How it works
Prism is a small set of services that turn raw market activity into ranked signals and push them to your screen. This page is the honest mid-depth tour: which pieces exist, what they do, and which third parties power them.
The pipeline in one sentence
An on-chain indexer streams Robinhood Chain market data into a central Postgres, an engine ticks every minute to score new tokens against the rules, a tracker keeps updating the peak gain for every recent call, an AI service writes a one-line explanation per signal, and a Telegram bot pushes the results to subscribers.
Data and indexing
The scoring engine
The engine runs every 60 seconds. On each tick it pulls the Robinhood Chain tokens that have been active in the last hour, scores each one, and inserts a signal row when the score crosses the threshold, the token passes every safety gate, and the per-token cooldown has passed.
The scorer reads swap events and pons launch events and always produces the same shape: a number from 0 to 100, split into momentum (0-50) and whale flow (0-50), gated by a market-cap golden band and the safety checks, plus a raw payload of the intermediate numbers the AI explanation uses to write its sentence.
The rules are spelled out on the Chains page. The scorer is tuned against historical outcomes and was re-tuned after the first few hundred calls based on which market-cap and momentum bands actually delivered.
Peak tracker
A separate tracker runs every 60 seconds, scans every signal from the last 30 days, and updates the high-water mark per call. If the current price beats the recorded peak (or beats the at-call price on a never-pumped signal), the new peak is written.
That is how the "Peak" column on cards and the "PEAK GAIN" stat on token pages stay honest over time. The peak never goes down. Even a round-tripped runner reads as a winner here, because the question the column answers is "what is the most this call ever gave you".
AI explanations
Each signal carries a one or two-sentence explanation in plain language ("9x volume vs the 24h baseline with three smart-money wallets buying, late-stage pump risk in play"). That sentence is generated by Anthropic's Claude (Sonnet 4.6) with the raw scoring payload as input.
The prompt is short and instructs the model to lead with what is happening on chain and avoid hype words. The same model handles every signal so the tone is consistent.
The web dashboard
Next.js 15, server-rendered. Pages call the engine API for fresh data on every request, with a short revalidation window so multiple loads in the same minute share cached enrichment. The signal cards, filters, sort options, and call history are all client components reading from server-rendered JSON.
The Telegram bot
A small Telegraf-based bot subscribes to two streams in the engine database: new threshold signals (gated by each subscriber's /threshold) and watchlist signals (every new signal for tokens on a subscriber's /watching list). Polls every 30 seconds. Both streams are deduped per subscriber so the same signal never fires twice.
What is not in this pipeline
The signal pipeline holds custody of nothing and runs no paid market-data feed. Everything you see is built on public on-chain data. The revenue model ties to holding $PRSM: it gates the opt-in auto-trade agent, and profit the agent generates is designed to route back to $PRSM holders, with the exact mechanism and rates published before any distribution.