A
- Agentic AI: The Ekyam solution layer utilizes AI, large language models (LLMs), and agent-based architectures to enable smart interactions, generate insights, and support autonomous operations for retailers.
- AI Agents: Specialized AI agents are designed to carry out tasks, support users, or make decisions within specific retail domains.
- AI-Powered Data Mapping: Ekyam uses Artificial Intelligence to analyze source and target data schemas (including EDI/iDOC structures) and suggest field mappings, simplifying integration setup.
- Actions (Ekyam Workflows): It includes specific actions performed within a workflow such as API calls, database lookups, EDI/iDOC processing, or sending notifications.
- Apache Kafka: An open-source, distributed event streaming platform used by Ekyam as its central message backbone for high-throughput, fault-tolerant, and scalable data handling.
- Asynchronous AI Task Management: The use of Apache Kafka within Ekyam to queue and manage AI tasks that are long-running, decoupling them from immediate user interaction.
- API (Application Programming Interface): A set of rules and protocols that allows different software applications to communicate and exchange data. Ekyam’s Universal Connector supports various API standards.
- API Key Authentication: An authentication method where an API key (a unique string) is used to grant access to an API. Supported by Ekyam’s Universal Connector.
- Authentication: The process of verifying the identity of a user, system, or application attempting to access Ekyam. Ekyam primarily uses JWT (JSON Web token) for this.
- Authorization: The process of determining what actions an authenticated user, system, or application is permitted to perform within Ekyam, often managed by Role-Based Access Control (RBAC).
- Authorization Code Grant (OAuth 2.0): An OAuth 2.0 flow used by web applications where a user grants permission for an application to access their resources.
B
- B2B Document Exchange: The process of businesses electronically exchanging structured documents like purchase orders, invoices, and shipping notices, often using EDI or iDOC formats. Ekyam facilitates this.
- Batch Jobs: A traditional integration method involving periodic extraction, transformation, and loading of data between systems at scheduled intervals. Ekyam aims to reduce reliance on these.
- Basic Authentication (HTTP): A simple authentication scheme built into the HTTP protocol, where the client sends a username and password with each request. Supported by Ekyam’s Universal Connector.
C
- Chronicle: Ekyam’s unified data engine that stores and contextualizes all product and inventory activity across the retail stack.
- Canonical Data Models (Ekyam Data Standards): Standardized representations for key business entities (e.g., Product, Order, Customer, Inventory) are defined by Ekyam to ensure consistent data interpretation.
- CDC (Change Data Capture): A technique for tracking and capturing data changes in a database by reading its transaction logs, enabling near real-time event detection for Ekyam Event Listeners.
- Chunking (RAG): The process of breaking down large documents into smaller, semantically coherent pieces for optimizing context retrieval for Ekyam’s Retrieval-Augmented System (RAG).
- Conditional Logic (Ekyam Workflows): The ability within Ekyam Workflows to execute different branches of steps based on specified conditions (e.g., IF/THEN/ELSE, Switch/Case).
- CRM (Customer Relationship Management): Systems used to manage customer interactions, data, and relationships. Ekyam integrates with CRMs.
- Configuration Isolation (Multi-Tenancy): An architectural principle in Ekyam ensuring that each client’s specific configurations (e.g., connectors, workflows, EDI/iDOC profiles) are separate and do not affect other clients.
- Confidence Scoring (AI Data Mapping): A score provided by Ekyam’s AI with each mapping suggestion, indicating the AI’s certainty about the match.
D
- Data Isolation (Multi-Tenancy): A core security principle in Ekyam ensuring that each client’s data is strictly separated and inaccessible to other clients.
- Data Warehouses: Ekyam integrates with the centralized repositories for storing large volumes of raw and processed data for analytics.
- Data Mapping: Ekyam does AI-powered data mapping. It is the process of defining correspondences between data fields from a source system and a target system.
- Data Silos: A situation where information is isolated within individual systems, preventing a holistic view of business operations. Ekyam aims to eliminate these.
- Document Loaders (LangChain): LangChain components used to ingest data from various sources (files, web pages) for processing in RAG pipelines.
- Decoupling (EDA): An architectural benefit of Event-Driven Architecture where event producers and consumers operate independently, communicating indirectly, which enhances resilience and scalability. Ekyam leverages this.
E
- eCommerce Platforms: Eykam integrates with the software applications that power online stores.
- EDA (Event-Driven Architecture): It is an architectural paradigm where system behavior is orchestrated by the production, detection, and consumption of events. This is a core principle of Ekyam.
- EDI (Electronic Data Interchange): A set of standards for structuring information to be electronically exchanged between businesses. Ekyam provides robust support for parsing, processing and generating EDI documents.
- Embedding Models (RAG): Machine learning models used in Ekyam’s RAG system to convert text or data chunks into numerical vector embeddings that capture semantic meaning.
- ERP (Enterprise Resource Planning): Integrated management software for core business processes. Ekyam integrates with ERPs, including SAP systems via iDOCs.
- Event Listeners (Ekyam): Components of Ekyam that actively monitor connected source systems (via polling, webhooks, CDC, file detection) for business events, acting as the platform’s sensory network.
- Event-Based Triggers (Ekyam Workflows): Workflow initiators that automatically start a workflow when a specific event is detected by an Ekyam Event Listener.
F
- FTP/SFTP Servers: File Transfer Protocol / Secure File Transfer Protocol servers used for file exchange. Ekyam’s Universal Connector can connect to these, often for EDI/iDOC transfer.
- File-Based Listeners (Ekyam): Event listeners that monitor specific directories (e.g., on FTP/SFTP, cloud storage) for new or modified files, including EDI or iDOC documents.
- Fine-Tuning (LLM): The process of further training a pre-trained LLM on a smaller, domain-specific dataset to adapt its knowledge and improve its performance on particular tasks.
- Functional Microservices (Ekyam): Core Ekyam platform capabilities (e.g., authentication, order processing, inventory ledger) designed as independent, scalable microservices.
G
- Graphical Workflow Designer (Ekyam): An intuitive, visual drag-and-drop interface within Ekyam for designing and configuring business process workflows.
H
- Hallucinations (LLM): A phenomenon where LLMs generate plausible but incorrect or fabricated information. Ekyam’s RAG architecture helps mitigate this.
I
- iDOC (Intermediate Document): A standard data container format used by SAP systems for exchanging business transaction data (e.g., orders, deliveries, invoices) with other SAP or non-SAP systems.
- iDOC Parsing Engine: A component within Ekyam’s Universal Reader responsible for interpreting the structure and content of incoming SAP iDOC files.
J
- JSON (JavaScript Object Notation): A lightweight data-interchange format commonly used in REST APIs. Ekyam processes and generates JSON.
- JWT (JSON Web Token): An open standard for securely transmitting information between parties as a JSON object, used by Ekyam for authentication and authorization.
L
- LangChain: An open-source framework used by Ekyam for developing applications powered by LLMs, providing modular components for chains, agents, memory, and tool usage.
- LangGraph: A library built on LangChain, used by Ekyam to create stateful, multi-actor AI applications with LLMs by modeling them as cyclical graphs.
- LangSmith: A platform for debugging, testing, evaluating, and monitoring LLM applications, used by Ekyam to ensure the observability and reliability of its LangChain-based AI agents.
- Large Language Models (LLMs): Advanced AI models (e.g., OpenAI, Gemini) capable of understanding and generating human-like text, forming a core part of Ekyam’s Agentic AI.
- Logical Data Segregation: A primary method for data isolation using a client’s ID to ensure all operations are confined to the data of the authenticated client.
M
- Machine Learning (ML): A field of AI that enables systems to learn from data without being explicitly programmed. Ekyam uses ML for AI-powered data mapping and potentially in its analytical models.
- MCP (Model Context Protocol): A conceptual framework inspiring Ekyam’s approach to structuring how AI agents access and utilize context, tools, and resources for effective task performance.
- Microservice Architecture: Ekyam’s platform-wide design where core functionalities and connectors are built as small, independent, and scalable services that communicate with each other.
- Middleware: Software that acts as a bridge between other applications, databases, and services, facilitating communication and data exchange. Ekyam functions as an advanced AI-powered middleware for retail.
- Multi-Tenancy: An architecture where a single instance of a software application (Ekyam) serves multiple clients (tenants) while keeping their data and configurations isolated and secure.
N
- Nodes: Units of computation within a LangGraph, representing LLM calls, tool invocations, or custom functions, used to build Ekyam’s AI agents.
- Natural Language Processing (NLP): A field of AI that enables computers to understand, interpret, and generate human language. Used by Ekyam’s AI for conversational interfaces and data mapping.
- NoSQL Databases: Databases that do not use the traditional relational (tabular) model, such as document stores (MongoDB) or key-value stores (Redis). Ekyam can connect to these.
O
- OAuth 1.0/1.0a & OAuth 2.0: Authorization frameworks that allow third-party applications to access user resources without exposing credentials. Supported by Ekyam’s Universal Connector.
- OMS (Order Management System): Software that manages the entire order lifecycle. Ekyam integrates with OMSs.
P
- PIM (Product Information Management): Systems for centralizing and managing product information. Ekyam integrates with PIMs.
- Pinecone Vector DB: A managed vector database used by Ekyam’s RAG system for efficient semantic search and retrieval of contextual information to ground LLM responses.
- POS (Point of Sale): Systems used in physical stores to process transactions. Ekyam integrates with POS systems.
- Polling (Event Listeners): A method where Ekyam Event Listeners periodically query source systems to check for new or updated data.
- Proactive Agents (Ekyam AI): AI agents in Ekyam that can monitor events and data, identify situations requiring attention, and initiate actions or alerts without direct user command.
- Prompts (MCP/LLM): Carefully engineered instructions given to LLMs to guide their behavior, reasoning, and response generation within Ekyam’s AI.
- Protocol Handling (Universal Writer): The capability of Ekyam’s Universal Writer to use the appropriate communication protocol (e.g., HTTP, SFTP) to deliver data to destination systems.
Q
- Quantity Committed: Inventory reserved for sales orders but not yet shipped. Tracked by Ekyam Universal Ledger.
- Quantity OnHand (QOH): Total physical stock at a location. Tracked by Ekyam Universal Ledger.
- Quantity OnOrder: Stock ordered from suppliers but not yet received. Tracked by Ekyam Universal Ledger.
R
- RAG (Retrieval Augmented Generation): An AI architecture used by Ekyam that enhances LLM responses by first retrieving relevant factual context from external knowledge sources (like Pinecone) and providing it to the LLM.
- RBAC (Role-Based Access Control): A security model used by Ekyam to manage user permissions based on assigned roles within a tenancy.
- Redis: An in-memory data store used by Ekyam for high-performance caching in its AI systems, reducing latency and costs.
- Refresh Token Grant (OAuth 2.0): An OAuth 2.0 mechanism to obtain new access tokens without requiring user re-authentication.
- Resilience (Architecture): The ability of the Ekyam platform and its microservices to withstand and recover from failures, ensuring high availability.
- Resources (MCP): Data and knowledge assets (e.g., Universal Ledger data, EDI/iDOC content, product catalogs) that Ekyam AI agents can access and reason over.
- REST (Representational State Transfer) APIs: A common architectural style for web APIs, widely supported by Ekyam’s Universal Connector.
- Retry Mechanisms (Kafka/Workflows): Ekyam’s capability to automatically retry failed operations (e.g., API calls, event processing) a configured number of times to handle transient issues.
- RouterChain (LangChain): A LangChain component used by Ekyam’s intelligent LLM router to decide which LLM or prompt to use for a given query.
S
- Safety Stock: A buffer inventory level maintained to prevent stockouts. Considered by Ekyam’s Universal Ledger.
- Scalability (Architecture): The ability of the Ekyam platform and its components (like Kafka and microservices) to handle increasing volumes of data and transactions by adding resources.
- Schema Analysis (AI Data Mapping): The process by which Ekyam’s AI ingests and understands the structure of source and target data to suggest mappings.
- SCM (Supply Chain Management) Systems: Software used to manage the end-to-end flow of goods, information, and finances in a supply chain.
- SDKs (Software Development Kits - Ekyam): Libraries and tools provided by Ekyam (initially in Python) to enable developers to build custom extensions and integrations for the platform.
- Semantic Search (RAG/Pinecone): Searching for information based on meaning and context rather than just keywords, enabled by vector embeddings and Pinecone in Ekyam’s RAG.
- SequentialChain (LangChain): A LangChain component for linking multiple chains or calls in a sequence, where the output of one becomes the input to the next.
- Single Source of Truth (SSoT): A central, reliable, and consistent repository of data (like Ekyam’s Universal Ledger) that all connected systems can trust and use for decision-making.
- System Prompts (MCP/LLM): High-level instructions defining an Ekyam AI agent’s persona, objectives, and constraints.
T
- Tenant ID (Multi-Tenancy): A unique identifier used in Ekyam to associate all data and configurations with a specific client, ensuring logical data segregation.
- Text Splitters (LangChain): LangChain components used to break down large documents into smaller chunks for effective processing in RAG pipelines.
- Tools (MCP/LangChain): Capabilities or actions (e.g., API calls, database queries, analytical model invocations) that Ekyam AI agents can perform to interact with their environment.
- Triggers (Ekyam Workflows): Mechanisms (event-based, scheduled, manual, API) that initiate the execution of an Ekyam Workflow.
U
- Universal Connector : A versatile Ekyam component, built as microservices, that enables connections to a wide array of external systems, APIs, and data sources using various protocols and authentication methods.
- Universal Ledger: A centralized, real-time, standardized data store within Ekyam that acts as the definitive source of truth for key operational data like inventory, orders, and customer information.
- Universal Reader: An Ekyam component responsible for ingesting raw data from various source systems (including parsing EDI/iDOCs) and transforming it into Ekyam Data Standards.
- Universal Writer: An Ekyam component that takes standardized data from within Ekyam and transforms/formats it for delivery to destination systems or trading partners, including generating EDI/iDOC documents.
V
- Vector Embeddings (RAG): Dense numerical representations of text or data that capture semantic meaning, used by Ekyam’s RAG system with Pinecone for similarity searches.
- Vector Store (Pinecone): A specialized database, like Pinecone, used by Ekyam to store and efficiently query vector embeddings for its RAG system.
W
- Webhooks (HTTP Callbacks): A method where a source system actively sends an HTTP notification to an Ekyam endpoint when an event occurs, enabling real-time data capture.
- WMS (Warehouse Management System): Software for controlling and optimizing warehouse operations. Ekyam integrates with WMSs.
- Workflows (Ekyam): The Ekyam component for defining, orchestrating, and automating multi-step business processes that span across integrated systems, incorporating transformations, business logic, and AI capabilities.
X
- XML (Extensible Markup Language): A markup language for encoding documents in a format that is both human-readable and machine-readable. Handled by Ekyam’s Universal Connector, Reader, and Writer.