Case Study
Generative AI
Multimodal AI Chat Platform
A chat platform handling text, image, voice and document inputs across three frontier models — built in eight weeks.
At a glance
What this shipped
The numbers that mattered to the client — measured before and after.
The problem
What we were called in to fix
The client, an AI startup, needed a single product where users could chat across modalities — paste an image and ask about it, transcribe a voice note, upload a PDF and query it — using whichever of GPT-4o, Claude or Gemini was best for the job.
Their existing prototype could do one model with text-only. Latency was several seconds, conversation memory was broken, and adding a new modality meant rewriting half the stack.
Our approach
How we actually built it
No magic — just the right architectural calls in the right order.
We rebuilt the message pipeline around a provider-agnostic interface so model selection, model swap and cost routing all happen in one place.
Streaming-first architecture: tokens render the moment the LLM emits them. No spinner-on-spinner experience.
Modality handlers (text, image, audio transcription, document RAG) compose cleanly — each one is a small, testable module rather than a special case in the chat loop.
Conversation memory is durable and queryable in Postgres, with a summary-on-overflow strategy so long threads stay coherent without blowing the context window.
The outcome
What changed for the client
First-token latency under 800ms across all three providers, end-to-end.
Added Claude 3.5 Sonnet as a fourth model behind the same interface in under two days.
Reduced inference cost per conversation by 38% via smart model routing — cheap models for cheap turns, frontier models when complexity demands it.
Tech stack
Every meaningful piece
“We don't do generic case-study writeups. Want the unredacted version with names, screenshots and architecture diagrams? We share those on a call.”
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