AI Memory is for teams that need a result they can measure. The next edge in AI products is memory. Generic AI answers the same question the same way every time. AI with memory knows the user, their history, and their preferences. We build the memory layer, context system, and personalisation engine that turn a feature into a trusted assistant.
A typical engagement includes User Memory Layers, Personalisation Engines, and Long-Term Context Management. Delivery focuses on AI Assistants & Copilots and E-commerce Personalisation. You also get Users never repeat themselves, Engagement and retention measurably improve, and GDPR-compliant memory architecture.
Senior engineers who have shipped similar systems do the work. Common technical work includes Vector Memory Stores, Memory Hierarchy Design, and Privacy-Safe Memory. Open the sections below for scope, or contact us with the outcome you need.
We store preferences, past chats, goals, and context. That memory is pulled into every response so users do not repeat themselves.
We model each user and adapt replies, recommendations, and UI in real time. The product feels built for that person.
We go past token limits with vector search, summaries, and retrieval. The model can use months of history without breaking the window.
The system learns likes, dislikes, and needs from behaviour. Users do not fill out a long settings page or onboarding quiz.
We store interactions as embeddings in Pinecone, Weaviate, or pgvector. Similarity search then returns the most relevant past context.
We split memory into events, facts about the user, and preferences. Each layer is retrieved at the right time.
Users consent, and each person’s data stays isolated. They can view, edit, and delete stored memory. GDPR and CCPA are built in.
APIs inject user context in under 50 ms. Personalisation does not slow the response.
The assistant remembers yesterday’s work, preferences, and open projects. Users get real continuity, not a blank chat.
The model knows style, size, budget, and history. Recommendations are personal, not just collaborative filtering.
Tutors remember knowledge gaps, pace, and preferred explanations. Each lesson matches where that student is.
Clinical tools remember history, medication sensitivities, and care preferences. Support stays context-aware and safer.
Each engagement is scoped to your goals, but most projects include User Memory Layers, Personalisation Engines, Long-Term Context Management, and Preference Learning Systems. We tailor deliverables to your product stage, team capacity, and business priorities.
Project timelines depend on complexity, integrations, and existing systems. Most engagements start with discovery and scoped planning, followed by iterative delivery milestones so you can review progress and ship value early.
Our stack is chosen based on your context, and commonly includes Vector Memory Stores, Memory Hierarchy Design, Privacy-Safe Memory, and Real-Time Personalisation APIs. We prioritize maintainability, performance, and long-term ownership by your internal team.
We follow a delivery process centered on AI Assistants & Copilots, E-commerce Personalisation, and Ed Tech & Learning Platforms and back it with Users never repeat themselves, Engagement and retention measurably improve, and GDPR-compliant memory architecture. You receive clear documentation, production-ready handover, and ongoing support options after launch.