AI Integration Consultant
Add AI to a product without treating the model as the whole system. Get help with LLM workflows, AI chatbots, retrieval, automation, codebase triage, human handoff, backend integration, and production-ready AI architecture.
Useful AI integration is rarely just adding a chat box. The surrounding software has to retrieve the right context, protect the wrong data, log decisions, handle fallbacks, control costs, and know when a human should take over. This work is for teams adding AI to an existing app, MVP, internal workflow, or technical rescue project.
Add AI assistance to a mobile app, web app, portal, or dashboard without destabilizing the core product or exposing the wrong data.
Connect AI to approved documents, site content, codebase context, internal knowledge, or structured business data.
Automate repetitive drafting, triage, summarization, classification, or intake steps while keeping review paths clear.
Stabilize a prototype that works in demos but lacks backend structure, auth, logging, permissions, evaluations, citations, or reliability.
Define when the system should escalate to a person instead of pretending every AI answer is safe to act on.
Evaluate model APIs, local/self-hosted models, retrieval architecture, rate limits, hosting, and cloud/local tradeoffs.
Built a local-first coding AI portal concept with BVT-specific retrieval, custom evaluation, rate limits, safety rules, FastAPI, Ollama, and human handoff.
View ProofIntegrated Pryon SDK into a React Native app with Auth0, Firebase, progressive AI responses, clickable citations, Vimeo context, Expo, TypeScript, and cross-platform behavior.
Worked on an AI-powered virtual fitting room iOS app using SwiftUI, Apple TrueDepth camera APIs, body measurement logic, and product-size comparison.
View ProofImplemented Apple Vision body-analysis workflows, TensorFlow.js background-removal endpoints, DALL-E placeholder-image generation, and algorithmic AI opponent logic in client projects.
Identify where AI should help, where deterministic software is safer, and what humans still need to review.
Design the data flow around model calls, retrieval, citations, permissions, storage, logging, rate limits, and backend APIs.
Build AI-backed features inside existing mobile apps, web apps, internal tools, portals, and operational systems.
Turn an AI demo into a more reliable system with clearer prompts, context, evaluation checks, and failure behavior.
Maybe. The first step is identifying a real workflow bottleneck, not picking a model.
Yes. Many prototypes need retrieval, backend structure, logging, permissions, observability, or clearer product boundaries.
Yes when it fits. Some systems belong on hosted APIs, while others may benefit from local or self-hosted model workflows.
Technical leadership for AI strategy, architecture, vendor decisions, and product planning.
View ServiceHealthcare workflow consulting where AI may support intake, triage, search, or documentation.
View ServiceLocal senior consulting for apps, AI features, MVPs, and codebase rescue.
View ServiceContact me about an AI feature, GPT integration, retrieval system, internal tool, MVP, or technical rescue project that needs practical architecture and implementation support.
Contact Me About Your AI Feature
Verified reviews from real projects
“Amazing in communication.”
Client · iOS App (Swift & Firebase)
“Went above and beyond.”
Client · Firebase Integration Revamp
“It was great working with Bill! Very pleasant and knowledgeable.”
Client · Language Learning App