You will get a 30-45 minutes consultation on Local AI, RAG and MCP
Project details
Are you planning to integrate AI into your business but cannot risk exposing your sensitive data (legal, financial, proprietary) to public APIs like OpenAI or Anthropic?
In this focused 30-minute technical consultation, we will map out a secure, 100% private AI architecture tailored to your specific business needs. Drawing on over 10 years of experience in high-load FinTech engineering and custom AI deployments, I will help you cut through the noise and choose the right tools for your specific constraints.
In this focused 30-minute technical consultation, we will map out a secure, 100% private AI architecture tailored to your specific business needs. Drawing on over 10 years of experience in high-load FinTech engineering and custom AI deployments, I will help you cut through the noise and choose the right tools for your specific constraints.
AI Development Type
Knowledge Representation, Software MaintenanceWhat's included $60
These options are included with the project scope.
$60
- Delivery Time 2 days
- AI Model Integration
About Mikhail
Local AI & RAG Architect | Custom MCP Integrations | AI Consultant
Kyiv, Ukraine - 10:54 pm local time
I am a Software Engineer and former Tech Lead with a Master’s degree in Software Engineering and deep roots in the FinTech/Crypto space (including senior roles at industry leaders like Binance). Today, I specialize in designing 100% private AI systems (RAG) and custom Model Context Protocol (MCP) servers for businesses—such as legal and finance firms—that cannot compromise on data privacy.
My Integration Process (End-to-End Delivery):
I deliver complete Local RAG and MCP integrations as comprehensive Fixed-Price projects. To guarantee success, every project follows a strict three-step pipeline:
1. Infrastructure & Task Analysis: We deep-dive into your current architecture, business goals, and data structures.
2. Strategy & Selection: I select the most efficient combination of technologies—identifying the optimal local LLMs, embedding models, vector databases, and system configurations tailored specifically to your data and tasks.
3. Seamless Integration: I build and deliver a production-ready, backend-agnostic solution that your team can immediately plug into their workflow.
Where to Start: The Discovery Consultation
Because accurate time and cost estimations require a clear understanding of your architecture, I highly recommend starting with a 30-Minute Consultation Session.
During this call, we will discuss your specific task, define the optimal technical solutions, estimate project timelines, and align on expected deliverables.
Let's connect and map out how we can safely, privately, and effectively bring advanced AI optimization tools into your workflow.
What Others Say About My Work:
I pride myself on deep technical expertise combined with strong communication. Here is what engineering leaders have said about working with me:
"He is able to quickly understand new technologies and tools and apply them to practical development... He always ensures that his work is beneficial to the team and the project." — Frank Z., Direct Manager at Binance
"I was particularly impressed with how fast he picked up work... how he evaluates risks and clearly communicates his thoughts on mitigation, and how he tries to maintain the bigger picture." — Dimitrios V., Engineering Leadership
"An incredibly hard-working, diligent, and attentive team member. Undeniably one of the most talented developers I've ever had the chance to work with." — Adam B., VP of Product
Steps for completing your project
After purchasing the project, send requirements so Mikhail can start the project.
Delivery time starts when Mikhail receives requirements from you.
Mikhail works on your project following the steps below.
Revisions may occur after the delivery date.
Use-Case Analysis
Use-Case Analysis: We will discuss your business problem and determine if a Local RAG system or a custom Model Context Protocol (MCP) server is the right solution.
Tech Stack Selection
I will provide initial recommendations on the most efficient open-source LLMs, embedding models, and vector databases (e.g., ChromaDB, Llama.cpp) for your data.