Powering Crypto Market Intelligence

Challenge

Fragmented digital asset data

An application that wants to answer "is this asset interesting right now?" needs market data, social activity, sentiment, news, narrative context and project fundamentals. Those six things arrive from different providers, with different identifiers, different field names, different units, different time bases and different update rhythms.

Building that integration inside the application is the default mistake. The reconciliation logic ends up scattered through feature code, every new surface re-solves the same identity problem slightly differently, and there is no single place to answer "how old is this number?".

Solution

Moonlytics data infrastructure

All of it moved below the application. Identity resolution, normalization, validation and storage happen once, in the layer, against one canonical asset registry. Moonboard reads a consistent model and spends its complexity budget on analysis and interface instead of on plumbing.

Processing

Normalization, enrichment, intelligence

Application

moonboard.ai

Each Moonboard surface maps onto a data category documented on this site. That mapping is the clearest available demonstration that the layer is real: every category has a consumer in production.

Result

A unified crypto market intelligence experience

What the separation actually buys, stated as facts about the system rather than as business metrics we have not measured:

• One asset identity across every surface. The asset in the scoreboard, the chart, the ecosystem view and the news feed is the same canonical record — not three lookups that agree most of the time.
• Market and social values share a timestamp exactly. Plotting attention against price is a straight read, with no interpolation between series sampled on different clocks.
• 6.7 million retained observations since November 2025 mean historical views and score backtests read stored history rather than recomputing it.
• New surfaces are additive. A new view is a new query against the canonical model, not a new provider integration.
• Data problems have one address. A wrong value is a layer question, not an application bug hunt across six features.

No revenue, engagement or conversion figures are claimed here. The measurable facts about this system are architectural, and those are what is listed.

Where the methodology lives

Moonboard's scoring formulas — how social momentum, liquidity, sentiment divergence and the composite rankings are computed — are documented on moonboard.io, the research and methodology domain. This site documents the data those formulas consume; it does not restate them.

That split is deliberate. One question is "what is this data and how good is it?" and the other is "what does this score mean?". They have different readers, and answering both in one place serves neither.

A note on independence

Moonboard is the largest consumer of this layer, not its only possible one. The canonical model is not shaped around Moonboard's features — it is shaped around what an asset observation is — which is what makes it usable by portfolio analytics, research and agent tooling without a rewrite.

Related

• The data categories Moonboard reads.
• The processing between provider and application.
• The canonical model it consumes.

Architecture and surface mapping verified against the production application on 2026-08-31.