End-to-end development
AI integration for bots and web services
I integrate AI where it reduces repetitive work or accelerates access to information, with cost and failure controls.
Approach
Assistants, document search, classification and generation embedded into your workflows.
AI creates value when it is part of a concrete process rather than a chat added for novelty. An assistant can search documents, classify requests, prepare drafts or extract structured data. Important actions receive source references, guardrails, result checks and human escalation.
01 / scope
What can be built
The workflow is designed around your process instead of forcing your business into a template.
Request classification and routing
Structured extraction from text and files
Draft generation with manual approval
02 / result
What you receive
- Model and workflow selection with budget constraints
- Prompts, knowledge base and safety rules
- Token controls, logging and quality evaluation
- Integration into a bot, website or internal service
03 / connect
Integrations and technology
OpenAI, Claude, Gemini and compatible APIsDocuments, websites and company knowledge basesTelegram, VK and web interfacesCRM, helpdesk and internal systems
Delivery process
A clear path from idea to launch
Technical specification included when you order development
- 01
Clarify the task
Describe the outcome in plain language. I ask focused questions about users, data and constraints.
- 02
Define the scope
I document workflows, boundaries, stages, timing and acceptance criteria. The specification is included with development.
- 03
Build and test
You see interim progress while I test expected paths and failure cases.
- 04
Launch
I hand over the source and instructions or deploy the product and verify it in production.
FAQ
Frequently asked questions
Can the AI answer only from our data?
The assistant can be limited to retrieved knowledge-base context, cite sources and hand the question to a person when evidence is insufficient.
How do you control API cost?
I limit context size, select the right model for each step, cache repeated operations and add daily or per-user budgets.
Can AI errors be eliminated completely?
No. Risk is reduced through grounded sources, strict output formats, validation and human review for critical actions. I make the automation boundary explicit before development.