AI implementation and business process automation
We implement artificial intelligence where it removes routine work and speeds up customer handling: AI agents and assistants, chatbots for websites and messengers, LLM integration into websites and CRM, process automation and content production. We start with the process and the data rather than with a model: first we define the task AI solves, how the result is measured and which data and budget constraints apply.
Where AI implementation starts
We review processes where staff repeat the same actions: handling enquiries and correspondence, answering typical questions, preparing documents, reports and content. For each process we record the input and output, data sources, accuracy requirements and who checks the result. This produces a list of tasks with an estimate of impact and complexity, and implementation starts with the one where the benefit is visible soonest.
Models and data
We work with cloud and self-hosted models: OpenAI, YandexGPT, GigaChat, DeepSeek and open models on your own server when data cannot leave the company. The model is chosen for the task, language and data residency requirements rather than by name. For answers based on company documents we use retrieval over a knowledge base (RAG), so the assistant relies on your materials and can show the source.
Integration with existing systems
An AI solution is useful when it lives inside working tools: the website, CRM, messengers, spreadsheets, 1C-Bitrix or another CMS. We connect models through APIs, build scenarios in n8n or write our own services, add queues, retries and an action log. The output lands where it is expected: in a deal card, in a reply to the customer or in a report.
Quality control and support
Before launch we assemble a test set of real requests and review the answers together with your team. After launch we keep a log of conversations and track the share of answers handed to a person, request cost and response time. Scenarios and the knowledge base are updated as the product changes. Support is agreed separately and covers monitoring, prompt updates and improvements.
AI services
AI agents and assistants
Agents that follow a scenario: qualify enquiries, draft replies, gather data from several systems and hand complex cases to an employee.
ExploreAI chatbot for websites and messengers
A consultant on the website, in Telegram and WhatsApp that answers from your knowledge base, collects contacts and passes the conversation to a manager.
ExploreLLM integration
Connecting ChatGPT, YandexGPT, GigaChat and other models to the website, CRM and internal services, with search over company documents.
ExploreBusiness process automation
n8n scenarios and custom services: enquiry handling, marketing, sales, reporting and routine operations with AI in the loop.
ExploreContent factory
Producing articles, product cards and descriptions with AI: demand check, generation, editing, publishing and submission for indexing.
ExploreGEO: visibility in AI search
Your company in the answers of ChatGPT, Perplexity, Yandex Neuro and other assistants: measurement, content and technical preparation of the site.
ExploreWhat a project can include
- Process and data audit, selection of tasks for implementation with an impact estimate.
- Design of scenarios, prompts and the knowledge base; choice of model and hosting.
- Development of the agent, bot or integration; connection to the website, CRM, messengers and spreadsheets.
- Testing on real requests, launch, team training and documentation hand-over.
- Support: monitoring quality and cost, refining scenarios.
What the company gets
A working tool embedded in current processes, not a separate chat nobody opens.
Clear rules: what AI does on its own, what an employee confirms and where results go.
A log and metrics: enquiries handled, share handed to a person, cost and response time.
Deployment, rollback and update documentation so the solution does not depend on a single contractor.
What to keep in mind
Language models make mistakes. For tasks with legal or financial consequences we keep human review and limit the agent's actions.
Answer quality depends on your data: an outdated knowledge base gives outdated answers. Updating materials is planned together with the launch.
Sending personal data to external models requires checking contracts and legal requirements; where needed we use models hosted in Russia.
Model usage is a separate running cost, which we estimate in advance from the expected load.
How the work is organised
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Step 1
You send a description of the process or task, examples of enquiries and documents, links to the website and the systems in use.
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Step 2
We run a review: where AI pays off, which data is needed, which constraints apply. We propose a first step with a clear scope.
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Step 3
We build a prototype on real data and check it with your team on a test set of requests.
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Step 4
We refine scenarios, connect integrations, launch and hand over documentation; support continues where needed.
Cost of AI implementation
The cost depends on the number of processes and scenarios, data sources, required integrations, chosen models and hosting mode, and the amount of testing. Model usage and infrastructure are estimated separately. We give an estimate after reviewing the task and sample data; the first stage can be limited to a single process to prove the effect before scaling.
Frequently asked questions
Which task should AI implementation start with?
A process with many similar enquiries or documents and an easily checked result: customer questions, first-line enquiry handling, descriptions and reports. Such a project shows the effect faster and produces data for the next steps.
Can we avoid sending data to external services?
Yes. We use models hosted in Russia or open models on your infrastructure. This affects quality and cost, so the hosting mode is chosen during the review.
Do we need to train a model on our data?
For most tasks a knowledge base with document retrieval and well-designed scenarios is enough. Fine-tuning is rarely required and is discussed separately when standard approaches do not reach the needed accuracy.
How is the result measured?
Before the start we fix the metrics: handling time, share of enquiries resolved without an employee, answer quality on the test set, request cost. After launch we compare the figures with the baseline.
Do you work with sites on 1C-Bitrix, Tilda and WordPress?
Yes. We connect AI solutions to sites on these platforms through embeddable blocks, APIs and modules; for Tilda we use external services because the builder limits server-side logic.