> For the complete documentation index, see [llms.txt](https://docs.visionmakers.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.visionmakers.io/whitepaper/intelligence.md).

# 6 · Intelligence and data

Mindshare scoring, agent orchestration and how answers are grounded

This is the layer that holds an opinion. Everything else moves data or executes instructions.

## Mindshare

Mindshare measures where collective attention is concentrating and how fast. It is the
foundational signal, because in this market **attention reliably leads liquidity**.

Three inputs combine:

- **Social velocity**: rate of change in mentions, weighted by each source's historical accuracy rather than its audience size.
- **On-chain adoption**: unique holders, retention across epochs, direction of net flow.
- **Behavioural analytics**: what users do after encountering a token, not what they say about it.

> **FIG-04**: Three weighted inputs become one ranked score; attention moves before price.

## From question to signature

1. **Parse**: resolve to intent plus parameters. *"Buy 5 SOL if it drops under $120"* yields intent `conditional_order`, asset `SOL`, size `5`, trigger `< 120 USD`.
2. **Ground**: check against live state: does the asset exist on a supported network, is there routable liquidity, is the balance sufficient.
3. **Reason**: form a judgement and assemble the evidence for it.
4. **Explain**: render at the user's demonstrated level.
5. **Offer**: present the action as one confirmable control.
6. **Sign**: the user approves. Only then does anything execute.

## Grounding, and what it rules out

The platform does not answer from model memory. Every factual claim in an answer traces to a
record in the intelligence core with lineage attached. Where data is missing or stale the answer
says so rather than interpolating.

This constrains the product in a useful way: the assistant cannot speculate about an asset it has
no data on, and it cannot produce a confident-sounding answer from nothing. The failure mode of a
grounded system is *"I don't have that"*, which is recoverable. The failure mode of an ungrounded
one is a plausible fabrication, which is not.

## Risk interpretation

Risk is assessed continuously per token across liquidity depth and stability, holder
concentration, contract-level flags such as mint authority and transfer restrictions, deployer
behaviour, and flow anomalies.

The result is a judgement **with its components exposed**, so a user can disagree with the
conclusion while still seeing the inputs. The platform deliberately does **not** publish a single
opaque safety score: a number that cannot be interrogated is not interpretation, it is just
another datum to be taken on trust.

## Agents

Agents are scheduled or triggered workflows that chain the above. A daily brief runs discovery,
sentiment and smart-money passes and delivers a summary to Telegram at a set time. A monitoring
agent watches tracked wallets against size and market-cap rules and streams matches.

Every agent run is **logged against the user's account**: what ran, what it found, what it
pre-staged and what the user signed. See [section 9](/whitepaper/security).

## Data governance

User data handling, retention and rights are set out in the [Privacy Policy](/legal/privacy) and
[Sub-processors](/legal/sub-processors) list. The transparency obligations that attach to
operating AI in a financial context for EU users are addressed in
[AI Transparency](/legal/ai-transparency).