Business · AI Products
The Enterprise AI Agent Platform
That Does the Work, Athena
Athena is an enterprise platform for designing and building AI Agents on top of corporate data and business systems. Deployment and operations are managed in the same place. It connects knowledge retrieval with external systems and tools, turning a wide range of business tasks into intelligent AI services and supporting enterprise-wide AI rollout.
Features
One Environment for Building and Running AI Agents
The full AI Agent lifecycle runs on a single platform: design, resource management, testing and evaluation, and operations.
Workflow & Agent Design
Structured Work as Workflows, Complex Work as Agentic
Connect building blocks such as LLMs, memory, and tools to design each task the right way. Structured work runs as a workflow; complex work runs as an Agentic flow. A finished Agent publishes straight to an API endpoint for integration with external systems.
AI Resource Management
Register a Resource Once, Reuse It Across Agents
LLMs, prompts, knowledge and RAG sources, memory, tools, and sub-Agents are registered and managed as reusable resources. Combine them to fit the task at hand and build new Agents and services quickly.
Test & Evaluation
Validate Performance and Reliability Before Rollout
Compare results across model and prompt combinations. Evaluate the Agent execution trace along with RAG retrieval and answer quality. Performance and reliability are validated systematically before anything goes live.
Agent Lifecycle Management
From Version Control to Deployment, Tracing, and Improvement
Workflows and Agents are versioned and deployed to development and production environments. Track execution history, step-by-step status, errors, and answer quality. Live AI services keep improving on that feedback.
Product Tour
Workflow Canvas
Agent Logic, Built by Drag and Drop
Connect LLMs, memory, and tools on the canvas to assemble service logic. The finished workflow publishes immediately as a REST API endpoint and connects to external systems.
Key Technology
Differentiated Agent Technology
A hybrid execution model, advanced RAG, and evaluation and tracing raise both Agent performance and reliability.
Work that depends on fixed procedure and control runs reliably as a DAG-based workflow. Work that requires judgment and exploration extends into an Agentic plan-and-execute structure. Both execution models are configured on the same platform, matched to the nature of the task.
Document structure analysis, OCR, custom chunking, embedding, hybrid search, and reranking form a single pipeline. Query intent analysis and multi-step retrieval handle the complex questions that a single retrieval pass cannot answer.
Models, prompts, knowledge, memory, tools, and sub-Agents are managed as independent resources. Reusing validated resources across workflows and Agents removes duplicated development and makes services easier to scale and maintain.
Compare model and prompt combinations, and measure RAG retrieval accuracy and answer quality quantitatively. Agent steps, tool calls, processing time, and errors are all traced. Each answer can be checked back to its supporting evidence and source.
REST APIs and MCP connect internal systems, data, and external tools. Personal-data masking, access permissions, version control, and execution audit logs let AI Agents run inside existing security and operations policy.
Enterprise Ready
Architecture for Enterprise-Wide AI Rollout
This is a structure for running AI across an entire group, not for a short-term PoC. Athena builds the AI; AURDA underpins it.
AI Product Suite
Athena and AURDA: Building and Operations, Split by Design
Athena builds business AI with its RAG pipeline, Agent Builder, and extension modules. AURDA carries governance, operations, and infrastructure. One suite covers the path from first deployment to full rollout.
Agentic RAG
Beyond Simple Retrieval: RAG That Reasons
Query intent analysis, query optimization, feedback loops, and multi-step reasoning resolve complex questions that single-pass RAG leaves unanswered. The platform absorbs the implementation complexity.
Universal AI Runtime
n8n, Dify, Docker: Heterogeneous AI, Run as One
Workflows, containers, and legacy Agents scattered across departments are executed, secured, and monitored against a single Control Plane standard. That is what makes enterprise-wide AI governance possible.
Customer Story
Athena, Proven in the Field
Finance, construction, and manufacturing. Select a card for the full story.
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Athena Product Overview
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