Question 1

What does 'AI-native' mean compared to adding AI to an existing app?

Accepted Answer

AI-native means intelligence is designed into every layer of the application from the start — data models, workflows, interfaces, and decision logic all built around AI capabilities. Bolting AI onto an existing system means wrapping a chatbot around static software. AI-native applications make fundamentally different architectural decisions.

Question 2

Do we own the source code?

Accepted Answer

Yes, 100%. Every line of code, every fine-tuned model weight, every pipeline configuration. You own the full intellectual property. No licensing fees, no vendor lock-in, no ongoing royalties.

Question 3

What LLMs can you deploy on our infrastructure?

Accepted Answer

We deploy any open-source LLM that fits your use case — Qwen3, DeepSeek-R1, Llama, Mistral, Phi, Gemma, and domain-specific models. We use vLLM as the inference engine for production throughput. Model selection depends on your GPU capacity, accuracy requirements, and domain.

Question 4

How long does a typical engagement take?

Accepted Answer

8–12 weeks for a standard custom AI application. Complex multi-agent systems or large-scale deployments may take 12–16 weeks. We provide a detailed timeline after the discovery phase.

Question 5

What if we don't have GPU infrastructure yet?

Accepted Answer

We handle that. We specify GPU servers, networking, cooling, and deployment architecture. We help you procure or configure the hardware foundation before any software deployment begins.

Full-Stack AI.
Your Architecture.

Start Your AI Architecture

Full-Stack AI.Your Architecture.

Start Your AI Architecture

Full-Stack AI.
Your Architecture.