You will get a production-ready RAG system connected to your business data
Project details
Most AI integrations fail because they're built as demos, not systems.
I build RAG pipelines and autonomous agent workflows that connect directly to your business data documents, databases, internal tools and return accurate, cited answers your team can actually act on.
What you get:
• Retrieval pipeline tuned to your specific data (not a generic ChatGPT wrapper)
• LLM integration with the provider of your choice (OpenAI, Anthropic, Gemini)
• Clean API endpoint your frontend or app can plug into immediately
• Deployed, documented, and handed over with a walkthrough not just raw code
Recent results from my own production systems:
• RAG pipeline that cut support resolution time by 41%
• Document assistant processing 3,400+ queries in 3 weeks
• 91% retrieval accuracy across 50+ contract types using hybrid BM25 + vector search
• Fine-tuned model hitting 93% task accuracy at 6x lower cost than GPT-4
If you have data and need an AI layer on top of it that's exactly what this project delivers.
I build RAG pipelines and autonomous agent workflows that connect directly to your business data documents, databases, internal tools and return accurate, cited answers your team can actually act on.
What you get:
• Retrieval pipeline tuned to your specific data (not a generic ChatGPT wrapper)
• LLM integration with the provider of your choice (OpenAI, Anthropic, Gemini)
• Clean API endpoint your frontend or app can plug into immediately
• Deployed, documented, and handed over with a walkthrough not just raw code
Recent results from my own production systems:
• RAG pipeline that cut support resolution time by 41%
• Document assistant processing 3,400+ queries in 3 weeks
• 91% retrieval accuracy across 50+ contract types using hybrid BM25 + vector search
• Fine-tuned model hitting 93% task accuracy at 6x lower cost than GPT-4
If you have data and need an AI layer on top of it that's exactly what this project delivers.
AI Algorithms
Convolutional Neural Network, Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Radial Basis Function Network, Recurrent Neural Network, Regression Analysis, Transformer Model, YOLOAI Applications
AI Chatbot, AI Mobile App Development, Anomaly Detection, Conversational AI, Image Analysis, Image Processing, Image Recognition, Natural Language Generation, Natural Language Understanding, Object Detection, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$50
|
Standard
$110
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 1 |
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 |
Optional add-ons
You can add these on the next page.
Additional Revision
+$30Frequently asked questions
About Jeremiah
AI Engineer | LangGraph Agents, RAG Systems & Full-Stack Applications
Lagos Island, Nigeria - 12:24 pm local time
In the past year alone, I've architected a RAG pipeline that cut customer support resolution time by 41%, fine-tuned a Mistral 7B model to 93% task accuracy at 6× lower cost than GPT-4, and shipped computer vision tools now used by thousands of active users monthly.
What I actually build for clients:
▸ Autonomous agent systems and LLM workflows using LangGraph and LangChain not demos, production deployments
▸ RAG pipelines that connect your proprietary knowledge to LLMs with accuracy you can measure
▸ End-to-end full-stack platforms, geospatial marketplaces, AI-powered document tools, logistics MVPs
▸ Computer vision applications for shelf-life estimation, inventory counting, and asset tracking
I work best with startups and SMBs who know what they want to build but need an engineer who can take full ownership from architecture decisions to deployment without hand-holding.
I'm based in Lagos, work async-first, and communicate like a professional, not a vendor.
If your project involves LLMs, agents, or full-stack AI systems, I'd rather show you relevant work than pitch you. Send me a message and let's see if we're a fit.
I work across the following technologies and use cases: RAG pipeline development, LangGraph agents, LangChain, LLM integration, OpenAI · Anthropic · Gemini APIs, vector databases (Pinecone, pgvector), FastAPI, Next.js, Flutter, computer vision (YOLOv8), geospatial APIs (PostGIS), AI automation, document intelligence, and production MVP builds.
Steps for completing your project
After purchasing the project, send requirements so Jeremiah can start the project.
Delivery time starts when Jeremiah receives requirements from you.
Jeremiah works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Scoping
You share your data sources, use case, and goals. I confirm the technical approach, architecture, and delivery timeline. You get a clear scope document before any code is written.
Build & Progress Update
I architect and build the system RAG pipeline, agent logic, API, and frontend. You get a progress update at the midpoint with a working demo so we can course-correct early if needed.