Predictive AI Platform

A signal platform that had to be right often enough to trade on. Sentiment, technical indicators and whale movement fused into one score — 35% better accuracy, serving 500+ active users.

The problem

Traders already have too many inputs. Sentiment feeds say one thing, indicators say another, and on-chain whale movement says a third — each in its own tab, each on its own timescale. The work isn't collecting more signal. It's reconciling signals that disagree, and being honest about how much to trust the result.

The approach

Three independent channels, scored separately and then combined:

• NLP sentiment modelling over social and news text, normalised so a quiet market and a loud one are comparable rather than one drowning the other
• Technical indicators computed on consistent windows, so a signal means the same thing at every timeframe
• Whale tracking — large-holder movement treated as a leading input rather than an after-the-fact explanation

The combination layer is where the accuracy came from. Weighting each channel by its recent hit rate, rather than fixing the weights up front, meant the platform degraded gracefully when one input went noisy instead of confidently passing the noise through.

Results

• 35% better signal accuracy against the prior single-channel baseline
• 500+ active users on the platform
• Scores exposed with their own confidence, so a weak signal reads as weak

What it demonstrates

The modelling is the visible half. The half that decided whether anyone kept using it was calibration — a platform that says "high confidence" and is wrong loses trust faster than one that admits it doesn't know.