Your AI doesn't understand your business. Ours builds a world model of it.
Every AI tool you've tried stores facts in a flat list. That's why chatbot memory feels like a parlour trick instead of a real advantage. We built something fundamentally different: a world model — a structured, agent-maintained representation of your entire business.
The world model doesn't just remember. Agents maintain it continuously — rescoring risks, pulling metrics, extracting knowledge — so it stays current without you lifting a finger. Here's how it works.
What this looks like in practice.
The difference between flat memory and a knowledge architecture shows up in every interaction.
Recall vs. relevance
Other AI tools
Remembers facts when you ask. Can't determine which of your 50 stored facts matter to the decision you're making right now.
Launcherly
A knowledge graph with domain tagging and temporal weighting surfaces what's relevant to your current context — before you ask.
Facts vs. relationships
Other AI tools
Stores 'ICP is mid-market CFOs' and 'Customer X loved the reporting' as separate, unconnected facts.
Launcherly
Connects entities, decisions, and evidence into a graph — so it knows that Customer X's feedback validates (or contradicts) your ICP hypothesis.
Static vs. temporal
Other AI tools
Treats every remembered fact as equally true, whether you said it yesterday or eight months ago.
Launcherly
Facts carry temporal weight. Financial data decays faster than biographical context. Superseded facts are marked, not deleted — so the system knows what changed and when.
Passive vs. autonomous
Other AI tools
Memory is a convenience feature. It makes the next chat slightly easier but doesn't compound into deeper understanding.
Launcherly
Every conversation enriches the world model. Background agents maintain it continuously — rescoring risks, pulling metrics, flagging anomalies. The model works for you even when you're not using it.
World model architecture
Three layers of structured understanding.
Graph-based knowledge representation, temporal awareness, and domain-specific intelligence — the foundation that agents reason over, maintain, and act upon.
Knowledge graph
A living graph of entities, relationships, and context — not flat chat logs.
- A dedicated extraction agent identifies entities (people, companies, decisions) and the relationships between them from every conversation
- Entity resolution prevents fragmentation — your CTO mentioned in January and your CTO mentioned today resolve to the same node
- Cross-conversation bridging connects insights automatically, so a customer insight from Research enriches a Strategy conversation
- Hybrid retrieval combines vector similarity with graph traversal — finding not just what's relevant, but what's connected to it
Temporal awareness
Facts aren't just stored — they're dated, weighted, and superseded.
- Every fact carries temporal metadata — when it was learned, last confirmed, and whether it's been superseded by newer evidence
- Category-specific decay rates: financial data decays faster than biographical context, so stale metrics don't mislead your agents
- Contradiction detection compares new information against existing knowledge and resolves conflicts automatically
- Your agents know the difference between what you said yesterday and what you said six months ago — and weight their guidance accordingly
Domain intelligence
Memory organized by signal type — customer, market, strategy, product, metrics, and more.
- Eight signal domains ensure customer insights, market intelligence, strategic decisions, and operational data are categorized at extraction time
- Domain-specific extraction captures exact customer quotes from research sessions and key metrics from analytics discussions
- Domain filtering surfaces the right memories per agent — your GTM Lead sees distribution data, your Product Advisor sees feature decisions
- Cross-domain pattern detection identifies connections that span categories, like how a customer insight affects your roadmap and go-to-market
Your tools feed the world model.
Conversations are only half the picture. Launcherly connects to the tools you already use and agents pull real data into your world model automatically — so every agent works with facts, not whatever you remembered to paste in.
GitHub
Shipping velocity, open issues, release cadence
Stripe
Revenue, MRR trends, churn signals, plan distribution
PostHog
Feature adoption, funnel conversion, user behaviour patterns
HubSpot
Pipeline stage, deal velocity, contact engagement
Every data point from a connected tool is extracted, structured, and linked to the same entities in your world model — so when your Strategic Advisor discusses pricing, it's informed by your actual Stripe revenue, not a number you mentioned three weeks ago.
Agent architecture
The world model is only half the story.
Having a great world model means nothing if agents don't know how to use and maintain it. Three principles determine how agents reason over the model and keep it current.
Spatial awareness
Every agent starts each interaction with a lightweight orientation — a snapshot of your business state. They see the shape of your context before diving into detail, so they ask better questions from the first message.
Progressive context
Agents earn their context rather than being overwhelmed by it. They start with orientation, discover what's available, then retrieve specific details only when needed. This keeps conversations focused.
Autonomous maintenance
Background agents tend the world model continuously — rescoring risks as evidence arrives, pulling metrics from connected tools, extracting knowledge from conversations. The model stays current without the founder lifting a finger. When your Research Lead discovers a pattern, your GTM Lead already has that context.
Your world model stays yours.
A living world model requires trust. Your business data is encrypted at rest and in transit, hosted on EU infrastructure, and never used to train AI models. Each organisation's world model is completely isolated — no cross-tenant data sharing, no exceptions. All tool integrations use read-only OAuth scopes — we pull data in, but never write to or modify your connected tools.
FAQ
Frequently asked questions
- What is a world model and how does Launcherly use it?
- A world model is Launcherly's structured representation of your entire business — strategy, risks, evidence, metrics, competitors, and the relationships between them. Unlike flat memory, it's a living system that agents maintain continuously: rescoring risks, pulling metrics from your tools, and extracting knowledge from conversations. Think of it as your business's operating system, not a chat log.
- What tools does Launcherly integrate with?
- Launcherly integrates with GitHub (shipping velocity, open issues, release cadence), Stripe (revenue, MRR trends, churn signals), PostHog (feature adoption, funnel conversion, user behaviour), and HubSpot (pipeline stage, deal velocity, contact engagement). All integrations use read-only OAuth scopes. Each connected tool feeds agents that automatically enrich your world model.
- What is temporal decay and why does it matter?
- Temporal decay means that facts lose relevance over time at different rates depending on their category. Financial data from last quarter matters less than your company name. Launcherly applies category-specific decay rates so agents always prioritise current information over stale data.
- How is a world model different from flat AI memory?
- Flat memory stores a list of facts with no structure. A world model stores entities and their relationships in a knowledge graph, enables multi-hop reasoning, and is maintained by autonomous agents. For example, flat memory knows your churn rate and your ICP separately. The world model connects them — and an agent rescores the associated risk when either changes.
- How does the world model compound over time?
- Every conversation, tool sync, and agent run adds to the world model. After a month, Launcherly understands your business like a new hire. After three months, it traces connections between decisions and outcomes that span quarters. And because agents maintain it autonomously, the model stays current even during weeks you're heads-down building.
- How does Launcherly handle data privacy and security?
- All data is encrypted at rest and in transit, hosted on EU infrastructure, and never used for AI model training. Each organisation's world model is completely isolated with no cross-tenant data sharing. Tool integrations use read-only OAuth scopes — Launcherly reads your data but never modifies your connected tools.
See what AI can do with a world model.
Connect your tools, start a conversation, and watch the world model build. The difference isn't intelligence — it's structured understanding that agents maintain and act upon.
Free to start. $25/month when you're ready.