AI implementation
AI implementation for real company workflows
We help choose practical AI use cases, prepare data, and embed a model into daily operations through a controlled pilot and clear quality checks.
Customer tasks
- review a manual workflow and identify where AI is actually useful;
- prepare a prototype for requests, documents, messages, or internal support flows;
- connect the AI scenario with CRM, knowledge bases, messengers, or internal APIs;
- check answer quality and define when complex cases move to a person.
Delivery scope
- workflow, data source, and access limitation review;
- target scenario, user roles, and processing logic description;
- architecture selection: language model, knowledge-base retrieval, classification, extraction, or a hybrid flow;
- service, integration, logging, and admin setting development;
- testing on customer examples and handover of the support model.
Typical stages
- Define the task and quality criteria using real examples.
- Check data, integration channels, and security requirements.
- Build a scenario prototype and expose limitations before production use.
- Finish integrations, access rules, error handling, and human control.
- Launch into the workflow and keep quality observation points visible.
What affects estimation
- quality and availability of source data;
- number of integrations and authorization requirements;
- need for a knowledge base, document search, or attachment parsing;
- control level: operator drafts or automated actions;
- logging, retention, and data deletion requirements.
Limits discussed upfront
- AI can be wrong, so critical actions need checks and clear responsibility;
- quality depends on source data, instructions, and regular feedback;
- some tasks are better solved with rules, forms, or standard integrations without a language model.
FAQ
Can we start with one workflow?
Yes. It is usually safer to validate one clear scenario, check data, and then expand to nearby workflows.
Do we need a custom model?
Not always. In many cases, a well-configured existing model, context, constraints, integrations, and quality control are enough.
Useful materials and examples
AI implementation
Discuss an AI scenario
Describe the workflow you want to speed up or offload. We will suggest a realistic implementation format and questions for estimation.