Conception: AI-Powered Knowledge Web App

Delivering Real-Time AI with Verifiable Citations

Modern users expect AI responses instantly, yet they also demand transparency and source verification. The difficulty lies in streaming responses token-by-token while simultaneously extracting, validating, and formatting citations without slowing down the experience.

Streaming AI Pipeline with Parallel Citation Processing

We engineered a SSE-based streaming architecture where AI responses render incrementally for immediate feedback. In parallel, a background worker processes citation extraction and verification. Citations are injected seamlessly into the live stream with structured references appended at completion. Redis caching minimizes redundant lookups, ensuring citation intelligence without sacrificing speed.

Managing 25 Simultaneous Active Tabs Without Performance Loss

Power users operate across multiple research threads at once. Supporting 25 open documents or AI chats, each with independent state, requires careful memory management, instant switching, and zero data loss.

Virtualized Tab Architecture with Intelligent State Persistence

We built a virtual tab system that keeps only active sessions mounted while serializing inactive ones to persistent storage. State hydration occurs instantly during tab switching, supported by optimistic UI updates and auto-save safeguards every two seconds. This architecture ensures fluid multitasking without memory bloat or lag.

Rendering a Dynamic Knowledge Graph at Scale

Visualizing thousands of interconnected notes, chats, and tags requires heavy computational logic. Without optimization, graph layout calculations can freeze the UI and degrade user trust.

Worker-Driven Graph Engine with Hardware-Accelerated Rendering

Graph computations run inside Web Workers to keep the main thread responsive. A D3-based force simulation calculates layout physics, while Canvas rendering ensures smooth performance even with large datasets. Updates are debounced and animated for seamless transitions, maintaining 60fps interactions across complex knowledge maps.

Enforcing Freemium Limits Without Friction

A sustainable SaaS model requires precise usage tracking across searches, AI commands, uploads, and history retention. Limits must be accurate, real-time, and encourage upgrades, without frustrating users.

Centralized Rate Limiting with Intelligent Usage Metering

We implemented a Redis-backed rate limiting service that tracks usage dimensions in real time with automatic TTL resets. Before executing any AI action, tier validation ensures compliance. When limits are reached, users receive clear, contextual upgrade prompts, turning enforcement into a conversion opportunity rather than a barrier.

Maintaining Millisecond-Level Full-Text Search Performance

As workspaces grow to thousands of documents, search must remain instantaneous. Anything above 100ms risks breaking the flow of thinking.

Distributed Search Infrastructure with Incremental Indexing

Elasticsearch powers full-text search with customized analyzers for boosting, fuzzy matching, and semantic expansion. Incremental indexing ensures new content is searchable immediately. Cached frequent queries and optimized pagination maintain sub-50ms response times at the 95th percentile.

Embedding AI Directly into the Writing Workflow

AI assistance must feel native, not like a separate tool. Users should refine, expand, or summarize text inline without losing formatting, context, or undo history.

Context-Aware Editor Plugins with Streaming AI Replacement

Custom editor plugins extract contextual tokens around selected text and send structured prompts to the AI layer. Responses stream back directly into the document with smooth typing animation, preserving formatting and undo states. Clear quota indicators reinforce transparency across tiers.

Orchestrating Multiple LLMs Across Subscription Tiers

Different subscription tiers provide access to different AI models. The system must intelligently route requests, handle model downtime, and maintain cost efficiency without exposing complexity to users.

Tier-Aware Model Router with Automatic Fallback Logic

We developed a centralized AI orchestration layer that maps user tier to preferred models and fallback chains. If a model fails or rate limits, the router automatically retries with an equivalent-tier alternative. All requests are logged for token usage and cost analytics, ensuring financial visibility and operational resilience.