AI agent development for business
An AI agent performs a task by scenario: receives an enquiry, clarifies details, finds the answer in a knowledge base, fills in the CRM card and hands a complex case to an employee. We build AI agents and assistants for sales, support, marketing and internal operations. Each agent gets a limited set of actions, a log and rules for handing over to a person.
What an AI agent does
Qualification of incoming enquiries and correspondence: the agent identifies the topic, completeness of data and priority, asks clarifying questions and creates a deal in the CRM. Answers to customer and employee questions based on company documents. Draft preparation: commercial proposals, tender responses, product descriptions. Collecting data from several systems into one report. For each task we define what the agent does on its own and where review is needed.
How an agent is built
An agent consists of a model, instructions, tools and memory. Tools are calls to your systems: knowledge-base search, reading and writing in the CRM, sending messages, calculations. The model chooses a tool according to the scenario, while we restrict the list of actions and add checks so the agent stays within the task. Memory keeps the conversation context and the customer's history for an agreed period.
Knowledge base and answer accuracy
Document-based answers rely on retrieval over a knowledge base (RAG): documents, regulations, price lists and website pages are split into fragments, indexed and passed to the model together with the question. The agent answers from your materials and can show the source. Before launch we check answers on a test set of real questions and record which topics the agent hands to an employee.
What development includes
We take the project from scenario to launch in working systems and documentation hand-over.
- Process review, sample enquiries and data sources
- Scenarios, model instructions and the list of allowed actions
- Knowledge base with document retrieval and content updates
- Integrations: CRM, messengers, website, spreadsheets, telephony, 1C-Bitrix
- Staging environment, test request set and acceptance with your team
- Action log, metrics and team notifications, for example in Telegram
- Deployment, rollback and update documentation
How the work is organised
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Step 1
You describe the process and send sample enquiries, documents and access to the systems the agent should work with.
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Step 2
We design the scenario: which actions the agent performs itself, where it asks for confirmation, when it hands over to a person.
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Step 3
We build a prototype on real data and check it on a test request set together with your team.
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Step 4
We connect integrations, launch in production, hand over documentation and continue support where needed.
Cost of AI agent development
The cost depends on the number of scenarios and agent actions, integrations, the size of the knowledge base and testing requirements. Model usage for the expected load and infrastructure are estimated separately. To get an estimate, send a description of the process, sample enquiries and the list of systems the agent should work with.
Frequently asked questions
How does an AI agent differ from a chatbot?
A chatbot answers questions in a conversation. An agent also performs actions: creates deals, fills in fields, gathers data from systems and triggers the next steps of a process. An agent often works without a dialogue, on an event: a new enquiry, a new email, a new row in a spreadsheet.
Which models do you use?
OpenAI, YandexGPT, GigaChat, DeepSeek and open models on your own server. The choice depends on language, data requirements and request cost; for personal data we prefer models hosted in Russia.
How does the agent connect to our CRM?
Through the CRM API: amoCRM, Bitrix24 and other systems with an open interface. The agent gets limited rights, for example creating a deal and adding a comment without deleting data.
What happens when the agent is not sure?
The scenario sets a confidence threshold and a list of topics that always go to an employee. The agent tells the customer a specialist will join and passes the conversation context to the CRM or messenger.
How long does development take?
A prototype for one process is usually ready within a few weeks after we receive the data. The timeline depends on the number of integrations, the size of the knowledge base and testing requirements; we fix it after the review.
Can we start with a single scenario?
Yes, that is the usual approach: launch the agent on one task, measure the effect and extend scenarios based on results.