Founder, Creatorbase
As solo founder, I handled research, design, development and go-to-market. I designed the campaign workflow and direct creator payments.
GaganGehani
Product leader, re(turned) builder.
Product leader across AI, growth and fintech. I founded Creatorbase, led product work as a founding team member at Veera and an early APAC product manager at Coinbase, built an entire suite of career support products at upGrad, and built the tech and D2C function from scratch at Skillmatics.
Open to AI product roles with Dubai, UAE and remote teams. AI product experience and hiring profile ↗
Career highlights
Creatorbase
Built a profitable, agentic UGC (user-generated content) platform on the blockchain.
- 427 vetted creators, globally.
- A publicly listed US company as a paid customer.
upGrad
career outcomes over ~4 years
- Built the career-support product vertical from 0 to 1.
- Built a marketing website chatbot that drove US$3.2M in incremental revenue.
Veera
users
Helped redesign the GTM around product-led growth.
- Initial 5X boost in the first four months after launch.
Skillmatics
revenue and ~100K users in 11 months
Helped build the tech vertical from scratch.
- 31% offline-to-online adoption with Zero (not a typo!) incremental CAC (customer acquisition cost).
Selected work
All projects| Index | Project | Category | Year | Open project |
|---|---|---|---|---|
| 01 | Creatorbase | Founder · AI product | 2025– | |
| 02 | Veera · Product-led growth and GTM | Director of Product · Growth | 2023–2024 | |
| 03 |  Coinbase · Referrals and promo codes | Product Manager · Global growth | 2021–2022 | |
| 04 | upGrad · Career Support Centre | Product Manager · Platform | 2017–2020 |
Director of Product, Veera
As a founding team member, I helped build Veera’s browser from 0 to 1 and led product strategy for GTM, AI and platform charters.
Product Manager, Coinbase
I was directly responsible for global growth products. I built referral and promo-code systems that accounted for 60% of user-base acquisition during the launch phase in new markets.
Try now
All productsRight Resource, Right Moment
One saved resource, shown when it fits the task you have just started.
Requires Grokbot to try.
Context Guard
Review what an agent knows and assumes about you, fact by fact.
Requires Grokbot to try.
Labs
Explore LabsThis is my experimental lab for product thoughts I keep having and prototypes I build.
DataDive AI
An on-demand AI data scientist on Twitter.
Receipts
An AI interviewer that turns a project conversation into interview flashcards, with supporting quotes kept separate from the written story.
HomeLore
A voice-first family-memory prototype that keeps original words, generated narration and unknowns distinct.
How I think
Right Resource, Right Moment
- Problem
- Saving a resource is easy. Getting it back at the right moment is harder.
- Decisions
- Show one high-confidence card. Silence stays unknown; explicit feedback can block a card. An annoying card pauses for 14 days.
- Measure
- Right-moment ratings, precision and false-interrupt rate.
Context Guard
- Problem
- An agent can act on memory you cannot see or correct.
- Decisions
- Show each fact and let the person confirm, correct or delete it.
- Measure
- Confirmed facts, corrections and deletions.
Making agent work steerable before the change lands
A proposal for adding an instruction to a specific step of an agent task and seeing whether the agent can still include it.
Making agent work steerable before the change lands
A proposal for adding an instruction to a specific step of an agent task and seeing whether the agent can still include it.
The change was small
I asked an agent to put a holding page on my portfolio. While it was working, I wanted to add LinkedIn alongside my X profile. The change was small. Knowing whether the instruction would reach the right part of the work was harder.
I could send another message, but I could not easily see whether the agent was still preparing the page, had already updated the links, or was about to deploy. I wanted to steer the work without stopping it and without triggering a second deployment.
Attach the instruction to the work
I would start with a short, visible map of the current turn. A new instruction could refer to one of its steps. I could ask to include LinkedIn in the profile-links step and see an acknowledgment beside the affected work.
When I send an instruction, I want to know whether the agent can still include it in the deployed result. An acknowledgment should tell me that, beyond confirming receipt.
The first MVP could be text: the steps, the current position and the acknowledgment beside the affected step. A visual component can follow once the underlying state is reliable.
Proposed interaction
A flow to test. In this example, a new instruction targets the profile-links step.
- Read current state
- Prepare page
- Update profile links
- Review
- Deploy
- Received
- The system has captured the instruction.
- Included
- The instruction is attached to work that can still incorporate it.
- Needs another pass
- The relevant step is already complete, so this change needs another pass.
Where a change needs another pass
I would define when a change needs another pass. Updating a local file and deploying a public page are different events. The interface should show whether there is still time to include the change, without making me understand the agent’s internal machinery.
I would keep this as a proposal until the steps and acknowledgments reliably match the work. A reassuring but inaccurate status would leave me with the same uncertainty.
What I would test
I would give people a small change while an agent task is in flight. I would check whether they can tell if it was included, whether they repeat themselves less, and how often the task needs another deployment. I would also check whether maintaining the map slows the work down.
I would also check whether the map and acknowledgments stay accurate when an instruction arrives after its step has finished.
The unit of value is finishing
Paper IRL explores whether turning saved X bookmarks into finite, private editions helps readers return to material they already chose.
The unit of value is finishing
Paper IRL explores whether turning saved X bookmarks into finite, private editions helps readers return to material they already chose.
An edition that can be finished
People save posts because they want to return to them. The collection grows, but reading competes with the same feeds and interruptions that created the collection. Paper IRL starts with an edition that can be finished.
The reader has already chosen the material by saving it. I want to turn it into a private physical edition they can sit down and finish.
What that changes
The saved material carries the reader’s intent, so the early product does not need to invent a ranking score. Source links and context help preserve what was saved. The edition needs to be readable and finite.
I would judge the early experience by whether a reader can sit down with an edition and finish it. I would add more material or more ways to browse only if they help with that. I still need to test whether this changes reading behavior.
Printing and delivery would remove another chore. I can promise them only after the order path works; producing a print-ready file is one step.
Prototype evidence and the next test
The prototype records describe HTML and PDF files with access scoped to the owner, an explicit sample mode and output that preserves the source material. Whether readers finish more material, and whether paid fulfillment works, remain untested.
A useful next experiment would compare what people finish from a small edition with what they revisit in their ordinary save queue. I would also ask whether the edition earns a recurring place in their week.
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