Question 1

How do you handle LLM lag?

Accepted Answer

We use streaming responses, optimistic UI states, and edge functions to keep the experience feeling snappy even when the model is slow.

Question 2

How do you prevent AI costs from spiraling?

Accepted Answer

We implement strict token limits, caching layers for common queries, and use smaller, specialized models for basic tasks to keep your API bills under control.

Question 3

Can you build apps that run models locally on the iPhone?

Accepted Answer

Yes. For privacy or offline use, we can deploy quantized models that run directly on Apple's Neural Engine, avoiding server costs entirely.

Question 4

How do you handle AI 'hallucinations' in a production app?

Accepted Answer

We use a mix of RAG (Retrieval-Augmented Generation), strict system prompts, and human-in-the-loop verification to ensure the AI stays grounded in your data.

Question 5

Can you integrate with our existing vector database?

Accepted Answer

We work with all major providers like Pinecone, Weaviate, and Supabase pgvector. We can help you optimize your embedding strategy for better retrieval.

Question 6

What's the best way to handle long-running AI tasks in the UI?

Accepted Answer

We use background workers and real-time status updates so users aren't left staring at a loading spinner while the model processes complex data.

Production-Grade AI Applications

Specialized Solutions

Industry FAQs

Ready to build
something exceptional?