Blog
From the engineering team building KAPEX — the middleware that gives LLMs memory that matters.
GDPR, HIPAA, and CCPA impose specific requirements on AI memory systems that most vector-store architectures cannot meet. Here is what compliant architecture looks like — and a practical implementation checklist.
AI companion apps are growing fast but hiding a structural flaw: stateless infrastructure that can't support the relational depth users actually need. Here's what's breaking — and what the fix looks like.
Salience scoring surfaces what matters most to a user — not just what's most similar. Learn why it's the missing layer in most AI memory systems.
MCP servers let AI models connect to any external tool or data source through a single open standard. Here's what they are and why your AI needs one.
Every LLM call is stateless by design. But applications serving real users over time need state. Here's how to architect the transition.
Enterprise LLM memory requires more than a vector store. This guide covers architecture, GDPR/HIPAA/CCPA compliance, and the procurement checklist.
Measuring whether AI memory actually improves outcomes requires more than vibes. Here's how to set up a real A/B test and what metrics actually matter.
Larger context windows don't solve the memory problem — they solve a different problem. Here's why conflating the two leads to expensive architectural mistakes.
A practical guide to adding persistent memory to any LLM app — covering architecture patterns, key trade-offs, and what most implementations get wrong.
Larger context windows don't solve the memory problem — they solve a different problem. Here's why conflating the two leads to expensive architectural mistakes.
A practical guide to adding persistent memory to any LLM app — covering architecture patterns, trade-offs, and what most implementations get wrong.
A blinded study across three cohorts. Users didn't know which panel had memory. By session 20, they chose the memory-equipped AI four out of five times. We share the methodology, the numbers, and what surprised us.
Every LLM conversation starts from zero. Context windows are getting larger, but bigger isn't smarter. Here's why the real problem isn't window size — it's the absence of prioritization.
RAG retrieves documents. Memory middleware retrieves relevance. They solve different problems — and most teams are using one when they need the other. A side-by-side comparison.
Not all information is equally important. Salience scoring quantifies what matters — and lets an AI system prioritize memories the way humans do. An introduction to the concept behind KAPEX.
Claude today, GPT tomorrow, open-source next quarter. The companies building durable AI products are the ones that can swap models without rewriting their stack.
Memory makes AI more useful — and more dangerous. Crisis detection, anti-fabrication guards, PII scrubbing, and trigger-aware retrieval. How to build a memory system you can trust.
Model Context Protocol is how AI systems connect to external capabilities — databases, APIs, memory. Here's what MCP is, how it works, and why it matters for memory middleware.
Agents that can browse, code, and plan are impressive. Agents that remember what they learned last week are transformative. The case for persistent memory in agentic AI.
SaaS-hosted memory means your users' conversations live on someone else's servers. For healthcare, finance, and government — that's a non-starter. The self-hosted alternative.
We went from rule-based chatbots to transformers to agents. The next shift is statefulness — AI that accumulates understanding over time. Here's what that future looks like.
Forgetting isn't a bug — it's a feature. Inspired by Ebbinghaus and cognitive science, memory decay ensures AI surfaces what matters now, not everything it's ever seen.
Vector databases, RAG pipelines, conversation logs, memory middleware — the landscape is crowded. A framework for evaluating what your organization actually needs.
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