ARTIFICIAL INTELLIGENCE EMERGES: A REVIEWED CORPORATE RISK ANALYSIS SYSTEM.

Abstract

This invention relates to a closed-loop verification and data processing gateway system (1) that enables corporate structures to work with a "zero error" target in risk management, transmitting tax identification number (TIN) and commercial identification information, and combining the semantic analysis capability of artificial intelligence (AI) with the deterministic accuracy of official sources on an operational pipeline.

Description

1 TARIFF ARTIFICIAL INTELLIGENCE RESULTS IN A REVIEWED CORPORATE RISK ANALYSIS. SYSTEM Technical Area This invention transmits tax identification number (TIN) and business identification information to corporate entities. working with the goal of "zero errors" in risk management of structures, artificial intelligence (AI) semantic analysis capability with the deterministic precision of formal sources a closed-loop verification that enables its integration on an operational line and is related to the data processing gateway system. 10 Previous Technique The current technology has the following technical shortcomings: 15 • 1. Processing Distributed and Asynchronous Data from Heterogeneous Sources (Manual / Semi-Automated Systems: Official databases containing corporate data. (MERSIS, Trade Registry Gazette, GIB, KAP, etc.) are not synchronized with each other. They are asymmetric structures with different schema structures (XML, JSON, plain text). 20 Technical Issue: This data can be obtained manually or via a classic API using external software. Pulling and combining data from these pools results in excessive CPU cycle time and This leads to high latency. Also, the moment data is loaded into memory... It becomes outdated by becoming static. The most critical technical barrier is the complex holding system. Circular / cross-affiliation relationships in their structures in classical relational databases 25 (RDBMS) inability to convert to directional graph structures, resulting in hidden The problem is that partnerships cannot be discovered algorithmically. 2 • 2. Traditional Generative Artificial Intelligence (LLM) Pipelines: In natural language Standards for generating strategic text analysis, corporate briefs, and risk inferences. Large language models are utilized. Technical Problem (Nonlinear Probabilistic Deviation / Hallucination): Current AI architectures are by nature probabilistic (probabilistic) in weighting. It works with matrices. When generating free text, the systems fill in the gaps in the training data. A unique corporate identity that needs to be deterministic in order to close the deal. fictitious tax identification numbers (VKN), registered trade names, or mathematical ratios It tends to (hallucinate). On the output of a probabilistic model. A dynamic circuit breaker / regulator that operates during the runtime pipeline. 10 Because there is no mechanism in place, the erroneous semantic data generated goes directly to the end user. It infiltrates the layer. • 3. Static Corporate Data Services and Data Warehouses: Companies' past presenting financial performance data and record information in relational tables They are traditional data services. 15 Technical Issue: These platforms are structurally "isolated nodes" It is focused on the study of all subsidiary components of a multi-layered holding structure. a scheme that can consolidate into a single in-memory matrix at runtime They do not have a normalization layer. Also, in real-time data streams (e.g.: (KAP xBRL notifications) dynamically reflect changes in semantic sensitivity to the current 20 They cannot reflect the generated analytical inferences in risk matrices and official meta-sources. a source sealing that cryptographically stamps its data (source They do not have anchoring infrastructure. In the current state of the technology, machine learning of large datasets is 25 by processing the financial bankruptcy risks and probability scores of companies with algorithms predicting the use of internal banking system data (KYC and account transactions) by processing and automating AI-based credit limit determination processes, ownership structures and corporate family trees of companies on a global scale (Family Solutions have been found that enable listing in relational databases (Tree). 30 3 However, in the current state of the technology, all of the quarantined draft text nonlinear claims, financial ratios, VKNs, and status through semantic and regular expression (regex) parsers of definitions AI 5 that enables the extraction and conversion into a temporary key-value matrix. The use of the Claim Matrix, the Golden Record Matrix which is locked in memory. ($M_{GT}$) and the $M_{AI}$ matrix containing the claims of generative artificial intelligence Using a Cell-Based Rule Engine that compares at the cell level, error isolating the producing cell, the absolutely accurate data in the Golden Record Matrix is ​​strictly Prompt Constraint Injection as a mathematical constraint parameter in AI Analysis 10 It is designed to be injected into the nucleus during runtime, and the system targets only the relevant cell. A Deterministic Feedback that forces reproduction in a way that will correct it. No solution has been found that enables the use of the loop. International patent number WO2026150383, which is included in the prior art, 15 in the document Autonomous Artificial Intelligence Agents, Agent-Based Workflows and Systems for Agent-Based AI Safety in LLM Co-Pilots and The methods are explained. In this invention, a tool created by artificial intelligence is used. call, payment instruction, database update, data export, network command, Customer communication, software distribution, or actuator command, not action authorization, 20 It is considered an ineffective candidate action. A protected area of ​​application, confirmed model state, runtime behavior, execution source, factual support, purpose-driven accessibility, policy timeliness, cancellation status, outcome zero confidence by verifying the simulation and termination pool compatibility. It enforces the limits of execution. When satisfied, the non-carrier 25 Termination capability is only available for verified actions, scopes, and termination pools. It is released for this purpose; this prevents vehicle misuse and agent restriction. Supports high-risk transactions, reduced scope, protected approval, tiered pricing. execution, reversible rollback capabilities, residual risk receipts or monitoring It proceeds through enhanced conditional certainty using advanced applications. 30 4 Beyond standard alignment guards and access control checks, external Before results emerge, the dependency of cryptographic execution is structural. It utilizes incompleteness, locked-in execution principles, and executive branches. This In the invention, the protected area is for implementing a specific output-result precision sequence. It is used. The Protected Area of ​​Application verifies the candidate's action, links the evidence, and determines the outcome. 5 evaluates, records verification evidence, and assesses a comprehensive ability or execution. It releases its handler and the certainty pool does not verify the necessary authorization unless... It prevents activation. However, in the document numbered WO2026150383, 10 in the official registry records modeling companies in a directional graph topology ($G=(V,E)$), holding companies and The breadth of cross-partnership spirals and indirect stakes in affiliate networks With Priority Search (BFS) and Cycle Detection algorithms By scanning, circular deadlocks and memory overflows (stack overflows) are detected. its resolution within, 15 from the Public Disclosure Platform (KAP) and stock exchange bulletins. incoming real-time xBRL financial statement streams via asynchronous pipelines capture, homogenize in the Unified Corporate Schema layer, and Enabling the dynamic feeding of the in-memory $M_{GT}$ matrix, approved. Each line of the analysis text directly contains the official API_Log_ID, official Document_No, and Combined with microsecond timestamp data, 20 SHA-256 algorithm results are obtained. It must be cryptographically signed and the report data payload cannot be tampered with directly. burial as a forensic seal, numerical tolerance of free text claims a piecewise one that satisfies the criteria of ($\epsilon$) and semantic exact equality ($\equiv$) Cell-based analysis via the mathematical function $f_v(M_{AI}, M_{GT})$ There is no mention of a system that ensures its monitoring. 25 Brief Description of the Invention The aim of this invention is to arrange companies in official registry records in a directed graph topology. ($G=(V,E)$) modeling, cross-partnership in holding and subsidiary networks Breadth-First Search (BFS) and Loop Detection of spirals and indirect shares By scanning with (Cycle Detection) algorithms, circular deadlocks and memory issues are detected. Resolving stack overflows in memory, Public Disclosure 5 Instant xBRL financial notifications from the platform (KAP) and stock exchange bulletins. capturing flows via asynchronous pipelines, Unified Corporate Schema homogenization in the layer and dynamic in-memory $M_{GT}$ matrix every line of the approved analysis text, which ensures its nourishment, is directly included. Official API_Log_ID, official Document_Number, and microsecond Timestamp 10 combined with the data and cryptographically analyzed using the SHA-256 algorithm. to be signed and directly onto the report data payload as an untamperable forensic seal. embedding, numerical tolerance ($\epsilon$) of free text claims and semantics A piecewise $f_v(M_{AI}, M_{GT})$ that runs the exact equality ($\equiv$) criteria. 15 that enables cell-level control via mathematical functions an AI-powered corporate risk analysis system that provides to accomplish. Detailed Description of the Invention The "Artificial Intelligence Output Controlled Analysis" was carried out to achieve the purpose of this invention. The "Corporate Risk Analysis System" is shown in the diagram; Figure 1: The subject of the invention is an artificial intelligence-driven output-controlled corporate risk analysis system. This is a schematic view. 25 The parts shown in the figure are individually numbered, and the corresponding numbers correspond to these numbers. It is given below. 1. System 30 6 2. Application 3. Analytics server Corporate structures that have submitted their tax identification number (TIN) and business identification information. working with the goal of "zero errors" in risk management, artificial intelligence (AI) semantics 5 analytical capability with the deterministic precision of official sources as an operational line The invention, which enables its integration, is an artificial intelligence output. a supervised corporate risk analysis system (1); - used by the user, with its hardware and software, and the user's 10. an electronic device that operates on and provides the user with an interface on it. structured to allow the entry of company name, holding trademark or name at least one application (2) and - by connecting with the application (2), official institution servers and databases, Thanks to the NLP module located on it, the user can access the (2) interface in the application 15 accessing the raw input in the name of the company, holding company, or brand. cleaning up spelling asymmetries, character normalization completion and semantic tokenization step and user access to the application (2) the structured search string of the entered text input To enable its transformation, the Graph-Based Hierarchy Exploration Module 20 located on it with the verified call string belonging to the user coming from the NLP module via TCP / IP and MERSİS and Trade Registry Databases via REST-API lines sending simultaneous and parallel queries to official institutional databases to provide, in this way, the asymmetric schema (JSON / XML) returned from official institutions company identification data, registered titles and shareholding percentages ($P_{i,j}$) 25 In a directed graph topology, nodes and Enabling mapping as edges allows the user to... (2) cross-partnerships in complex group structures related to the information entered into the interface Graph-Based Hierarchy Exploration in Preventing the Creation of an Infinite Loop The module includes Breadth-First Search (BFS) and Loop 30. 7 Running cycle detection algorithms, so that the visited Each unique VKN is stored in a "Visited_Nodes_Set" cluster, thus saving memory. to prevent stack overflows during runtime, Graph-based with integrated corporate chart and golden record matrix Based on the graph topology generated by the Hierarchy Discovery Module, the official data lines are 5. (Time Stamp Transaction Logs) and (xBRL / XML Formatted Financial Statement Data) (structures) to enable the user to be supplied with asynchronous parallel pipelines. all heterogeneous information collected in relation to the information entered in the application (2) interface unified corporate schema on the data ensuring conversion to homogeneous data types at the layer, the converted exact 10 Documentation and control of data at the in-memory (caching) layer. A Gold lock that is unchangeable and unmanipulated throughout the process To ensure that the matrix is ​​saved as a record matrix ($M_{GT}$), To enable it to serve as the absolute reference (Ground Truth) dataset, Prompt 15, powered by the AI ​​analysis core and the golden record matrix, by running the large language model (LLM) architecture under its constraints, in natural language, To enable the production of a draft analysis text in free text format, the draft analysis text generated by the software interceptor on it Before infiltrating the interface (2) in the application, working on the operational line 20 at runtime by a software interceptor (interrupt gateway) to ensure its capture and quarantine, the separation unit on it and the Draft Analysis Text, which was quarantined based on the work of the AI ​​claim matrix, Semantic parsers and Regex (Regular Expressions) within the Parsing Unit to enable morphological analysis through the use of engines, thereby ensuring natural all probabilistic claims within the language text ("...company's VKN is X, liquidity is 25") expressions such as "the ratio is Y, the current status is Z" should be removed, and the claims in question should be examined. A structured, temporary AI Assertion Matrix with a key-value-status architecture. Enabling the conversion of ($M_{AI}$) is done temporarily with the rule engine located on it. The AI ​​Claim Matrix ($M_{AI}$) is stored in memory, and the in-memory locked Golden Record is also stored there. Performing a cell-by-cell cross-query on the matrix ($M_{GT}$), 30 8 the validation process is carried out through a piecewise "$f_v$" function to provide, with the Deterministic Feedback Loop on the rule engine The closed-loop system is activated the moment a discrepancy is detected in cell control ($f_v == 0$). To enable the circuit correction loop to run, isolate the GHTA-generating cell. the absolute accuracy of the data in the Golden Record Matrix is ​​based on a rigorous mathematical 5 Prompt Constraint Injection as a Constraint Parameter in the AI ​​Analysis Core it should be injected during the working time and only the relevant cell should be corrected. to force it to reproduce in this way, thereby ensuring the entire matrix of the cycle The cells remain closed-loop for seconds until they receive confirmation of $f_v == 1$. to ensure its completion, with the Cryptographic Resource Sealing Unit on it 10 The final text, having received full approval ($f_v == 1$) from the rule engine, is then sent to the official API. The unique API_Log_ID, proprietary Document_Number, and microsecond Time associated with the queries. Its signature is combined with metadata and cryptographically analyzed using the SHA-256 algorithm. finally The final analysis output is implemented with the secure report output module on it (2) 15 to enable sending to the interface or serving to external integrated platforms It includes at least one analytics server (3) configured for this purpose. The application (2) in the system (1) which is the subject of the invention is the user’s mobile phone, tablet, working on an electronic device in the form of a computer, on its interface The user enters input in the form of trademark name, unregistered title segments. to provide, transmit user input to the analysis server (3) We are being structured. 25 The invention concerns the NLP module on the analysis server (3) located in the system (1). processing of the raw input of the user coming from the application (2), so that To ensure the removal of morphological noise from the user's raw input. It is structured accordingly. 30 9 With the Graph-Based Hierarchy Discovery Module on the analysis server (3) The extracted affiliate, ownership and user input from the application (2) In a directional graph data model on memory within cross-partnership data. ($G= (V, E) $) to ensure that it is constructed as nodes and edges 5 It is being structured. With the unified enterprise schema and golden record matrix on the analysis server (3) from different official institutions and regarding the information entered by the user into the application (2) Non-homogeneous data (XML, JSON, plain text, 10) coming from external data stream lines. To enable the grouping of xBRL data schemas under a single framework, thereby Official Registry APIs in the form of MERSİS and Commercial Registry Gazette registration records, The Revenue Administration (GİB) provides Tax Identification Number (VKN) and NACE code data. Official APIs in the form of databases, live data belonging to the Public Disclosure Platform Asynchronous data lines of real-time streaming xBRL and XML financial statement packets 15 (Stream Processing) extracts data and normalizes it into a homogeneous data type. It is structured to ensure that this is done. AI analysis core and text generation LLM on analysis server (3) By running the engine together, prompt 20 is fed from the golden register matrix. processing of the constraints with LLM, so that the user can access the interface (2) in the application. analytical inferences and strategies in free text format related to the information received. It is structured to enable the generation of grades. AI Analysis 25 with Draft Analysis Text Layer on Analysis Server (3) A probabilistic hypothesis, generated by its core, whose accuracy has not yet been tested. in character and unstructured draft natural language text It is structured to ensure that it is retained. The validation process in the rule engine on the analysis server (3) “f_v(M_AI, M_GT) = 1 if |M_AI(i,j) - M_GT(i,j)| ≤ ε or M_AI(i,j) ≡ M_GT(i,j) f_v(M_AI, M_GT) = 0, in all other cases (Hallucination / Deviation)” the execution of the sentence is done through a piecewise "$f_v$ function, In the function, ε represents the micro-deviation constraint defined for numerical ratios, and the symbol ≡ represents 5. to ensure semantic exact match for string / status data. It is being structured. In one arrangement of the invention, the analysis server (3) allows the user to apply (2) 10 related holding company subsidiary networks and cross-partnership spirals based on the information entered Depth-First Search (DFS), Dijkstra in the process of solving it The algorithm or matrix that calculates the transitive closure of the graph. To enable the execution of multiplication-based accessibility algorithms. It is being structured. In one arrangement of the invention, the analysis server (3) in relationship mapping processes Recursive Collaboration on Optimized Relational Databases (RDBMS) Using Table Expressions (Recursive CTE - Common Table Expressions) to enable the construction of hierarchical interrogation pipelines It is being structured. 20 In one arrangement of the invention, the analysis server (3) outputs the model output during runtime. the software-based cutting architecture that captures, the application layer using a block of code (middleware) that runs within it, at the network layer using a localized API Gateway, a reverse proxy (Reverse Proxy - 25 For example: Envoy / Nginx using an eBPF filter layer or inter-microservice using a Service Mesh (Sidecar Proxy) architecture that controls communication It is structured to provide this. 11 In one arrangement of the invention, the analysis server (3) data integrity and tampering SHA-3, BLAKE3, and other higher-generation technologies are used to guarantee immutability. cryptographic hash functions or Asymmetric Encryption (RSA / ECDSA) based digital signature infrastructures or distributed ledger technologies (Blockchain / DLT) is an equivalent of both non-repudiation and source anchoring. 5 It is structured to enable its use as such. Dynamic Data on the analysis server (3) in an arrangement of the invention Start the Masking and Anonymization Engine, while simultaneously running the Golden Record. Data retrieved from the matrix ($M_{GT}$) is sent to the AI ​​Analysis Core 10 first ensure it is scanned by the relevant layer, which contains trade secrets. sensitive financial data, individual shareholder names, or restricted corporate identities data at runtime with temporary cryptographic tokens to ensure that the Golden Record Matrix, which is stored in memory, is masked. ($M_{GT}$) ensures that the application is kept constantly live and hot (hot data), user 15 (2) waiting for external APIs to respond by initiating a query on it eliminating the need for delay, reducing latency to milliseconds. to ensure that it is reduced to that level and CPU cycles are saved It is structured accordingly. Local Feedback on the analysis server (3) in an arrangement of the invention The Learning and Vector Database Layer execution uses deterministic feedback. how many times the feed cycle is triggered, which prompt constraints are injected and in which cells artificial intelligence tends to produce hallucinations to ensure that this is recorded, so that artificial intelligence can record the past 25 applying lessons learned from errors ("Constraint Injection Logs") (2) In the next similar query entered, the model will be "Few-Shot Prompting". by providing upfront feeding through this method, thus creating a feedback loop. the frequency of the correction cycle decreases over time and the initial artificial intelligence 12 It starts generating 100% accurate text per transaction, which reduces transaction costs (tokens). It is structured to ensure that (costs) are radically reduced. In one arrangement of the invention, the Vector Space Based on the analysis server (3) The Secondary Semantic Decision Engine is being run, rule engine 5 To provide flexibility to rigid / deterministic string matching, artificial There is a semantic equivalent between the text produced by intelligence and official records. However, expressions that are different in wording (while "Active Activity" is written in the official record) AI generating "Live Working Status" through vector similarity. to ensure that it is checked, so that instead of just a strict string / character match, 10 by checking the spirit and semantic accuracy of the expression, the rule engine Preventing unnecessary blockages (increasing the False Positive rate) and It is structured to ensure that the flow of the processes is increased. Industrial application of the invention 15 The system in question (1) runs the application on the user's electronic device (2) provides input of a company name, holding name, brand name to the interface. Analysis with various modules, layers and located on the server (3) AI-powered software in modules / layers analyzes user input 20 The analysis server (3) thus enables generative artificial intelligence models to analyze the data. strategic texts in natural language that are probabilistically generated data manipulations (hallucinations) during runtime to capture, dismantle on the operational line, official authorities' definitive (deterministic) data for cell-based verification and cryptographic verification 25 It seals them. In this way, corporate group structures (Parent-Subsidiary relationships) and AI's reliability / data fitting in financial reporting outputs The problem has been resolved. 13 The system in question (1) creates a directional graph of companies in the official registry records. Modeling in the topology ($G=(V,E)$), cross-cutting in holding and subsidiary networks Partnership spirals and indirect stakes in Breadth-First Search (BFS) and Circular lockups are detected by scanning with Cycle Detection algorithms. and resolving stack overflows in memory, Public 5 Instant xBRL financial information from the Public Disclosure Platform (KAP) and stock exchange bulletins. Capturing notification streams via asynchronous pipelines, Unified Corporate Homogenization at the schema layer and the in-memory $M_{GT}$ matrix Dynamically fed, each line of the approved analysis text is directly reflected in the official document. API_Log_ID, official Document_Number and microsecond Timestamp data with 10 combined and cryptographically signed using the SHA-256 algorithm and embedding an untamperable forensic seal directly into the report data payload, numerical tolerance ($\epsilon$) and semantic complete equality of free text claims A piecewise $f_v(M_{AI}, M_{GT})$ mathematical function that runs the ($\equiv$) criteria. Cell-based monitoring is enabled via the function. 15 The system in question (1) is within the scope of the Personal Data Protection Law (KVKK). It operates. The subject of the invention is an artificial intelligence output-controlled corporate risk analysis system (1) 20 It is possible to develop various applications of the invention described here. It cannot be limited to examples; it is essentially as stated in the requests.

Claims

14 REQUESTS 1. Corporate structures that have submitted their tax identification number (TIN) and business identification information. working with the goal of "zero errors" in risk management, artificial intelligence (AI) semantics analytical capability with the deterministic precision of official sources, operational line 5 enabling its unification; - used by the user, with its hardware and software, and the user's the ability to communicate with other people, and to access local and global networks an electronic device that operates on and provides the user with an interface on it. 10 configured to allow the entry of company name, holding trademark or name containing at least one application (2) and - by connecting with the application (2), official institution servers and databases, Thanks to the NLP module located on it, the user can access the application's (2) interface. accessing the raw input in the name of the company, holding company, or brand. Cleaning up spelling asymmetries, character normalization 15 completion and semantic tokenization step and user access to the application (2) the structured search string of the entered text input to enable its transformation, the Graph-Based Hierarchy Exploration Module located on it with the verified call string belonging to the user coming from the NLP module via TCP / IP and 20 databases in the form of MERSİS and Trade Registry Databases via REST-API lines. sending simultaneous and parallel queries to official institutional databases to provide, in this way, the asymmetric schema (JSON / XML) returned from official institutions company identification data, registered titles and shareholding percentages ($P_{i,j}$) In a directed graph topology, nodes and Enabling the mapping of edges allows the user to see 25 areas in the application. (2) cross-partnerships in complex group structures related to the information entered into the interface Graph-Based Hierarchy Exploration in Preventing the Creation of an Infinite Loop The module includes Breadth-First Search (BFS) and Loop features. Running cycle detection algorithms, so that the visited Each unique VKN is stored in a “Visited_Nodes_Set” cluster, allocating memory 30 to prevent stack overflows during runtime, Graph-based with integrated corporate chart and golden record matrix The official data lines are determined via the graph topology generated by the Hierarchy Discovery Module. (Time Stamp Transaction Logs) and (xBRL / XML Formatted Financial Statement Data) (structures) to ensure that they are fed by asynchronous parallel pipelines, user 5 all heterogeneous information collected in relation to the information entered in the application (2) interface unified corporate schema on the data ensuring conversion to homogeneous data types at the layer, the converted precise Documentation and control of data at the in-memory (caching) layer. A Gold 10 that is locked in a way that cannot be altered or manipulated throughout the process. To ensure that the matrix is ​​saved as a record matrix ($M_{GT}$), To enable it to serve as the absolute reference (Ground Truth) dataset, prompt, powered by the AI ​​analysis core and the golden record matrix, by running the large language model (LLM) architecture under its constraints, in natural language, To enable the production of a draft analysis text in free text format, 15 the draft analysis text generated by the software interceptor on it Before infiltrating the interface (2) in the application, working on the operational line by a software interceptor (gateway) at runtime to ensure its capture and quarantine, the separation unit on it and the Draft Analysis Text, quarantined based on the AI ​​claim matrix analysis, 20 Semantic parsers and Regex (Regular Expressions) within the Parsing Unit to enable morphological analysis through the use of engines, thereby ensuring natural all probabilistic claims within the language text ("...company's VKN is X, liquidity") expressions such as "the ratio is Y, the current status is Z" should be removed, and the claims in question should be examined. 25 Structured Temporary AI Assertion Matrix with Key-Value-Status Architecture Enabling the conversion of ($M_{AI}$) is done temporarily with the rule engine located on it. The AI ​​Claim Matrix ($M_{AI}$) is stored in memory, and the in-memory locked Golden Record is also stored there. The matrix ($M_{GT}$) should be subjected to cell-by-cell cross-query. the validation process is carried out through a piecewise "$f_v$" function to provide, with the Deterministic Feedback Loop on it, the rule engine 30 16 The closed-loop system is activated the moment a discrepancy is detected in cell control ($f_v == 0$). To enable the circuit correction loop to run, isolate the GHTA-generating cell. the absolute accuracy of the data in the Golden Record Matrix is ​​determined by rigorous mathematical precision. Prompt Constraint Injection as a Constraint Parameter in the AI ​​Analysis Core It should be injected during the working time and only the relevant cell to be corrected. 5 to force it to reproduce in this way, thereby ensuring the entire matrix of the cycle The cells remain closed-loop for seconds until they receive confirmation of $f_v == 1$. to ensure its completion, with the Cryptographic Resource Sealing Unit on it. The final text, having received full approval ($f_v == 1$) from the rule engine, is then sent to the official API. The unique API_Log_ID, registered Document_Number, and microsecond Time associated with the queries are 10. Its signature is combined with metadata and cryptographically analyzed using the SHA-256 algorithm. finally The final analysis output is implemented with the secure report output module on it (2) to enable sending to the interface or serving to external integrated platforms 15 characterized by having at least one analysis server (3) configured for this purpose an AI output-controlled corporate risk analysis system (1).

2. On the user's electronic device such as a mobile phone, tablet, or computer to work, the user's trademark name, unregistered title on the interface Enabling input to be written in segments, analyzing user input 20 The application (2) is characterized by being configured to transmit to the server (3) An AI output-controlled enterprise risk analysis system as in Claim 1 (1).

3. Raw input from the user coming from the application (2) with the NLP module on it processing, thereby reducing morphological noise in the user's raw input by 25%. characterized by the analysis server (3) configured to ensure its cleaning. An AI-controlled corporate risk analysis, as in Claim 1 or 2. system (1). 17 4. Graph-based Hierarchy Discovery Module on the application (2) Affiliate, ownership, and cross-partnership data extracted related to user input. Nodes in a directed Graph Data Model ($G= (V, E) $) on memory within it and an analytics server configured to enable the construction of edges. artificial 5 as in any of the above demands characterized by (3). an intelligence output controlled corporate risk analysis system (1).

5. With its integrated corporate structure and golden record matrix, the user information entered into the application (2) from different official institutions and external data streams Non-homogeneous (XML, JSON, plain text, xBRL) data coming from lines 10 to bring together the schemes under a single roof, thereby enabling MERSİS and Commercial Official Registry APIs in the form of Registry Gazette registration records, Revenue Administration The General Directorate of Taxation (GİB) has databases for Tax Identification Numbers (VKN) and NACE codes. Official APIs in this form, live real-time streaming of the Public Disclosure Platform Asynchronous data lines of xBRL and XML financial statement packages (Stream 15 (Processing) extracting and normalizing the data into a homogeneous data type. characterized by the analysis server (3) configured to provide AI output control is performed as in any of the above requests. corporate risk analysis system (1).

6. The AI ​​analysis core and the text generation LLM engine working together. By running it, the prompt constraints fed from the golden register matrix are applied to the LLM. processing of the information that the user enters into the application's (2) interface. relevant analytical inferences and strategy notes in free text format 25 characterized by the analysis server (3) configured to enable its production As in any of the requests made above, artificial intelligence output is controlled. corporate risk analysis system (1).

7. By the Draft Analysis Text Layer on top of it, by the AI ​​Analysis Core generated, not yet verified, probabilistic in nature and 30 18 To ensure the retention of unstructured draft natural language text. The above is characterized by the analysis server (3) which is configured for the above. AI output control in any of the requests is a corporate risk. analysis system (1).

8. The validation process in the rule engine on it is “f_v(M_AI, M_GT) = 1, if |M_AI(i,j) - M_GT(i,j)| ≤ ε or M_AI(i,j) ≡ M_GT(i,j) f_v(M_AI, M_GT) = 0, in all other cases (Hallucination / Deviation)” is a fragmented “$f_v$ the execution through the function, in which ε is the numerical ratio. The defined micro-deviation constraint, and the ≡ symbol, is 10 for string / statistical data. analysis structured to ensure that semantic agreement is fully expressed as in any of the above requests characterized by its server (3) an AI output-controlled corporate risk analysis system (1).

9. Holding subsidiary networks and 15 related to the information entered by the user into the application (2). Depth-First Search (DFS) in the process of unraveling cross-partnership spirals. Depth-First Search), Dijkstra's Algorithm, or the transit closure of a graph (transitive closure). Matrix multiplication-based accessibility algorithms that calculate (closure) characterized by the analysis server (3) configured to enable its operation. As in any of the requests made above, the AI ​​output is a controlled 20 corporate risk analysis system (1).

10. Optimized relational databases (RDBMS) in relationship mapping processes Recursive Common Table Expressions (CTE) Building hierarchical query pipelines using Expressions 25 characterized by the analysis server (3) configured to provide AI output control is performed as in any of the above requests. corporate risk analysis system (1). 19 11. A software-based cutting architecture that captures the model output during runtime, a block of code that runs within the application layer (middleware) uses an API Gateway located at the network layer. using a reverse proxy (e.g., Envoy / Nginx eBPF filter) Service 5 (which controls the use of the microservice layer) or communication between microservices) Analysis configured to enable the use of Mesh (Sidecar Proxy) architecture. as in any of the above requests characterized by its server (3) an AI output-controlled corporate risk analysis system (1).

12. SHA-3 is used to guarantee data integrity and untamperability. 10 Next-generation cryptographic hash functions like BLAKE3 or Asymmetric Encryption (RSA / ECDSA) based digital signature infrastructures or distributed ledgers technologies (Blockchain / DLT) have a non-repudiation and source sealing capability. Configured to enable its use as an equivalent to (Source Anchoring) Any of the above requests characterized by the analysis server (3) 15 an AI output-controlled corporate risk analysis system like one (1).

13. Dynamic Data Masking and Anonymization Engine the execution of the AI ​​process, during which data is retrieved from the Golden Record Matrix ($M_{GT}$). It must be scanned by that layer before being sent to the Analysis Core. 20 to provide sensitive financial data containing trade secrets, and the real names of individual partners or restricted corporate identity data temporarily cryptographically at runtime to ensure that the Gold is masked with tokens, thus keeping it in memory. Ensuring the log matrix ($M_{GT}$) remains constantly live and hot data, When the user initiates a query on the application (2), the external APIs respond 25 eliminating the obligation to wait for delivery, reducing the latency reducing processing times to milliseconds and saving CPU cycles. characterized by the analysis server (3) configured to provide AI output control is performed as in any of the above requests. Corporate risk analysis system (1). 30 14. Local Feedback Learning and Vector Database Layer on Top of It the number of times the deterministic feedback loop has been triggered, which prompt constraints were injected and in which cells the AI ​​was located To ensure that it is recorded that it tends to produce hallucinations, so that 5 lessons learned from past mistakes of artificial intelligence ("Constraint Injection") In the next similar query entered on the application (2) "Logs", the model is given "Few- Providing pre-fed input using the "Shot Prompting" method, thus providing feedback. the decrease in the operating frequency of the correction loop in the cycle over time and The artificial intelligence should start generating 100% accurate text on the first try, which takes 10 minutes. to ensure that their costs (token costs) are radically reduced from the above requests characterized by the configured analysis server (3) an AI-driven corporate risk analysis system, like any other. (1).

15. Vector Space Based Secondary Semantic Decision Engine its execution allows the rule engine to be flexible with strict / deterministic string matching. to ensure that it is integrated between the AI-generated text and official records. expressions that have the same semantic meaning but are different in word form (While the official record says "Active Activity," the AI ​​produces "Working Status Live") 20 to enable verification via vector similarity, so that only a strict one Instead of just matching strings / characters, the spirit and semantic accuracy of the expression are also important. by checking if the rule engine unnecessarily initiates a block (False Positive) to prevent (the rate) and to improve the fluidity of the processes performed. The above 25 is characterized by the analysis server (3) configured for this purpose. AI output control in any of the requests is a corporate risk. analysis system (1).