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 recognition and model training, Python file contours, 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, CV and OCR, mainstream LLM APIs or on-prem where required, audit logs and role boundaries from day one.

Representative work

Recent deliveries include ai-vision (form recognition and model training), thinklens-bot, and cursor-telegram-integration—systems where ai implementation skills (LLM, RAG, chatbots, OCR, document AI, forecasting, TypeScript, Node.js, Python, ERP APIs, 1C, data security, process automation) were applied end to end.

Those projects combined product UI, APIs, and operational concerns: roles, exports, integrations, and observability. The goal is always software your team can extend—not a one-off demo that collapses under real users.

Typical collaboration starts with a written brief: constraints, integrations, compliance, and what “done” means for the first release. We slice work into vertical milestones so you see progress every one to two weeks and can reprioritize without losing the thread.

After go-live we offer a short hypercare window: fix edge cases, tune performance, and transfer knowledge. Long-term support can stay with your in-house team or continue as a retainer—your choice.

We document decisions in plain language, keep staging environments aligned with production, and leave runbooks so your team is not blocked after handoff. If you already have designers or backend engineers, we plug into your rituals instead of inventing a parallel process.