Rukmer Inc

Rukmer Inc

🧠 Give AI a memory. Give your business an edge.

Coming soonLaunches Monday, Oct 12 · in 11 days

AI & Machine Learning·Data & Infrastructure

2 people waiting for launch day

About

Rukmer — The Memory Layer for AI Agents

AI has intelligence. But intelligence without memory is limited.

Every business generates an enormous amount of knowledge every day—customer conversations, emails, meetings, decisions, documents, project updates, internal discussions, files, and workflows. Yet this knowledge remains fragmented across dozens of tools and disappears into disconnected conversations and data silos.

This is where Rukmer comes in.

Rukmer is the Memory Layer for AI Agents, built to give AI persistent, contextual, and actionable knowledge about how a business actually operates.

Instead of forcing teams to move their work into another platform or interact through yet another chatbot, Rukmer works across the tools businesses already use. It connects with applications such as Gmail, Outlook, Slack, Microsoft Teams, Notion, and other business systems, bringing fragmented organizational knowledge together into a continuously evolving layer of context.

Rukmer doesn't simply search for a document or retrieve a keyword.

It helps AI understand what happened, what was decided, why it happened, who was involved, what changed, and what should happen next.

At the core of Rukmer is a Company Brain—a continuously evolving representation of an organization's knowledge, decisions, relationships, processes, and institutional memory. This allows AI agents to move beyond isolated prompts and operate with a deeper understanding of the business.

What Rukmer enables:

🧠 Persistent Memory — AI can retain important knowledge, decisions, and context instead of forgetting after every interaction.

🔗 Cross-App Context — Connect information scattered across the tools your teams already use.

🎯 Contextual Retrieval — Deliver the right knowledge to an AI agent at the right moment, rather than overwhelming it with irrelevant information.

🏢 Company Brain — Transform fragmented organizational knowledge into a structured intelligence layer for the entire business.

⚡ Memory-to-Action — Turn accumulated context into recommendations, decisions, and workflow execution.

🔄 Continuous Context — Keep organizational intelligence evolving as new conversations, decisions, and information are created.

The bigger vision is simple:

Rukmer is building the intelligence infrastructure that allows AI agents to understand a business over time—not just respond to a prompt.

Imagine an AI agent that remembers the customer conversation from three months ago, understands the decision your team made last week, knows which document contains the latest requirements, connects that information with today's request, and uses the combined context to take the next action.

That's the difference between an AI that answers questions and an AI that understands your business.

Rukmer is building the layer that makes that possible.

Your business already has the knowledge. Rukmer gives AI the memory to use it.

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PricingUsage-based

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Joined Sep 2026

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2722 dispatches from hundreds of founders, pulled from the week's best podcasts.

Pricing
LTV at this stage I think is Fairyland I think it's like pitch deck metrics because the LTV in a business that's been around for two or three years or a business that just started to invest in growth it's a guess and you can extrapolate but it's not a really good system to understand in my mind your unit economics at that moment and so we've anchored the entire team in business to payback period

Manage paid spend by payback period — LTV at year 3 is a guess, not a metric

LTV projections for early-stage subscription businesses are extrapolations built on tiny cohorts — they feel precise but they are not. Ladder replaced LTV/CAC with payback period: how many months until gross margin from this subscriber recoups the acquisition cost? This metric is concrete, computable from real data, and drives the right daily decisions. Ladder hit profitability on a per-user basis in low single-digit months and could see it improving in real time.

Shipping
It was a problem I myself had experience with, so I knew there was something there. So I built the simplest MVP. I ended up building the MVP in about 2 weeks and I started marketing right away on Reddit. I got my first MRR and even more purely off Reddit.

Ship a 2-week MVP and start marketing on Reddit the same day

Diego compressed everything: 2-week MVP, no waitlist, no pre-launch — start posting on Reddit the day the product is usable. The same compression worked for first MRR and got him to $17K within four months. Marketing on day one is what made the speed pay off.

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