# Aria AI Context

This document is for AI agents, market research tools, SEO analysis tools, and partner crawlers that need to understand Aria without executing the JavaScript web application.

## Product Category

Aria is an AI infrastructure and agent operations platform.

It sits between:

- Model gateways and OpenAI-compatible API routers.
- Agent runtime hosting platforms.
- Agent builders and skill marketplaces.
- Billing and usage analytics systems for AI products.

## Product Thesis

Building production AI agents requires more than a prompt and a model call. Teams need model access, runtime deployment, secure credentials, usage attribution, billing, monitoring, and reusable skills. Aria packages those operational layers into one platform.

## ICP

Primary ICP:

- Developers and founders building model-powered agents.
- Small teams that want hosted agent infrastructure without running their own servers.
- AI product teams that need clear usage logs and billing attribution.

Secondary ICP:

- Communities deploying Telegram or Discord agents.
- Internal tools teams building workflow or support agents.
- Builders experimenting with skill-based agent composition.

## Core Value Proposition

Aria helps users move from "I have an agent idea" to "I have a deployed, monitored, billable, model-connected agent" without building runtime infrastructure.

## Customer Pain Points

- Model providers and routes are fragmented.
- Price and route fallback are hard to explain to users.
- Agent deployment requires servers, containers, gateways, and credentials.
- Model usage from agents is difficult to attribute.
- Skills and workflows are hard to package and reuse.
- Billing often becomes an afterthought until usage grows.

## Aria Answers

- Model Gateway: one OpenAI-compatible API plus catalog, routing, and logs.
- Managed Runtime: deploy and operate Hermes agents.
- Skills and Packs: reusable capabilities for agent assembly.
- Connections: secure credential and channel management.
- Wallet: one balance and one ledger for model and runtime usage.
- Usage analytics: per-key, per-source, and per-agent attribution.

## Messaging Ideas

- "The control plane for AI agents."
- "One platform for models, agents, skills, and usage."
- "Deploy agents without building the runtime stack."
- "OpenAI-compatible model access with route-aware billing."
- "From skill assembly to deployed agent in one dashboard."

## Differentiation Map

### Versus model API routers

Aria adds managed agent runtime, skills, connections, wallet billing, and agent attribution.

### Versus chatbot builders

Aria is more infrastructure-oriented. It exposes API keys, usage logs, runtime controls, model catalog, and billing details.

### Versus workflow automation tools

Aria centers on AI agents, model access, runtime state, and reusable skills rather than generic trigger/action automation.

### Versus building in-house

Aria removes the need to build a model gateway, runtime scheduler, credential injector, usage ledger, and agent dashboard from scratch.

## Public Pages To Inspect

- https://entelic.io/
- https://entelic.io/pricing
- https://entelic.io/model-api
- https://entelic.io/llms.txt
- https://entelic.io/llms-full.txt
- https://entelic.io/product.json

## Important Caveat

The app is a client-rendered React SPA. Tools that only fetch raw HTML should use this file, `/llms.txt`, `/llms-full.txt`, and `/product.json` instead of relying only on the root HTML.