Our research team released a technical paper detailing the memory architecture that powers WebAir AI. The core challenge: how do you maintain conversational context when the underlying model changes between turns — while keeping all memory private and secure?
The problem
Most multi-model setups treat each model as a stateless endpoint. Context is either duplicated into every prompt (expensive, lossy) or discarded (frustrating for users). Neither approach works at scale. And none of them let you bring your existing AI memory with you.
Our approach
We built a unified memory system with three tiers:
1. Session memory — short-term context for the active conversation, stored as structured summaries rather than raw transcripts.
2. Project memory — mid-term context scoped to a workspace, capturing decisions, preferences, and domain-specific terminology.
3. Organizational memory — long-term knowledge that persists across projects, including compliance rules, style guides, and institutional knowledge.
All memory is private and encrypted. It is never used for model training. Users can also import their existing memory from other AI tools — ChatGPT, Claude, or any other system — so switching to WebAir AI involves zero friction.
Results
In controlled evaluations, teams using unified memory completed multi-step research tasks 34% faster than teams using single-model chat with manual context management. Output consistency across model switches improved by 61%.
The full paper is available on our research page.