You will get a working AI agent or RAG prototype with production architecture
Top Rated
Project details
You will get a working AI agent or RAG prototype with production architecture in mind.
This project is for teams that want to move beyond a prompt demo and build a usable AI product foundation: an LLM workflow, retrieval layer, tool-using agent, backend/API integration, or prototype that can be handed off and extended.
Depending on the package, I can help define the architecture, connect the AI workflow to your data or tools, build the working prototype, provide source code, document the technical decisions, and prepare the handoff plan.
This is not model training or a generic chatbot package. The focus is product architecture: how the AI system gets context, calls tools, handles state, produces useful output, and fits into a real software product.
The result is a scoped AI foundation you can test, show to stakeholders, and use as the basis for the next implementation phase.
This project is for teams that want to move beyond a prompt demo and build a usable AI product foundation: an LLM workflow, retrieval layer, tool-using agent, backend/API integration, or prototype that can be handed off and extended.
Depending on the package, I can help define the architecture, connect the AI workflow to your data or tools, build the working prototype, provide source code, document the technical decisions, and prepare the handoff plan.
This is not model training or a generic chatbot package. The focus is product architecture: how the AI system gets context, calls tools, handles state, produces useful output, and fits into a real software product.
The result is a scoped AI foundation you can test, show to stakeholders, and use as the basis for the next implementation phase.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI-Generated Code, Conversational AI, Natural Language GenerationAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, StreamlitAI Models
ChatGPT, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$4,500
|
Advanced
$8,500
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 21 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | - | - | |
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | ||
Source Code | - |
Frequently asked questions
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RV
Ryan V.
Jun 19, 2026
Technical Assessment of App Prototype — Paid Test Task (Fractional CTO Hiring)
Completed task well in advance and to a high level.
RS
Rudy S.
Jun 8, 2026
Full Stack Web Development React JS & Laravel PHP
VT
Vangelis T.
Jul 31, 2024
Senior Flutter Developer
Felix is a full stack mobile architect, he can do anything in Flutter and he brought Buenro so much further, we want him full time working with us.
Great team member, incredibly pleasant to work with. Would and will hire ✌️✌️
Great team member, incredibly pleasant to work with. Would and will hire ✌️✌️
DR
Dennis R.
Jul 12, 2024
Senior Full-Stack Developer for Development Agency
Felix has great communication skills and is a pleasure to work with. Recommended!
RS
Rudy S.
Mar 21, 2024
ReactJs developer
When hiring Felix I had an emphasis on a high analytical capability and chose Felix because of that. This been a great choice: The quality of work has been great. Felix is not only analytical but also easy to work with (very flexible) and a super friendly and easy going guy.
About Felix
Production AI Architect & Fractional CTO | Full-Stack Platforms
100%
Job Success
Rottweil, Germany - 11:59 am local time
I architect and ship complete production platforms with AI built in: backend, APIs, web, mobile, cloud, data, and the agents on top. LITEBLOX, which I designed and built, runs across ~6,000 units in the field: a NestJS backend with 150+ APIs, two Next.js frontends (admin cockpit and customer portal), a Flutter app, PostgreSQL and EU cloud.
I am usually brought in for four types of work:
• Turning an AI demo into a production product.
• Building agentic, RAG, or on-device AI systems around real data.
• Architecting full-stack SaaS, mobile, backend, and cloud platforms.
• Acting as fractional CTO when the project needs technical ownership, delivery structure, and senior judgment.
I've built privacy-first on-device AI and production LLM features. My agentic-AI work includes controlled tool access, source-backed retrieval, verification, cost tracking, audit logs, and deterministic agent flow control. The model does not own the workflow. The system does.
Compliance is at the heart of my architecture, so the system can run AI on-premise or air-gapped on confidential in-house data, not just against a public API. GDPR, EU Data Act and ISO on live products. The same discipline carries to HIPAA.
One person owns the whole system: architecture, implementation, delivery, and technical decisions. As a fractional CTO I've led delivery and international teams, shipping production software inside enterprise processes. I can run the build or join yours.
For larger builds, I usually start with a paid architecture or diagnostic milestone. That gives you the technical plan, risks, milestones, and first build slice before either side commits to months of work.
Over a decade shipping software, 100% Job Success on Upwork, every contract closed at five stars.
If you've got a product or an AI system to take from idea to production, let's talk.
Core stack: TypeScript, React/Next.js, Node/NestJS, Go, Flutter/Dart, Swift, PostgreSQL/pgvector, Redis, Docker/Terraform, CI/CD. AI: LLMs (OpenAI/Anthropic/Google), RAG, multi-agent orchestration, on-device AI (Apple MLX, Gemma), vector databases.
—―――
AI Engineer · LLM · RAG · Multi-Agent Systems · AI Agents · Generative AI · On-Device AI · Apple MLX · OpenAI · Anthropic · Google Gemini · Vector Database · pgvector · Full-Stack · Software Architecture · TypeScript · Next.js · Node.js · Golang · Flutter · Swift · PostgreSQL · Redis · Docker · Stripe · OAuth · CI/CD · AWS · GCP
Steps for completing your project
After purchasing the project, send requirements so Felix can start the project.
Delivery time starts when Felix receives requirements from you.
Felix works on your project following the steps below.
Revisions may occur after the delivery date.
Share product context and requirements
Client sends the workflow goal, data sources, existing product details, technical constraints, and any available access or files.
Define the AI workflow and architecture
I map the agent or RAG flow, data/context handling, LLM provider options, integrations, and implementation approach.