Services / AI Engineering
AI engineering that ships.
On-device models, cloud LLMs, and the bridges between them.
The architectural call about what runs on-device, what runs in the cloud, and where the boundary sits — answered against your latency, cost, and privacy budget. We then wire OpenAI, Anthropic, and Gemini APIs (or Core ML, RAG over Pinecone / pgvector / Weaviate, MCP servers, SSE-streamed chat with function calling) into a shipping product, not a demo.
Right call when…
- →You're moving from prototype to production AI features.
- →Latency, cost, and privacy push you toward hybrid architectures.
- →You need agents that actually do things, not chatbots.
Where we've done this.
Token-streamed AI chat client on iOS
Streaming LLM responses to a native iOS client
Structured-output ingestion from unstructured social content
Questions people ask about AI Engineering
Do you build with OpenAI/Anthropic/Gemini APIs, on-device, or both?
All three. We make the architectural call about what runs where, based on your latency, cost, and privacy budget.
What's the difference between API calls and proper AI engineering?
API calls are a starting point. Production AI is RAG over your data, prompt engineering as a discipline, agent orchestration, fallback handling, observability, cost control, and the architectural calls about hybrid (on-device + cloud) topology.
Do you do MCP server development?
Yes — we build MCP servers for tools, knowledge sources, and agent orchestration.