation, not a generic answer about how B2B SaaS companies generally handle feature requests.

This is the difference between a research assistant who read some books and one who has read your books. The latter is dramatically more useful for decisions that have real stakes attached.

Autonomous Research with AI Agents

Some research tasks are well-defined enough to run without you. Competitor monitoring, literature reviews, market sizing, customer signal synthesis from support tickets — these follow a repeatable pattern: gather sources, extract relevant information, synthesize into a structured summary.

Wity's AI Agents (wity.ai/tools/agent-builder) handle this class of task autonomously. You define the research brief once — what to look for, where to look, how to structure the output — and the agent runs on a schedule or in response to a trigger. By the time you sit down to make a decision, the research has already been done.

For the product team evaluating the async video feature: they configure a competitor research agent that monitors Loom, Notion, and Slack release notes weekly. Every Monday, there's a structured summary of what competitors shipped, how users responded on Reddit and G2, and any pricing changes. They didn't do this research. They reviewed it and moved faster because of it.

Capturing Insight Before It Disappears

Research doesn't happen only at a desk. The insight often arrives mid-commute, in the shower, or during a conversation you weren't expecting to be significant. Voice Notes (wity.ai/tools/ai-note-taker) captures these moments: speak the thought, Wity transcribes it and connects it to your existing thinking system.

This matters more than it sounds. The half-formed idea you have at 7am while walking to the train is usually lost by 9am when the calendar takes over. When it's captured and connected to the right node in your research map, it becomes usable. A lot of the best insights in any research process are these ambient observations — they get lost because the capture mechanism doesn't exist at the moment they occur. Voice Notes closes that gap.

The Complete Research-to-Idea Workflow

Put it together and the workflow looks like this:

• Map the problem space — use Visual Brainstorming to plant your seed question and let Wity expand it into a structured territory map. Identify the key unknowns.
• Run autonomous background research — configure agents to gather competitor data, customer signals, and market context while you work on other things.
• Capture ambient insight — use Voice Notes for every observation that surfaces outside your desk, synced automatically to your thinking system.
• Synthesize with context — bring your documents into AI Chat to ask specific, high-stakes questions against your actual data.
• Return to the canvas — add what you've learned to the visual map. The map now reflects your full research, not just your starting assumptions. New connections become visible.

This isn't a new category of productivity tool. It's a different approach to how research actually produces ideas: not by collecting more information, but by structuring it in a way that surfaces connections the human mind misses when looking at seventeen separate tabs.

What Changes When Research Is Synthesized

Teams that run this workflow report a consistent shift: they spend less time in preliminary meetings trying to align on what they know, because what they know is already structured and accessible. The async video product team didn't need a three-hour discovery session to figure out the competitive landscape — the agent had already done it, and the canvas had already mapped it. The meeting became a decision about what to build next, not a status update on what everyone had been researching separately.

That's the real value of integrated research and ideation: not faster research, but better decisions per unit of time invested.