Stream the response
Progressive rendering lets users begin reading while the model is still working, reducing perceived latency without disguising it.
Case study 01 / AI product
Synaptic Insight Engine
A conversational research tool designed to preserve context, return useful answers quickly, and keep the interface out of the user’s way.
01 / The problem
Useful AI depends on more than the model.
Early versions could answer a question, but they did not yet feel like a dependable product. Complex responses arrived too slowly, conversation state was fragile between sessions, and repeat API calls created unnecessary cost.
The real challenge was orchestration: make the system feel fast, preserve context, and control usage without hiding failure states from the person using it.
02 / Key decisions
Persist the conversation
Supabase stores sessions and message history so users can return to prior work without reconstructing context.
Cache repeated work
Request caching and batching reduce redundant model calls while preserving fresh responses where they matter.
03 / Results
lower perceived response latency through streaming
conversation-session load time
fewer API calls after caching and batching
Figures are based on the project documentation supplied for this portfolio.
04 / Takeaway
The interface should make the system easier to trust.
This project reinforced a principle that carries across Ryan’s work: the difficult technical decisions matter most when they improve the user’s understanding, continuity, or control.