Applied AI research from building real systems.
These white papers capture the architectural patterns, product decisions, and engineering lessons behind CitronLab's work: how to make AI faster, more reliable, more private, more contextual, and more useful inside real workflows. This is applied research, not abstract AI commentary.
Consensus-Based AI Pre-Processing
A pattern for combining deterministic rules and consensus validation before LLM analysis, so the AI receives verified facts and uses its reasoning capacity for context rather than guessing.
Temporal Memory
A time-aware memory architecture where facts have relevance, half-lives, reinforcement, anchors, and decay — helping AI systems handle changing context more like humans do.
Two-Phase Streaming
A streaming architecture that shows deterministic results quickly while AI enrichment continues in the background, making AI products feel faster and more useful.
Cold Start Zero
A practical look at when Rust can reduce cold-start latency and cost for real-time serverless workloads where every millisecond affects the user experience.
On-Device LLM
A privacy-by-architecture argument for running capable AI locally on modern devices, especially for sensitive personal, financial, professional, or proprietary data.
Proactive AI
An architecture for AI that monitors context and surfaces relevant information before the user asks — using knowledge graphs, temporal relevance, and privacy-aware context awareness.
Research that shows up in the products we build.
Our white papers describe patterns we use when building real systems: pre-processing facts before AI reasoning, streaming useful results before LLM calls finish, designing memory around time and relevance, choosing private or on-device architectures for sensitive data, and building proactive systems that surface useful context without becoming intrusive.