Stuzhuk Lab — Chemistry of Code
Chemistry of Code
AI implementation
Ship assistants, RAG, OCR, and ERP-connected AI that survive real traffic—not demos. Vertical slice in weeks, then expand with evaluation and cost controls.
Tell me the goal, stack constraints, and timeline — I reply on Telegram.
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.
Representative work
Recent deliveries include 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.
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.
Tell me the goal, stack constraints, and timeline — I reply on Telegram.
