OpenAI Assistants API: how to avoid rework when developing an AI assistant in Russia

The OpenAI Assistants API is becoming obsolete, so a new AI assistant cannot be designed without checking the current OpenAI stack. SEO Mind42 helps audit projects, plan migration from the Assistants API, develop integrations with business systems, and prepare an architecture that can evolve without urgent rework.

OpenAI is directing developers toward the Responses API and Agents SDK. For businesses, this means that before launching a chatbot, it is necessary to evaluate not only the quality of text generation but also context storage, tool usage, file processing, data access, and support for future API changes.

SEO Mind42 designs and implements AI assistants for websites, CRMs, personal accounts, knowledge bases, and internal services. We do not limit ourselves to connecting a model: we analyze the business scenario, define the boundaries of automation, configure the backend, function calling, RAG search, and response validation.

• Audit of an existing solution based on the OpenAI Assistants API and a plan for further action.
• Development of an AI assistant for a website, CRM, knowledge base, or internal processes.
• Connecting tools, custom functions, document search, and corporate data sources.
• Configuring roles, access restrictions, request routing, and quality validation.
• Delivery of the source code, technical documentation, and recommendations for maintaining the AI solution.

If a paid model is required for the task—for example, GPT-5.6 Terra—it is cheaper to obtain access through the Clodex partner service rather than directly from the vendor. The price difference is shown below.

What is the OpenAI Assistants API, and why does a project need its architecture checked?

The OpenAI Assistants API is an OpenAI API interface for creating assistants with system instructions, conversations, files, and tools. In the classic model, the developer creates an assistant, maintains the history in a thread, adds a message, and starts processing through a run. This approach made it possible to quickly assemble a prototype; however, for a new product, its current status and compatibility with the required OpenAI models should be checked in advance.

Businesses care not about the names of entities but about controlled actions: what information the AI assistant can see, which source it uses to obtain an answer, when it calls the CRM API, and when it passes a request to an employee. Without this logic, a chatbot remains a demonstration of text generation rather than becoming part of the sales, support, or document workflow.

The Responses API and Agents SDK provide a more current foundation for building AI agents, orchestration, and tool calling. The choice depends on the task: sometimes a single agent with several tools is needed, while in other cases a separate response-generation service with a strictly limited set of functions is sufficient. We evaluate the architecture before development begins so that the team does not have to move critical logic after the product is already being used by customers.

Technical specialists may also find our overview of API services and access to language models for SEO tasksuseful. It helps explain why the choice of provider and integration method affects availability, budget, and project support in Russia.

When is migration from the OpenAI Assistants API needed?

Migration from the Assistants API is needed not only after a notice that the interface is becoming obsolete. It should be planned when the current bot is difficult to expand, works unstably with files or tools, stores conversation context opaquely, or requires separate logic for new channels. Special attention should be paid to projects in which the assistant works simultaneously on a website, in a personal account, CRM, and support service.

A mechanical replacement of API calls rarely solves the problem. Migration affects history storage rules, message processing, custom function invocation logic, vector store operation, file uploading and indexing, request routing, test scenarios, and error monitoring. First, we study the code, user journeys, and data sources; then we propose a sequence of technical changes.

A new product also requires evaluation. If the team is only considering the OpenAI Assistants API as the foundation for an MVP, it is wiser to compare it with the Responses API, Agents SDK, and other options before the backend, interface, and support processes become tied to legacy entities.

What business tasks can an AI assistant solve?

An AI assistant handles repetitive tasks in which an employee has to search for information, clarify parameters, classify requests, or transfer data between systems. The result depends on the quality of the knowledge base, the limits of the scenario, and which actions the model is allowed to perform through the API integration.

AI assistant for the sales department

The assistant handles the initial inquiry, answers product questions, clarifies needs, and creates a structured request for the manager in the CRM. It can identify the topic of the inquiry, collect contact details with the user's consent, and transfer the deal record via webhook or API. Prices, delivery times, contractual terms, and special offers should come from approved sources or be verified by an employee.

AI assistant for customer support

The first support level searches the knowledge base for an answer, identifies the type of problem, suggests instructions, and transfers complex cases to an operator. Along with the request, the employee receives a brief conversation summary, the materials found, and the questions already asked to the user. This scenario does not replace support entirely: it reduces manual searching and helps the operator understand the context more quickly.

AI assistant for an internal knowledge base

The RAG approach allows employees to ask questions about regulations, instructions, technical documentation, and internal policies. The backend retrieves relevant fragments from the vector store, passes them to the model, and the assistant then generates an answer with a reference to the original document in the company's interface. Access rights must be checked before the search; otherwise, a user may obtain information from a restricted section of the knowledge base.

AI assistant for e-commerce and catalogs

In an online store, a bot helps customers choose products, explains their features and compatibility, and answers questions about delivery and returns. Current inventory levels, order statuses, prices, and promotion terms cannot be stored only in the prompt. The assistant should request this data from the product system through function calling, while the backend must verify permissions, request parameters, and external API errors.

AI assistant for SaaS and digital services

For a SaaS product, an AI assistant explains the interface, helps users complete onboarding, creates task drafts, summarizes inquiries, and suggests the next step in the user journey. Routing is useful within the product: questions about billing, technical errors, service features, and access require different data sources and different response rules.

AI assistant for documents and content

A model can summarize files, extract facts, classify documents, and prepare drafts of emails, product cards, and customer responses. Text generation requires editorial rules and human review when the material contains legally, financially, medically, or technically critical wording. For SEO teams, we also analyze artificial intelligence tools for promotion and scenarios in which automation does not reduce content quality.

What risks should be considered before launching an AI assistant?

The risk arises not from the chatbot itself but from the composition of the data, access rights, and actions performed by the integration. If conversations, CRM records, files, or requests contain personal data, the architecture should be agreed with the customer's responsible personnel before information is transferred to external AI services.

Personal data and cross-border transfers

Federal Law No. 152-FZ, “On Personal Data,” regulates the processing of personal data that may appear in conversations, documents, and CRM records. Organizations working with data belonging to Russian citizens must take into account the legal requirements for storing and processing such data and separately analyze cross-border transfers.

Information security and liability

Federal Law No. 149-FZ, “On Information, Information Technologies, and the Protection of Information,” establishes the general context for organizational and technical information security measures. Violations in the area of personal data are subject to Article 13.11 of the Code of Administrative Offenses of the Russian Federation, while sector-specific supervision is carried out by Roskomnadzor. A standard AI assistant integration generally does not require separate approval from Roskomnadzor, but processing personal data may give rise to other obligations, including notifications and requirements concerning cross-border transfers.

Access to the knowledge base and business systems

An assistant should not receive full access to the CRM, file storage, or catalog simply because this makes the prototype easier to implement. The backend limits the list of available methods, verifies the user's role, and passes only the minimum necessary context to the model. For regulated information systems, the customer may need a separate assessment of information security requirements, taking into account the relevant specialists and the requirements of the Federal Service for Technical and Export Control.

Critical responses and actions

An AI agent must not be allowed to independently approve returns, change payment details, publish legally significant responses, or perform financial transactions without a separate controlled process. In such scenarios, the system prepares a draft, requests employee confirmation, or transfers the inquiry to the appropriate queue.

• Pass only the data needed for the specific response to the model.
• Mask identifiers and sensitive fields when the scenario allows it.
• Separate access to documents, the CRM, and tools by user role.
• Record data sources, tool calls, errors, and critical actions performed by the AI assistant.

Read more about legally compliant use of neural networks in Russia in the article on the legal aspects of using AI. For a specific project, the legal model and protection measures should be determined jointly with the customer's personnel responsible for personal data and information security.

If you decide to choose a paid plan while reading, compare the official price with the price through a partner before subscribing directly: the difference is usually several times, and the calculation is provided at the beginning and end of the article.

How do we work with a project based on the OpenAI Assistants API?

Developing an AI assistant starts with validating the task and constraints, not with choosing a single model or copying a Python example from the OpenAI documentation. We build the solution step by step so that, before scaling, we can verify the operation of the knowledge base, tools, API integrations, and scenarios for handing an inquiry over to a human.

1. Initial consultation and context gathering. We clarify the AI assistant’s goal, users, communication channels, data sources, current infrastructure, constraints, and expected outcome.
2. Architecture audit or design. We review existing assistants, threads, messages, runs, history storage, tools, function calling, error handling, and access permissions. For a new project, we choose the Responses API, Agents SDK, or another approach that matches the task.
3. Scenario prototype. We build an MVP with a limited set of functions: for example, searching the knowledge base, creating a request, or retrieving permitted data from the CRM.
4. Integration with business systems. We connect the website, customer portal, CRM, catalog, file storage, external APIs, and internal services through the backend and controlled function calls.
5. Testing and quality tuning. We test typical and nonstandard requests, restrictions, empty data, tool errors, answer accuracy, correct escalation, and transferring the dialogue to an employee.
6. Launch and ongoing support. We deliver the code and documentation in the agreed format, describe monitoring, token cost control, the process for updating models, and the development of the AI solution.

What determines the cost of developing an AI assistant?

The price depends on the project’s starting point. Auditing a working bot and preparing a migration plan require one set of tasks, while developing a solution from scratch with CRM, customer portal, multiple data sources, document search, and external tools requires another. The estimate is influenced by the number of user scenarios, the complexity of the backend logic, whether the client’s systems have APIs, and the rules for access control.

Knowledge base preparation, RAG configuration, file processing, answer testing, logging, analytics, quality monitoring, AI solution support, and OpenAI model costs are assessed separately. The query “OpenAI Assistants API price” cannot be reduced to the cost of a single API call: the budget depends on the amount of context, inquiry frequency, dialogue length, selected models, and infrastructure.

• Technical audit of a solution using the OpenAI Assistants API.
• AI assistant architecture design.
• AI assistant MVP development.
• Migration to the current OpenAI API stack.
• Integration with the CRM, website, knowledge base, and internal services.
• Support and development of the AI product after launch.

Why can’t you simply take code from the documentation and launch the bot?

The OpenAI documentation and Python examples help make a basic API request, but they do not design the product for the team. Ready-made code does not define which data may be sent to the model, who gets access to the results, what to do when an external service fails, or how to respond when a user asks a question outside the permitted scenario.

The knowledge base also requires preparation. Duplicate, outdated, and contradictory documents impair document search and force the model to build its answer on weak context. Before implementation, we review the structure of the materials, sources of current information, update rules, and the mechanism by which the assistant should acknowledge that the knowledge base contains no reliable answer.

Function calling requires separate oversight. The model proposes call parameters, but the backend must validate them, check the user’s permissions, restrict available actions, and correctly handle an unsuccessful response from the CRM, catalog, or another service. Without this, an AI assistant may formulate a confident answer based on incomplete or inaccessible data.

Costs also cannot be calculated solely by the number of messages. Token costs depend on the size of the system instructions, the context being transmitted, the depth of the dialogue history, the volume of files, request frequency, and the selected model. Support is also necessary because APIs, OpenAI models, business processes, and knowledge base content change over time.

If you already have a prototype or technical specification, SEO Mind42 can help check whether it contains risks that could lead to future rework.

FAQ

Launch an AI assistant without tying it to outdated architecture

If you already have a project using the OpenAI Assistants API, an AI bot idea, or a task involving the automation of support, sales, or document workflows, start with a technical assessment. We will determine the required integrations, check the risks, propose a current approach, and prepare a clear development plan.

• We will check whether the current architecture is suitable for product development.
• We will determine where RAG, API integration, tools, and human oversight are needed.
• We will assess the risks of handling personal data and access to systems.
• We will prepare a roadmap from the audit or MVP through AI assistant support.

Paid access to OpenAI models

Official OpenAI prices and partner prices through Clodex are shown in the table below. For example, GPT-5.6 Terra through a partner is 28,6 times cheaper than the official price.