ShillBoard holds crypto analysts accountable.
Every prediction logged. Every outcome measured.
The record speaks for itself.
You're on the list.
How it works
ShillBoard reads predictions from public content — articles, video, social posts, newsletters, podcasts — uses a language-model inference pipeline to extract each claim, then scores it against what the market actually did. Here is the path a single prediction takes through the system.
Illustrative. Predictor and figures shown are examples.
What it does
Crypto commentary is full of confident calls with no accountability. ShillBoard tracks who is actually right.
Predictions are logged at the moment they are made — timestamped, attributed, and scored when the target date arrives.
Ranked by verified accuracy, not follower count. No hiding. No revisions. The data decides.
Where we stand
It records who predicted what about a token and when, then scores the call once its date passes — a ranking built on a record predictors cannot rewrite after the fact.
When a predictor restates a call or changes their stance, that is logged too — linked into a chain. Changing your mind is captured, not hidden, and repeating the same call doesn't pad the score.
Every score is reproducible from the recorded price it was resolved against. Small samples are corrected, so a few lucky hits don't outrank a long, steady record.
ShillBoard reports what others predicted and whether it came true. It makes no recommendations and no signals of its own. Informational and educational only.
The unit is a prediction scored after its date passes — not a live trading signal, and not a place to post. The product is the scored record.
A predictor cannot delete or quietly edit a past call. The record stands; if their view changes, the new call is added to the chain — never swapped for the old one.
The business
ShillBoard is a product of Data Argo Ltd, a UK-registered AI-native data studio (company no. 16174805). Data Argo builds products and engineering practices around agentic and large-language-model technology — applying AI inference both to what its products do and to how they are built.
ShillBoard is its first product: an accountability layer for crypto market commentary. It reads predictions from public content, uses a language-model inference pipeline to extract each claim, and scores it against real market outcomes. The result is a track record built from what was actually said, measured against what actually happened.
The platform is built on Google Cloud and is preparing for launch.
Tony has spent 25+ years in data architecture and engineering, in senior and head-of roles across financial services and enterprise data platforms, including a senior solutions architect role at Snowflake. He designed ShillBoard's inference pipeline — extracting predictions from public content, scored against market outcomes — and builds with an agentic engineering practice.
Get in touch
For partnership, press, or programme enquiries, reach the team directly. We're happy to walk through the product and architecture.