CAPABILITY
Custom AI development when off-the-shelf tools are not enough
I build custom AI systems in Python with LangChain, LangGraph, and the OpenAI Agents SDK when a platform cannot do the job cleanly.
An API request and retrieved documents feed a custom Python service built with LangGraph. Evaluation and logging run alongside, and the service returns grounded responses and calls custom tools.
OVERVIEW
A platform is the right answer most of the time. When the logic is unusual, the integration is messy, or the data cannot leave your infrastructure, a custom build is the answer instead.
I write production Python: agents, RAG pipelines, APIs, and the glue that connects systems that were never meant to talk to each other.
WHEN THIS HELPS
Signals this is worth automating
- A workflow outgrew what a no-code platform can do cleanly.
- You need an agent with custom logic, memory, or evaluation.
- A tool has no API, so something has to bridge it.
- Data has to stay on your own infrastructure.
- You need an API or service your product can call.
WHAT I BUILD
What the system includes
Custom agents with LangChain, LangGraph, and the OpenAI Agents SDK.
Multi-step and multi-agent workflows with human-in-the-loop checkpoints.
RAG pipelines over your documents and databases.
FastAPI services and internal tools your team can call.
Integrations for systems with no clean API.
Evaluation and observability so quality is measured, not guessed.
HOW IT WORKS
From process to system
We agree on the outcome, the constraints, and where the code has to live.
Frameworks and models follow the problem, not the other way around.
The system is built in steps and tested against real cases before it goes live.
You get the code, the documentation, and a walkthrough so your team can run it.
COMMON QUESTIONS
Common questions
When should I choose custom over a platform?
When the logic is unusual, the integration is messy, or a platform cannot meet a technical or data constraint. Otherwise a platform is faster and cheaper.
Which frameworks do you use?
LangChain, LangGraph, and the OpenAI Agents SDK most often, and CrewAI or others when they fit. The choice follows the task.
Do I own the code?
Yes. It runs in your repository or your infrastructure, and you get documentation and a walkthrough.
How much does custom AI development cost?
It depends on the scope, the integrations, and the data work. You get a fixed quote after scoping.
COMPARE
Compare your options
WHERE IT FITS
Where this fits
Best for teams that have outgrown a platform, need custom logic, or have a technical constraint a hosted tool cannot meet.
Multi-agent research system
Research that runs every day without tying up the team
Ready to automate this workflow?
Show me your current process and tools, and we can map what the automated system should look like.
Book a discovery call→