Founder
A personal AI for running a company you cannot hold in your head
Running a small company means holding more context than any one person comfortably can. This is the part a personal AI is genuinely good at.
- Situation
- Running a small company and preparing for a fundraising round.
- Brought in
- The pitch deck and the financial model
- Notes from investor calls as they happen
- A document describing the strategy for the next twelve months
- First ask
- “Which parts of the deck are weak given what these three investors care about most?”
- Result
- Specific edits tied to real investor priorities rather than generic pitch advice. The conversation stays open through the round, so each new call adds to the picture instead of starting a new one.
- Why it helps
- The strategy, the deck and the call notes sit in one place. The next question does not need to reintroduce any of it.
How it unfolds
Set the ground once
Bring in the strategy document, the deck and the model. This is the context every later question would otherwise need pasted in front of it.
Let the round accumulate
Add notes after each investor call. The picture of what this particular room of people cares about builds itself.
Ask the uncomfortable questions
Where the story is thin, which numbers will get pushed on, what you have quietly stopped mentioning. Grounded in your own material, not in pitch advice from the internet.
Keep the thread through the whole round
Weeks later, the conversation still knows what was said in week one. So do you, without going back through your notes.
Further questions in this journey
- “What did I promise in that call that is not in the plan?”
- “Where does the model disagree with the deck?”
- “Summarise what these investors have consistently pushed on.”
Capabilities that carry this journey
Your data in one place
Storage, connections and imports: one private hub, owned by you, that the AI reasons over.
Memory, notes and glossary
Notes you deliberately capture, plus a glossary that keeps your world consistent.
Delegating work
Research, drafts and analysis become tasks with a lifecycle, not one-off replies.
Daily summary and watches
A morning rollup of what changed, and standing monitors for the things you asked it to watch.
Background reading: ai comes to your data, not the other way around.