- Designed a 3-stage multimodal reasoning orchestration pipeline that transforms raw image/audio inputs into intent-aligned, context-aware textual outputs
- Built dual-modality ingestion (image + audio) with automatic type detection and routing
- Implemented modality reduction layer to convert raw binary signals (vision/audio) into high-fidelity textual representations for downstream reasoning
- Added an intermediate analytical reasoning layer to normalize outputs, suppress hallucinations, correct OCR/ASR artifacts, and extract verified entities and facts
- Engineered a multi-intent synthesis engine supporting distinct processing pathways (Describe, Technical Analysis, Simplify, Summarize) with controlled tone, vocabulary, and output constraints
- Designed intent-aware prompt orchestration, ensuring deterministic adherence to user-selected cognitive objectives rather than single-shot LLM responses
- Integrated real-time end-to-end latency instrumentation, measuring full pipeline execution time (upload → final render) to identify orchestration bottlenecks
- Implemented token usage estimation per intent pathway, enabling comparative analysis of computational cost across reasoning strategies
- Added in-memory request-level caching to eliminate redundant multimodal processing for identical inputs, significantly reducing recomputation and perceived latency