// about

Agents are changing how decisions get made.

I started TickerDB because I experienced first hand how much agents struggled with raw market data.

After months of trial and error, I found agents performed better with abstracted categorical bands. Reducing token costs and improving reasoning.

Most financial tools still assume the human is doing all the work. They show charts, indicators, news, and leave the interpretation to you.

That made sense when software could only display information. It makes less sense when models can reason, call tools, and keep working while you sleep.

The human isn't going away. The role changes. Instead of digging through every chart, indicator, and news headline, the agent can do the heavy first pass: watching continuously, researching deeply, and surfacing the opportunities and risks that deserve your attention.

The interface doesn't disappear. It becomes more dynamic. The dashboard you open may be generated around what the agent found: a summary, a chart, a comparison, a set of scenarios, or a focused view built for the decision in front of you.

For that workflow to work, agents need context on top of raw price data. OHLCV is foundational. But agents also need the missing categorical layer: bands, conditions, anomalies, and state changes they can reason over without rebuilding the same picture from scratch.

That context should already exist. It should be structured, categorical, and easy for a model to use. Not because the model is weak, but because better context changes the quality of the research it can do and the opportunities it can surface.

Financial software is moving toward a workflow where agents do ongoing research. Enabling humans to make faster, better informed decisions.

Our team is building the layer that powers the agentic workflow.