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Agentic AI workflow is a paradigm where artificial intelligence can autonomously execute multi-step tasks, much like a human. Unlike traditional systems where AI generates single-shot responses, the agentic approach enables AI to formulate plans to achieve a goal, independently define the necessary steps, and execute them sequentially.
This approach has gained significant traction in the corporate world in recent years. Key drivers include the more intelligent management of complex, multi stage tasks, a reduced need for human intervention, the ability to establish more natural interactions thanks to advanced language models, and enhanced flexibility and speed in business processes.
For organizations, agentic AI signifies not just automation but also a more efficient, agile, and user-friendly digital workforce. Skymod offers the architecture that enables this transformation to be implemented securely, scalably, and in a way that is tailored to specific organizational needs.
How is Agentic AI Workflow Realized with Skymod?
Skymod is an integrated platform empowering organizations to develop secure and flexible AI solutions leveraging their own data. Its architecture inherently supports the agentic AI workflow approach.
Skymod’s architecture is fundamentally built upon a hybrid structure, comprising three core layers: the local layer, the cloud (LLM) layer, and the user layer.
The first layer encompasses local components operating within an organization’s own data centers or in-country servers. Here, user authentication and access control are managed; documents such as PDFs and Word files are processed into text; this content is then sent to an embedding model to generate vector representations, which are subsequently stored in a vector database. Furthermore, the anonymization of sensitive data before it leaves the organization’s perimeter is performed at this layer. This ensures full compliance with data localization regulations and subjects organizational information to a security vetting process.
The second layer encompasses large language model (LLM) services accessed via the cloud. Skymod employs a security-focused API gateway to mediate communication with these models. This gateway ensures that only anonymized content is transmitted, generates tailored prompt templates for incoming queries, and inspects outgoing responses for unwanted expressions or sensitive content. This approach allows organizations to leverage the power of robust LLM models while safeguarding corporate data privacy.
The third layer comprises the interfaces through which users directly interact with AI assistants. Skymod users can retrieve information from corporate documents on SharePoint, extract content from specific websites to work with up-to-date data, or create custom AI assistants tailored to their specific needs. They can engage in direct conversations with these assistants via the SkyStudio interface and integrate the assistants they create into their own websites, mobile applications, or, optionally, WhatsApp. This entire experience is simplified to eliminate the need for technical expertise, enabling users to interact with AI effortlessly. Consequently, everyone within the organization can benefit from powerful AI solutions without dealing with technical intricacies.
So, how does this structure operate in a real-world scenario?
For instance, when a sales representative asks their AI-powered assistant, “Which was our top-selling product last quarter and why did it sell so well?”, a multi-layered workflow is triggered in the background. First, the user’s identity is authenticated, and their authorization is verified. Subsequently, the query’s content is analyzed; if it contains sensitive information, an anonymization process is applied. Once these initial security steps are complete, the system searches the embedding database for content relevant to the query. This content consists of previously vectorized documents, such as sales reports.
The most relevant content identified is then analyzed and ranked by a reranker model. After establishing the most meaningful context, the system transmits it to the large language model, which generates a natural, corporate-style response. For example: “The top-selling product last quarter was the A123 model. It achieved high sales due to campaign support and stock availability.” The model’s response undergoes a final security check before being presented to the user.
The entire process is completed within a few seconds. The user, having only asked a question, receives a simple and reliable answer without being aware of the complex, multi-step workflow operating behind the scenes.
Skymod’s hybrid architecture is not only technically robust but also designed to align with the nature and security requirements of corporate business processes. By harmonizing the multi-step decision-making and execution capabilities of agentic AI with internal data policies and regulations, Skymod enables artificial intelligence to become a genuine and effective aid in business operations.
Skymod's Basic System Components
Within Skymod’s infrastructure, numerous system components operate in concert to enable an AI agent to complete a task autonomously from start to finish. This architecture encompasses the entire process from the secure handling of a user’s query and the retrieval of accurate information to the generation of a meaningful response. Here are the fundamental elements that make this process possible:
Data Anonymization Service: This is one of Skymod’s most critical security layers. Personal information (such as names, ID numbers, customer details) present in user queries or documents to be analyzed is automatically detected and masked with temporary symbols by the system. This process is powered by a Turkish and industry-specific Named Entity Recognition (NER) model. For example, a name like “Ahmet Yılmaz” is replaced with symbols like “@@PERSON_1@@” before being sent to the LLM. This ensures that the content retains its meaning while guaranteeing privacy when transmitted to external models.
Embedding and Vector Database: These components provide Skymod with the ability to recall information and quickly access relevant content. The system breaks down uploaded documents into small, meaningful chunks and converts these chunks into mathematical vector representations. These vectors are stored on secure servers within Turkey. When a user asks a question, the system can retrieve the semantically closest content from this database within seconds. This ensures that the AI agent accesses truly relevant information before generating a response.
Reranker: This component comes into play at this stage. Among the document snippets retrieved via embedding, it’s crucial to select the most relevant ones. The Reranker re-evaluates these snippets with a deeper understanding of language and elevates those that most closely align with the user’s query. This ensures that only the most meaningful content is passed on to the Large Language Model (LLM), directly impacting the accuracy of the generated response.
LLM API Gateway: This manages Skymod’s secure connection with external LLM providers. Integrated to work with services like OpenAI, Anthropic, Google Gemini, and Mistral, this architecture allows organizations to choose between different language models based on their needs. Before each request is sent, the content is re-anonymized, and all communication is protected with TLS 1.3 encryption.
SkyLLM: This is Skymod’s native Large Language Model solution. SkyLLM is activated when companies prefer not to transfer their data externally or when operating in offline environments is necessary. This model is based on open-source LLMs and optimized according to the company’s specific requirements, hosted within the organization’s own infrastructure. This makes the agentic AI workflow sustainable entirely with local resources. Additionally, it can be further trained with company data to enhance its knowledge specific to the organization.
Response Control and Audit Module: This is the final stage for the response received from the LLM. This module checks whether the generated response complies with company policies, security criteria, and language usage guidelines. If the system detects instances such as the accidental re-disclosure of anonymized data or the use of an inappropriate tone, it identifies and corrects them. This ensures that every response is both secure and aligned with corporate standards.
When all these components work together, the AI agent doesn’t simply generate a direct answer to a user’s question. Instead, it first consults internal knowledge, retrieves the necessary information, selects the most relevant parts, and then uses this information to generate a natural and contextually appropriate response.
SkyStudio Platform: AI Assistants for Non-Technical Users
SkyStudio, Skymod’s intuitive interface that makes its powerful AI infrastructure accessible to everyone, enables you to develop AI assistants without requiring technical expertise. This platform allows users to either instantly start chatting with pre-built assistants or create their own organization-specific intelligent assistants powered by their documents from the ground up.
By selecting a ready-made assistant, users can immediately begin conversations with pre-configured scenarios. Users who wish to create a custom assistant can shape their own digital helper by completing a few fundamental steps.
In SkyStudio’s interface, the assistant’s name and logo are defined first. Subsequently, the sources from which the assistant will retrieve information are added: documents like PDFs and Word files, images, or specific web pages can be easily uploaded to the system.
The assistant’s behavior is also under the user’s control. The “Assistant Prompt” field defines how the AI should respond. For example, descriptions like “You are a formal customer representative, provide concise and clear answers” shape the assistant’s persona.
SkyStudio also offers users the flexibility to choose the language model they want to work with. Powerful models such as GPT-4, Gemini, Claude, and Mistral can be selected. Furthermore, parameters like whether the assistant should generate creative responses and the length of its answers can be easily adjusted via optional menus.
For advanced users, the platform also includes support for RAG (Retrieval-Augmented Generation) architecture and advanced configurations. Users can integrate external tools with their assistants when needed.
This entire process is presented through a simple and user-friendly interface, requiring no technical knowledge. Thanks to SkyStudio, AI development is no longer solely the domain of programmers but transforms into a process where everyone can contribute.
Agentic AI Workflow Scenarios for Different Departments
Let’s explore some example use cases of agentic AI assistants that can be created using SkyStudio across different departments. These examples demonstrate the platform’s flexibility and adaptability to various business processes:
Human Resources (HR): HR teams often spend time answering frequently asked questions. An “HR Assistant” created with SkyStudio can analyze company HR guidelines, leave policies, or payroll regulations and answer employee inquiries based on this information. For example, when an employee asks, “How many weeks’ notice do I need to give during the resignation process?”, the assistant can scan the relevant document, locate the specific clause, and provide a clear summary as a response. Similarly, it can answer questions like “What are the criteria for bonus payments?” with reliable information sourced from the HR documents uploaded by the company. This is a highly valuable use case for reducing the burden of repetitive questions and accelerating access to information.
Customer Service: A “Support Assistant” built with SkyStudio can answer customer questions based on the company’s return policy, warranty documents, or frequently asked questions. For instance, when a customer asks, “What is the return period?”, the assistant can find the relevant section in the previously uploaded return procedure document and clearly convey it to the user. If a product warranty document has been added to the system, it can easily answer questions like “How long is the warranty for this product?”. By leveraging tools integrated into SkyStudio, when a customer asks, “Where is my order?”, the assistant can interact with the backend service and share the current status.
Sales and Marketing: Sales and marketing teams make decisions based on various documents and reports. A “Sales Assistant” set up on SkyStudio can analyze content such as uploaded catalogs, product specifications, and sales strategy presentations. For example, when a sales representative asks, “What are the advantages of the Model X product?”, the assistant can scan the previously uploaded product document and list the relevant advantages point by point. Similarly, it can provide clear and concise answers to requests like “Summarize the campaign terms” from the available content. With tool support, it can enrich its responses by fetching information such as product stock levels or prices.
Legal Department: A “Legal Assistant” operates using company contracts, legislative texts, regulations, or internal guidelines. When a user asks, “Which clause in the supplier agreement outlines the penalty clauses?”, the assistant can scan the relevant contract, locate the correct clause, and provide a simplified summary if needed. Furthermore, when a manager asks, “What are the parts of the new GDPR communique that might affect our company?”, the assistant can analyze the relevant communique and list the potential impacts in bullet points. This use case allows lawyers to save time and accelerates routine document analysis.
Data Sovereignty and Performance Benefits of Using Local RAG
The local RAG (Retrieval-Augmented Generation) approach inherent in Skymod’s architecture offers organizations not only a fast and efficient AI infrastructure but also significant advantages in terms of security and control. This structure provides a much more reliable and flexible alternative compared to classic cloud solutions, especially for companies working with sensitive data.
Data Sovereignty: Local RAG signifies that control over company data remains entirely within the company. Since all documents, databases, and embeddings are maintained within the organization’s own infrastructure, dependence on external systems is minimized. This is critical, particularly for confidential or strategically important data. For example, a bank’s customer account data or an R&D firm’s patent documents do not leave the organization’s perimeter when processed with Skymod. As only anonymized and necessary portions are sent to external LLM services, there is no risk to the entirety of the company’s data. This situation goes beyond regulatory compliance requirements, allowing companies to use AI with complete peace of mind.
Low Latency and High Bandwidth: The use of a local vector database and a local LLM offers lower latency and faster response times, especially in scenarios involving frequent queries. External API calls can experience delays due to variables such as internet connection and service load. However, Skymod’s local infrastructure, operating with high bandwidth within the company network, ensures that search and retrieval operations are performed quickly.
Customizability and Fine-Tuning: Locally operating components can be more easily customized according to company needs. For example, it becomes possible to implement different priorities in ranking vector search results or to fine-tune the local LLM to align with company jargon. While external services offer predefined fixed structures and limited customization options, the local solution provides greater flexibility for improving performance or achieving the desired level of sensitivity through fine-tuning. This is also part of the performance advantage: with Skymod, each organization can optimize its RAG system to be as efficient as possible.
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