Yevhen Stuzhuk - Personal Portfolio
I am a Senior Software Engineer
AI implementation
Intelligent solutions that strengthen your systems and processes: assistants, document recognition, analytics, forecasts, and ERP integration.
Technologies. Data. Results. I implement AI that connects to your real systems—not a demo chat in a vacuum. Assistants for staff and customers, document pipelines, forecasts, and hooks into ERP (CRM, warehouse, finance, manufacturing)—including 1C integration when that is your core.
Typical delivery: define the business outcome, pick a narrow first scenario (one document type, one FAQ domain, one forecast), ground the model on your APIs and metadata, then expand. Stack: TypeScript/Node.js or Python for orchestration, mainstream LLM APIs or on-prem where required, audit logs and role boundaries from day one.
FAQ
Tap a question to expand the answer.
Do you build generic chatbots or tied to our data?
Grounded assistants on your APIs, docs, and ERP metadata—RAG or tool-calling—not a public ChatGPT wrapper. Scope and data boundaries are defined before the first prompt ships.
Which ERP systems can AI connect to?
1C, custom ERP, and module-level links to CRM, warehouse, finance, manufacturing via HTTP APIs and queues. See also 1C integration for the accounting core.
How do you handle document recognition errors?
Confidence thresholds, validation rules against ERP, and a human review queue for low scores—no silent posting to the ledger.
Can models run on-prem or in our VPC?
Yes when policy requires it: on-prem or private cloud LLM/OCR, no training on your data without contract, encryption and access logs by default.
What is a realistic first phase?
One vertical slice in 3–8 weeks: e.g. internal FAQ bot, one document type, or one forecast metric—then expand modules and channels.
