Inspiration
Most AI products are still centered around a simple interaction model: users ask questions, and the model returns text.
We wanted to explore a different direction.
Aria was inspired by the idea that AI should not only understand information, but also be able to act on it — using tools, working with code, analyzing structured and unstructured data, and coordinating multi-step workflows across different environments.
Our long-term vision is to build an intelligent system that can move from conversation to execution.
Instead of building another standalone chatbot, we designed Aria as an AI workspace where reasoning, tools, data, code, and workflows can operate together.
What it does
Aria is an AI platform designed to turn natural language into research, code, analysis, and real-world actions.
Users can interact with Aria through conversational interfaces while the system determines which tools, data sources, or execution workflows are required.
Aria can:
- Analyze documents, market data, and structured datasets
- Generate, understand, and modify code
- Run multi-step AI agent workflows
- Call external tools and APIs
- Perform quantitative and financial analysis
- Generate and backtest trading strategies
- Work across research, engineering, and business tasks
- Maintain contextual information across workflows
- Stream reasoning, tool execution, and results back to the user
The goal is to make AI interaction less about generating answers and more about completing work.
How we built it
Aria is built as a modular AI orchestration system.
At the center of the architecture is an AI orchestration layer that handles:
- User intent and context
- Model routing
- Tool selection
- Multi-step execution
- Streaming responses
- Data persistence
- Validation and risk controls
The application uses streaming APIs so users can see model output, task progress, and tool execution in real time.
For more complex tasks, Aria can enter an agentic execution loop:
User request → reasoning → tool call → tool result → further reasoning → final output
We also developed a quantitative engine that includes portfolio optimization, factor analysis, risk modeling, strategy generation, backtesting, and market data processing.
The platform is designed so these capabilities can be exposed through the same AI interface rather than forcing users to manually navigate between independent tools.
Challenges we ran into
One of the biggest challenges was making AI outputs reliable enough to interact with real tools.
A language model can generate convincing responses even when the underlying data is incomplete, so we had to separate model reasoning from actual tool execution and make external data the source of truth whenever possible.
Another challenge was orchestration.
A complex request may require several dependent operations, such as retrieving data, analyzing it, running a model, validating the result, and then presenting the output. Designing a system capable of coordinating these steps while keeping latency manageable required a modular tool architecture and asynchronous execution.
We also had to design fallbacks for cases where cloud AI services or external APIs become unavailable.
Security and data persistence were additional challenges, particularly when synchronizing user-generated strategies, code, and analysis between different clients.
Accomplishments that we're proud of
We are particularly proud that Aria evolved beyond a conventional chat interface.
The system can combine AI reasoning with real tools and execution workflows, including:
- multi-step agent execution
- streaming tool calls
- quantitative analysis
- strategy generation and validation
- financial risk analysis
- backtesting
- code generation and modification
- cross-platform data synchronization
This architecture gives us a foundation that can expand into many different domains without rebuilding the core AI system.
What we learned
Building Aria reinforced an important lesson for us:
The intelligence of an AI product is not determined only by the underlying model.
A large part of useful intelligence comes from the system around the model — context, memory, tools, data access, orchestration, validation, and execution.
We also learned that specialized engines can coexist with general-purpose AI.
Instead of expecting the language model to calculate everything itself, Aria delegates specialized tasks to deterministic tools and domain-specific services.
This makes the overall system more reliable and extensible.
What's next for Aria
Our next step is to make Aria increasingly capable of completing longer and more complex workflows autonomously.
We are working toward:
- deeper multimodal understanding
- more advanced agent orchestration
- persistent project context
- improved tool discovery
- stronger coding capabilities
- enterprise integrations
- real-time data workflows
- collaborative AI workspaces
- expanded quantitative and analytical capabilities
The broader vision is to develop Aria into an intelligent operating layer where users can move seamlessly from an idea to analysis, code, execution, and results.
Built With
- agents
- ai
- api
- cloud
- docker
- fastapi
- firebase
- firestore
- github
- llm
- numpy
- pandas
- postgresql
- python
- pytorch
- quantitative
- react
- redis
- rest
- swift
- swiftui
- typescript
- vector
- websockets
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