Most AI research tools give you an answer and move on. Innogath is built for what comes after: understanding, branching, revisiting, and turning research into something usable. It combines a structured report, a visual graph, branching pages, and linked notes so your thinking doesn’t disappear into a chat thread.
👋 Hey Product Hunt,
I’m Eric, founder of Innogath. Thanks for checking us out today.
I built Innogath because most AI research tools feel great at one thing: giving you an answer fast.
But real research usually starts breaking right after that.
You open a few threads.
You go deeper on one branch.
You want to come back to an earlier idea.
You want to compare two paths, keep notes, and turn the work into something usable.
And suddenly everything is trapped inside one long chat thread.
That’s the problem Innogath is built for.
Innogath turns deep research into a workspace you can actually navigate:
* a structured report you can read like a book
* a visual graph of ideas and branches
* branching pages you can return to anytime
* linked notes you can turn into writing
* node-based chat, so you can continue research in context instead of starting over
The core idea is simple:
**Research is not linear. Your workspace shouldn’t be either.**
What makes Innogath different isn’t just that it can generate a report.
It helps you stay inside the research, keep the structure alive, and keep building on it.
A few things we focused on:
* turning one-off answers into a navigable research map
* making branches visible instead of burying them in chat history
* letting you move between reading, exploring, note-taking, and writing in one place
* keeping sources and structure tied to the work, not detached from it
Who it’s for:
* people doing deep research on complex topics
* founders comparing ideas, markets, and product directions
* writers and learners who want to revisit and expand their thinking
* anyone who feels chat-based AI tools “end too early”
What I’d love feedback on:
1. Does the book + graph workflow feel genuinely different from chat-based tools?
2. Do branching pages make research easier to revisit and extend?
3. At what point does the workflow feel powerful vs. overwhelming?
I’ll be here all day answering questions and would genuinely love blunt feedback.
Thanks for taking a look.
This resonates — in structured finance and renewable energy M&A, research outputs rarely have a useful structure. You end up with stacks of memos that nobody navigates after the deal closes. The "navigable book + graph" format is exactly what due diligence research should look like. I publish financial model templates on Eloquens for exactly this reason — structured, navigable outputs rather than raw files. Really looking forward to seeing how this develops for technical/financial research use cases.
Deep research is still one of my favorite use cases for AI. This is a really interesting project. How do you guys actually orchestrate your agents? Do you allow users to choose the primary model used? I could easily use more than $9.60 in Opus credits on a single deep research run!
Could be useful! Alhtough one big problem with research tools is knowledge decay, so how does Innogath help users revisit and reuse old research months later? Also can I export my book and graph in other formats?
The ‘parent memory’ idea sounds powerful but also risky: if a parent page contains a mistaken assumption, that error can propagate down the tree. What guardrails do you have (or plan) to help users correct upstream facts and keep downstream branches consistent without rerunning everything?
Research for myself is one thing — but I usually need to hand it off. What does sharing look like? Does the other person get the full graph with branches, or just the final report?
Hi Eric Just kicked off my first research on it — really interesting product, I like the branching approach over linear chat. Good luck with the launch!
About Innogath on Product Hunt
“Turn deep research into a navigable book + graph”
Innogath launched on Product Hunt on April 16th, 2026 and earned 82 upvotes and 12 comments, placing #24 on the daily leaderboard. Most AI research tools give you an answer and move on. Innogath is built for what comes after: understanding, branching, revisiting, and turning research into something usable. It combines a structured report, a visual graph, branching pages, and linked notes so your thinking doesn’t disappear into a chat thread.
Innogath was featured in Productivity (649.7k followers), Writing (59.1k followers) and Artificial Intelligence (466.2k followers) on Product Hunt. Together, these topics include over 223.8k products, making this a competitive space to launch in.
Who hunted Innogath?
Innogath was hunted by Eric Liew. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Innogath stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Eric Liew