Individual and enterprise use scale at once
The same assistant has to serve a single user and enterprise-level load at the same time; capacity has to hold for both.
Understands, generates,recommends.
- Product
- Akıllı Asistan & İçerik Analiz Platformu
- Status
- Completed
Briefly
We built Akıllı Asistan & İçerik Analiz Platformu as a mobile app for text analysis, content generation, a voice assistant and real-time, data-driven recommendations — built on a high-performance API layer with caching, and scaling from individual to enterprise use.
- Approach
- An AI assistant that scales
01Constraints
Three constraints shaped the design.
Response latency depends on caching
Text analysis and content generation can be slow; without caching, response time becomes unusable.
Text, voice and recommendations must stay consistent
Whether a user arrives by text, voice or the recommendation screen, the assistant's behavior can't contradict itself.
02 — Approach
One assistant, four capabilities.
An assistant built on a high-performance API layer with caching: it analyzes the user's input (text or voice), generates content, and offers recommendations based on real-time data. The same infrastructure scales from individual use to enterprise load.
- 01User inputtext or voice
- 02Analysis / generationlanguage model
- 03Recommendation enginereal-time data
- 04Response / recommendationin the app
{ "request_id": "AA-2026-004512", "input_type": "voice", "action": "generate_recommendation", "cache_hit": true, "duration_ms": 340}{ "request_id": "AA-2026-004512", "input_type": "voice", "action": "generate_recommendation" }
03Screens
A response should feel as fast as conversation.
Recommendation feedlive
Next case
Doksan9