You've noticed every AI tool forgets what matters.
It's not a bug in the model — it's a fundamental architecture problem. 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. 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 Superseded facts are marked, not deleted — so the system knows what changed and when.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. compounding
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 graph. Corrections improve future guidance. Cross-session patterns emerge automatically. Day 100 is dramatically more valuable than day one.
Memory architecture
Three layers of structured memory.
Graph-based knowledge representation, temporal awareness, and domain-specific intelligence — working together in a single, cohesive architecture.
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 graph.
Conversations are only half the picture. Launcherly connects to the tools you already use and pulls real data into the same knowledge graph — so your agents work 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 knowledge graph — 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
Memory is only half the story.
Having great memory means nothing if agents don't know how to use it. Three principles determine how context flows from memory into conversation.
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.
Coordinated specialists
Five conversational agents and over a dozen background agents share a common understanding through structured context layers — OKRs, risks, evidence, findings. When your Research Lead discovers a pattern, your GTM Lead and Product Advisor already have that context.
Your knowledge stays yours.
A rich memory system requires trust. Your business data is encrypted at rest and in transit, hosted on EU infrastructure, and never used to train AI models. Each organization's knowledge graph 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 knowledge graph and how does Launcherly use it?
- A knowledge graph is a data structure that stores entities (people, decisions, metrics) and the relationships between them. Launcherly uses a knowledge graph to connect insights across every conversation and tool integration — so when your Research Lead discovers a pattern, your Strategic Advisor already has that context.
- 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.
- What is temporal decay and why does it matter for AI memory?
- 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 knowledge graph different from flat AI memory?
- Flat memory stores a list of facts with no structure. A knowledge graph stores entities and their relationships, enabling multi-hop reasoning. For example, flat memory knows your churn rate and your ICP separately. A knowledge graph connects them — revealing that churn is concentrated in a segment outside your ICP.
- How does Launcherly's context compound over time?
- Every conversation, tool sync, and decision adds nodes and edges to your knowledge graph. After a month, Launcherly understands your business like a new hire. After three months, it traces connections between decisions and outcomes that span quarters. The value grows combinatorially, not linearly.
- 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 knowledge graph 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 real context.
Connect your tools, start a conversation, and watch the knowledge graph build. The difference isn't intelligence — it's context that compounds.
Free trial available. Plans from $29/month.