base - The system of intelligence for lean founding teams
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Base gives lean founding teams the operating layer of a much larger company. It is a command interface and system of intelligence, five layers:
Setup, Orchestration, Company Brain, Memory, Intelligence.
Instruct it in natural language to pull financial reports or draft investor updates, and it executes across your connected stack, getting sharper the longer you use it.
Vibe coding got you to a product. Base gets you to a business.
Replies
Hi Product Hunt,
I'm Caroline, founder of Base.
I've spent the last decade scaling four venture-backed businesses, three reaching unicorn valuation, and investing at a private equity fund. Sat on both sides of the table as an operator and an investor, the same thing kept showing up. Founders can ship a product in a weekend now, Lovable, Cursor and Bolt took care of that. Building the business behind it is a different problem entirely, and there's no equivalent shortcut for contracts, CRM, hiring and finance all landing at once with no team to handle them.
So I built the thing I wish someone had handed me. Base gives lean founding teams the operating layer of a much larger company. Instruct it in natural language to pull a report, draft an update, chase an invoice, and it executes across your connected stack.
Underneath sits a company brain built on a knowledge graph. Every contract, invoice, deal and conversation gets resolved into entities and relationships rather than sitting as isolated records in separate tools. That's what makes the difference between a tool that executes one command and a system that knows your business is behind on a payment, which deal's gone quiet, and what actually needs you today, without being asked. It's the part of Base that compounds. The longer you use it, the sharper it gets.
Come connect a tool or two, run it for a day, and tell me where it breaks. I'm reading every comment today.
Caroline
@caroline_hazlehurst really interesting launch🙌 vision behind Base is ambitious in the best way.
qq what underlying graph architecture keeps query latency sub-second when re-indexing complex entity relationships across multiple SaaS tools? Congrats for shipping @caroline_hazlehurst 👏
@vikramp7470 Great question. Today it's deterministic entity resolution on canonical tables rather than a graph database, a deliberate choice while volume is still building, since a graph layer earns its complexity cost at a different scale than where we are now. Per-adapter caching with webhook-triggered invalidation means most updates only touch the one tool that changed, rather than a full re-index. The architecture we're building towards is agent long-term memory with consolidation, then cross-tenant learning over time. Happy to go deeper, always good to talk to people who ask this kind of question.
@caroline_hazlehurst Solid explanation, appreciate the pragmatic approach decisions like these make all the difference.
@caroline_hazlehurst Congrats on the launch! Curious how far back it can look once you connect a tool, does it work with historical data or only from the day you connect it?
This looks awesome!! Exactly what I've been wanting to open up every morning.
@gpat_uk Thank you!! Appreciate the support and would love to hear how it goes once you've had a proper run with it !
@caroline_hazlehurst This is so needed. I’ve watched three friends ship a product then completely stall on the business side. Bookmarking to send them this.
"System of intelligence for lean founding teams" is a broad promise — curious what that looks like concretely day-to-day. Is it more about surfacing insights from existing tools, or does it actively suggest next actions?