Why most AI systems forget everything — and how to fix it. We walk through the cognitive principles behind memory systems and implement a working prototype with your team. Our signature workshop.
Ask any team what frustrates them about their AI systems, and "it forgets everything" is near the top of the list. The assistant that made the same mistake last week makes it again today. The agent that learned a customer's preferences yesterday asks for them again tomorrow. Every session starts from zero — and every user feels it.
Bigger context windows don't solve this; they postpone it. Real memory is an architecture: distinct memory types with different jobs, a consolidation process that turns raw experience into lasting knowledge, and retrieval that surfaces the right memory at the right moment. Human cognition solved this problem millions of years ago — episodic, semantic and procedural memory working together — and it remains the best blueprint we have.
This is our signature topic — the foundation of our CLS-Engram research and the third of our three pillars. In this deep-dive, your team doesn't just hear the theory: you implement a working memory layer against your own stack before the day ends.
Episodic, semantic and procedural memory: what each type stores, why the separation matters, and what breaks when systems blur them into one vector store.
Context stuffing, plain RAG, summary chains — where each approach works, where it collapses, and how to recognise the ceiling before you hit it in production.
The pipeline from raw interaction to lasting, structured knowledge: what to keep, what to compress, what to forget — and when to run it.
Relevance beyond similarity: recency, salience and context signals combined, so the system surfaces the right memory instead of the nearest embedding.
The core of the day. In guided pairs, we implement a working memory prototype against your actual stack and data model — not a toy repo.
The right to be forgotten when knowledge is derived from many interactions, retention design, and documentation duties for learning systems.
No. Before every workshop we run a 30-minute briefing call and tailor examples, exercises and depth to your industry, your stack and your team's starting point. The agenda above is the frame — the content inside it is yours.
Pricing depends on group size, location and how much customisation your context needs. We'll give you a fixed quote after the briefing call — no surprises, no hidden day rates.
Both work. On-site tends to be stronger for group discussion and hands-on exercises; remote splits well into two half-sessions. We run workshops across Europe in English or German.
The hands-on block assumes participants can code comfortably in Python or TypeScript. Mixed groups work well — architects and product leads follow the design modules fully and pair with engineers in the build block.
A free 30-minute get-to-know call — we look at your situation, confirm this is the right format and find out if it’s a fit. No pitch, no commitment.
Book a Free Call →Prefer to write it down first? Tell us about your team and what you're working on — we'll get back to you within 24 hours.