On-device ML and server-side LLM pipelines — shipped to production.
AI features that actually ship: Core ML for on-device inference, server-side LLM pipelines via OpenAI / Anthropic / Gemini, RAG, and conversational UX patterns. Not prototypes — production-grade implementations with streaming, error handling, and graceful fallbacks.
What's included
On-device inference with Vision, NLP, and custom .mlmodel packages. Runs offline, zero latency, no PII leaves the device.
OpenAI, Anthropic, or Gemini API wiring with streaming response rendering and token-budget management.
Retrieval-augmented generation: vector store, embedding pipeline, and context injection for domain-specific chatbots.
Streaming text rendering, typing indicators, error states, and graceful fallback when the model is unavailable.
Embedding-based search replacing keyword search — dramatically better results for unstructured content.
Audit of existing AI features for latency, cost, error rate, and user experience quality.
How it works
1-2 day spike to validate the AI approach: latency, cost, accuracy, and offline requirements before committing to build.
Streaming, retry logic, rate-limit handling, cost guardrails, and monitoring hooks — not just the happy path.
Prompt engineering, model selection, and latency optimisation with measurable before/after benchmarks.
Is this right for you?
Product teams adding AI features
You need an iOS engineer who can own both the ML layer and the product UX, not two separate contractors.
Apps with large content libraries
Semantic search, AI-powered recommendations, and intelligent filtering — transformative for content-heavy apps.
Customer-facing AI experiences
Booking assistants, support bots, onboarding flows — conversational UX requires iOS-specific implementation expertise.
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