I help businesses build custom AI applications that automate manual work, cut operational costs, and deliver instant answers to customers. I also build beautiful, high-performance cross-platform mobile apps for both Apple iOS and Google Android from a single codebase using Flutter.
How your technical bottlenecks get converted into high-performance reliability.
A transparent, step-by-step roadmap from initial scope to live deployment.
Raw documents, PDFs, and database records are parsed into contextual chunks and converted into high-dimensional vector embeddings.
When a user asks a question, vector cosine similarity instantly retrieves the top relevant context snippets within 50 milliseconds.
The model analyzes the verified context and responds with strictly typed JSON schemas (Zod) or safely executes pre-approved backend tools.
Results are streamed in real time to your cross-platform Flutter mobile app (iOS / Android) or web dashboard with instant local offline cache.
Everything built to production standards with documentation, tests, and clean Git commits.
Transform your PDFs, manuals, and internal documentation into an intelligent semantic search system that answers questions accurately.
Intelligent agents capable of booking appointments, looking up customer records, querying databases, and sending automated emails.
Native-quality mobile applications built with Flutter and Dart, featuring smooth animations, offline storage, and biometric login.
Complete preparation, code signing, and submission assistance to get your app approved and live on the Apple App Store and Google Play Store.
Connect your CRM, email, and internal databases with custom Python microservices and n8n workflows for 24/7 hands-free operation.
Reduce your monthly OpenAI and Claude API bills by caching repeated queries and using optimized, compact system prompts.
Client: Nexus AI Systems • 90% Cost Reduction & 99.2% Search Accuracy Across 100,000+ Documents
Customer support agents spent up to 25 minutes locating policy details across hundreds of lengthy PDF manuals, leading to delayed replies and high support payroll costs.
I built an end-to-end enterprise RAG knowledge engine using Python, vector embeddings, and a custom automated n8n workflow. Support agents now receive exact answers with cited page numbers in under 2 seconds.
< 350ms
Response Time
90% Cost Cut
Cost Savings
99.2%
Retrieval Accuracy
Work directly with a senior engineer. Full IP and code ownership from day one.
Great for AI prompt engineering, API integrations, and mobile updates
Work directly with an experienced AI and mobile developer to build custom tools, add LLM features, or improve your Flutter mobile app.
Great for complete RAG knowledge systems or cross-platform mobile apps
Fixed scope, guaranteed delivery date, and complete deployment to cloud servers or app stores.
Direct answers to questions founders ask before hiring.
I use an architecture called Retrieval-Augmented Generation (RAG). Instead of letting the AI guess from memory, the system first searches your verified business documents and provides the exact text as proof. The AI is strictly instructed to only answer using those facts and to cite the exact source document. If the answer is not in your documents, it honestly states that the information is unavailable.