I write the code myself. This is a hands-on engineering engagement, not advisory-only - you get a working, deployed system.
I design and build production-ready AI features, agents and cloud-native applications integrated with existing systems.
(Audience)
You have a validated AI use case and a deadline. You need an engineer who can build it to production standard, not another proof of concept.
Your team understands the basics but hasn't shipped a production agent or RAG pipeline before. You want an experienced partner to avoid the common pitfalls.
Discovery and feasibility are done - now you need someone to actually build, test, and ship the system reliably.
You're adding AI capabilities to a Spring Boot or FastAPI service already in production, and need it done without destabilizing what's already running.
I review your requirements, data, and existing stack, then design a target architecture for the AI system before any code is written.
I build the system in short, iterative sprints with regular demos, so you see real progress and can redirect early if needed.
I integrate the system with your existing services and write tests validated against a real evaluation set, not just manual spot-checks.
I ship to production with proper observability, security review, and a rollback plan - so launch day isn't a leap of faith.
I stay available to fix issues and tune the system based on real usage, then hand it over cleanly to your internal team.
Systems are built with error handling, retries, and fallback logic from day one - not bolted on after something breaks.
The target architecture is designed to handle growing usage and data volume without a rebuild.
Documentation and a walkthrough mean your team can own and extend the system after I'm gone.
Logging, tracing, and monitoring are part of the build, not an afterthought - so issues get caught before users notice.
Technology decisions are based on what your team can maintain, not what's trendiest.
Design a target architecture diagram for the AI system
Build a production RAG pipeline with hybrid search and reranking
Implement a multi-agent system with tool-calling and memory
Integrate a vector database such as Pinecone or pgvector
Build API endpoints in Spring Boot or FastAPI exposing the AI system
Set up evaluation, logging, and monitoring for the pipeline
Deploy to production with a rollback and incident response plan
Testimonials
Bartosz shipped our RAG pipeline in six weeks - something our internal team had been stuck on for months. Clean code, proper tests, real documentation.
He inherited a half-built agent system and turned it into something we actually trust in production.
FAQ
I write the code myself. This is a hands-on engineering engagement, not advisory-only - you get a working, deployed system.
(Contact)
Schedule a call to explore what AI can do for your business. No sales pitch - just a direct conversation about your challenges and opportunities.