It's simple to confuse "AI" with anything that analyzes data or makes predictions in this era of buzzwords. However, it's crucial to distinguish between creating and utilizing AI when it comes to FinFram, a financial screening tool that is becoming popular among analysts and investors.

What FinFram Actually Builds:

FinFram is a Python-based screener that analyzes past stock data, calculates volatility and average price, and creates structured forecasting prompts. This is traditional algorithmic engineering, which is clear, deterministic, and rule-based.

A neural network is not being trained. It's not creating a model from the ground up. AI is not being developed.

Where AI Comes In:

FinFram creates forward-looking insights by integrating with external AI models, namely Groq. These models generate forecasts or sentiment analysis by interpreting the structured financial data. Here's the secret, though:

The AI is not created by FinFram. It takes it out.

It's AI-as-a-service. FinFram receives forecasts from a third-party model after sending data to it. The intelligence is contracted out.

The Hybrid Architecture:

FinFram can be compared to a chef:

They own the recipe (algorithm).

They don't cultivate or crush the spices themselves; the spice blend (AI model) is pre-made.

FinFram is able to remain lean, modular, and concentrated on what it does best, screening and formatting financial data, thanks to this hybrid setup.

Why This Distinction Matters:

Clarity is essential when assessing FinFram for editorial, legal, or technological reasons:

FinFram owns the logic and structure due to algorithmic development.

When AI is integrated, its interpretation depends on outside intelligence.

The next time someone claims that FinFram is an AI tool, you can respond with assurance:

"In reality, it's an algorithmic screener that makes use of AI, but it doesn't create it."

Conclusion:

We don't have any AI model, we use pre-existing models to make our algorithm work. In the future? maybe we will get the chance to train our own model but for now we resume on other resources.