LLM integration into websites and CRM
We connect language models to your systems: the website, CRM, help desk, internal services and spreadsheets. Integration of ChatGPT, YandexGPT, GigaChat, DeepSeek or an open model on your own server is built through APIs with queues, limits and logging. We also design retrieval over company documents so the model answers from your data.
What can be integrated
Text generation and review in the website admin: product descriptions, metadata, replies to reviews. Classification and enrichment of enquiries in the CRM: topic, urgency, extracted data. Summaries of correspondence and calls in the customer card. Document search and answers for employees. Translation and adaptation of content for language versions. Each function is embedded into the existing interface so staff do not switch between windows.
Model choice and hosting
We compare models on your examples by quality, speed and request cost. Cloud models suit tasks without personal data and with high quality requirements. For data that cannot leave the company we use YandexGPT and GigaChat in Russian clouds or open models on your server. The architecture allows replacing the model without rewriting the integration.
Retrieval over company documents (RAG)
Documents, website pages, regulations and knowledge bases are split into fragments, indexed as vectors and passed to the model together with the question. The model answers from current materials and can cite the source. We set up index updates when documents change and access rights so that an employee sees only permitted materials.
Reliability, limits and security
The integration runs in production mode, not as a script on a laptop.
- Queues, retries and request rate limits
- Request and response log, cost control by department
- Masking of personal data before sending to external models
- Staging environment and checks on desktop, tablet and phone for user-facing scenarios
- Error monitoring and team notifications
- Deployment, rollback and model replacement documentation
How the work is organised
-
Step 1
You describe the task and the systems the model should connect to; you send sample data and storage requirements.
-
Step 2
We compare models on your examples, choose the architecture and hosting mode and fix the scope.
-
Step 3
We develop the integration and, if needed, document retrieval; we test on staging together with your team.
-
Step 4
We launch in production, set up monitoring and hand over documentation.
Cost of LLM integration
The cost depends on the number of systems and functions, the volume of documents for retrieval, data security requirements and the model hosting mode. Model usage and infrastructure are estimated separately for the expected load. For an estimate, send a description of the task, the list of systems and sample data.
Frequently asked questions
Which models do you connect?
OpenAI, YandexGPT, GigaChat, DeepSeek, Claude and open models such as Qwen or Llama on your server. We choose based on a comparison on your data.
Can a model be embedded into a 1C-Bitrix site?
Yes. We develop a module or service that connects to information blocks and the admin panel: description and metadata generation, answers to visitor questions. For WordPress and other CMS we use plugins and APIs.
How is personal data protected?
Data is masked or anonymised before being sent to an external model; for sensitive tasks we use models in Russian clouds or on your server. The processing procedure is agreed with your requirements and contracts.
What is RAG and when is it needed?
It is retrieval over your documents with the found fragments inserted into the model request. It is needed when answers must rely on internal materials: regulations, price lists, instructions rather than the model's general knowledge.
What if the model provider changes its terms?
The integration is designed with an abstraction over the model: switching providers is a configuration change, not a rewrite. The request log and the test set help verify quality after the switch.