The Data & Intelligence Layer for Tokenized Markets.

Institutional-grade data, analytics and AI to monitor, understand and operate tokenized assets across onchain and global markets.

Talk to the CEO →

Onchain

IssuanceSupply TransfersHolders WalletsSettlement

Markets

PriceVolumeOrder books SpreadDepthLiquidity Venues
Sun Zu Intelligence Layer Data · Analytics · Monitoring
Sun Zu AI Institutional Market Intelligence
UnderstandMonitor ExecuteManage liquidity

A New Financial Data Layer

Financial assets are moving onchain.
Their intelligence infrastructure must follow.

  • IssuanceAssets are created, minted and redeemed on public networks, outside traditional market plumbing.
  • OwnershipHolders and wallets replace registries, and concentration becomes observable in real time.
  • MarketsThe same asset trades across exchanges, venues and onchain liquidity, each with its own microstructure.

Tokenized assets create a new financial data environment where traditional market data, blockchain activity, liquidity, ownership and transaction flows need to be understood together.

Market data providers stop at the venue. Blockchain data providers stop at the chain. An institution holding a tokenized asset needs both, reconciled, on the same asset.

The instrument changed.
The intelligence layer has not.

The Sun Zu Differentiator

One asset.
One intelligence layer.

From issuance to holder From order book to settlement From data to decision

Sun Zu Lab connects blockchain activity, market structure and asset-level data into a unified intelligence layer, giving institutions a complete view of how a tokenized asset behaves across markets and networks.

Platform Foundation

From fragmented data
to actionable intelligence.

Stage 01

Data Ingest & normalize

Market, onchain and token data normalized into one institutional-grade referential.

Market dataBlockchain dataToken data Historical dataOrder booksTransfers SupplyOwnership
Stage 02

Analytics Compute layer

Raw observations turned into market, liquidity and asset-level measurements.

PriceVolumeSpreadDepth VolatilityMarket qualityFlowsHolder analytics
Stage 03

Monitoring Continuous surveillance

Assets and markets watched continuously across venues and chains.

Liquidity monitoringMarket surveillance Risk signalsPrice dislocations Large transfersKPI monitoring Custom alerts
Stage 04

AI Institutional workflows

The interface to the layer beneath it: reasoning over the same normalized datasets.

InvestigateExplainCompare MonitorReportRecommend

The AI layer is only as good as the data beneath it.

Sun Zu AI operates on the same normalized market, onchain and historical intelligence layer used across the platform. No detached model layer, no unverifiable context.

Tokenized Asset Lifecycle

Understand the full lifecycle
of a tokenized asset.

From issuance and ownership to secondary-market liquidity, risk and reporting, Sun Zu connects every layer of a tokenized asset into one intelligence environment.

01

Issuance

  • Supply
  • Mint / Burn
  • Asset creation
02

Ownership

  • Holders
  • Wallets
  • Concentration
03

Transfers

  • Flows
  • Large movements
  • Wallet activity
04

Liquidity

  • Market makers
  • Venues
  • Order books
05

Trading

  • Price
  • Volume
  • Spread
  • Depth
06

Risk

  • Dislocations
  • Liquidity deterioration
  • Anomalies
07

Intelligence

  • AI analysis
  • Alerts
  • Reporting
  • Investigations

Tokenization creates the asset.
Intelligence makes it operable.

Issuance → Ownership → Transfers → Liquidity → Trading → Risk → Intelligence

Sun Zu AI

Institutional Market Intelligence.
Powered by proprietary market data.

One AI platform to understand markets, monitor risk, improve execution and manage liquidity across digital and tokenized assets.

AI is the interface.
Sun Zu’s data infrastructure is the foundation.

SUN ZU AIInstitutional Market Intelligence · four modules, one intelligence layer

AI Market Intelligence

Understand the market.

Understand an asset, market or event using live and historical data through natural language.

  • AI Market Research Analyst
  • AI Token Liquidity Analyst
  • AI Market Event Explainer
  • AI Historical Scenario Explorer
  • AI Pricing & Market Quality Analyst

Example question“Why did liquidity deteriorate over the last 12 hours, on which venues, and how does this compare with similar historical episodes?”

Question decompositionIllustrative analysis
Live dataCurrent venue quotes and depth
Historical dataComparable liquidity episodes
Market structureVenue distribution and quality
Onchain flowsTransfers around the window
Institutional answer

Deterioration concentrated on a subset of venues, consistent with reduced displayed depth and lower liquidity-provider participation, with a comparable pattern observed in prior episodes of similar market structure.

AI Monitoring & Surveillance

Know when something matters.

Continuously monitor markets, detect meaningful events and explain why they require attention.

  • AI Market Surveillance Analyst
  • AI Peg & Liquidity Monitor
  • AI Depeg Investigator
  • AI Risk & Market Monitoring Agent
  • AI Secondary Market Health Agent
  • AI Reference Asset Divergence Monitor

Product principleAn alert states that a threshold was crossed. An investigation states what happened, where, how large, why it likely happened and what to look at next.

Alert → investigationIllustrative analysis
⚠ PRICE > THRESHOLD Legacy monitoring
  • What happenedPricing deviation against the reference level
  • WhereConcentrated on a subset of venues
  • MagnitudeMaterial relative to the recent baseline
  • ContextDepth thinner than usual for the period
  • Likely explanationOrder book imbalance without matching onchain flow
  • Recommended investigationReview venue quoting and liquidity-provider presence

AI Execution Intelligence

Make better trading decisions.

Use live liquidity, historical market structure and cross-venue data to understand where, when and how to execute.

  • AI Execution Advisor
  • AI Pre / Post-Trade Analyst
  • AI Best Execution Intelligence
  • AI Treasury Execution Assistant
  • AI Venue Quality Analyst

Example question“I need to sell a large position over the next four hours. Compare available liquidity across venues and estimate market impact under different execution strategies.”

Decision support. Sun Zu does not execute trades.

Execution analysisIllustrative analysis
  • Order sizeLarge relative to displayed depth
  • Venue liquidityConcentrated on a small number of venues
  • Depth & spreadUneven across venues at benchmark levels
  • Historical impactComparable orders in similar conditions
Execution options compared
Strategy ASingle venue, immediate
Highest impact
Strategy BSplit across venues
Moderate
Strategy CScheduled over the window
Lowest

Relative comparison only. Illustrative, not a production result.

AI Liquidity Management

Measure and manage market quality.

Monitor, benchmark and investigate the performance of liquidity providers and market makers across venues.

  • AI Market Maker Supervisor
  • AI Liquidity Performance Analyst
  • AI SLA Breach Investigator
  • AI Venue Performance Benchmark
  • AI Liquidity Provider Allocation Agent
  • AI Client Reporting Agent

Example question“Which of our market makers performed best this week, who breached their obligations, where did liquidity deteriorate and what should we change?”

Liquidity provider reviewIllustrative analysis
Provider AQuoting quality
Within scope
Provider BQuoting quality
Within scope
Provider CQuoting quality
SLA breach
Root cause & recommendation Presence gaps concentrated on one venue during thinner market conditions, with wider quoted spreads at benchmark depth levels. Suggested next step: review obligations for that venue and reallocate expected depth.

Anonymized illustrative example. Not a production result.

SUN ZU INTELLIGENCE LAYER
ReferentialAnalyticsMonitoringHistorical
MARKET DATA + ONCHAIN DATA
VenuesOrder booksChainsTransfersHolders
UNDERSTAND

Markets, assets and events explained from the underlying data.

MONITOR

Continuous surveillance that turns alerts into investigations.

EXECUTE

Liquidity and impact intelligence before and after execution.

MANAGE

Market quality measured, benchmarked and reported.

Institutional Workflows

One intelligence platform.
Built around your market.

The same Sun Zu intelligence layer powers specialized workflows for different institutional users.

Institutional workflows for RWA Issuers

01 AI On/Off-Chain Liquidity Monitor Unify blockchain and venue data to understand liquidity across markets.
02 AI Pricing & Market Quality Analyst Monitor price, spreads, depth and market quality.
03 AI Reference Asset Divergence Monitor Detect and explain deviations between a tokenized asset and its reference asset or NAV.
04 AI Market Fragmentation Analyst Understand how liquidity is distributed across chains, exchanges, brokers and venues.
05 AI Secondary Market Health Agent Continuously assess the quality of an asset’s secondary market.
→ Another workflow in mind? The same intelligence layer can be shaped around your market and your obligations. Discuss your use case.

Institutional workflows for Stablecoin Issuers

01 AI Peg & Liquidity Monitor Monitor price, depth and dislocations across markets.
02 AI Depeg Investigator Investigate what caused a peg deviation.
03 AI Ecosystem Liquidity Analyst Understand liquidity fragmentation across CEX, DEX and chains.
04 AI Market Maker Supervisor Measure each market maker’s contribution to peg stability and liquidity.
05 AI Cross-Chain Liquidity Monitor Compare liquidity conditions across networks.
→ Another workflow in mind? The same intelligence layer can be shaped around your market and your obligations. Discuss your use case.

Institutional workflows for Token Issuers

01 AI Market Maker Supervisor Monitor contractual obligations and market quality.
02 AI Token Liquidity Analyst Understand liquidity deterioration.
03 AI Liquidity Reporting Agent Generate recurring executive reports.
04 AI Market Event Explainer Explain significant price and liquidity events.
05 AI Treasury Execution Assistant Assess liquidity and potential market impact before execution.
→ Another workflow in mind? The same intelligence layer can be shaped around your market and your obligations. Discuss your use case.

Institutional workflows for Exchanges

01 AI Market Surveillance Analyst Detect anomalies, price dislocations and liquidity deterioration.
02 AI Competitive Liquidity Monitor Benchmark spreads, depth and market quality.
03 AI Listing & Market Health Analyst Assess the health of listed markets.
04 AI Delisting Risk Monitor Identify assets where market quality is deteriorating materially.
05 AI Liquidity Provider Allocation Agent Compare liquidity-provider performance.
→ Another workflow in mind? The same intelligence layer can be shaped around your market and your obligations. Discuss your use case.

Institutional workflows for Asset Managers

01 AI Market Research Analyst Explore historical market regimes using natural language.
02 AI Execution Advisor Understand liquidity and execution alternatives.
03 AI Pre / Post-Trade Analyst Evaluate execution versus available market conditions.
04 AI Liquidity Regime Detector Detect changes in liquidity conditions.
05 AI Historical Scenario Explorer Find comparable historical market environments.
→ Another workflow in mind? The same intelligence layer can be shaped around your market and your obligations. Discuss your use case.

Institutional workflows for Banks and Brokers

01 AI Digital Asset Market Analyst Natural-language interface for digital and tokenized markets.
02 AI Best Execution Intelligence Compare liquidity conditions across venues.
03 AI Risk & Market Monitoring Agent Monitor liquidity events and abnormal conditions.
04 AI Counterparty Market Intelligence Analyze observed market quality across venues and counterparties.
05 AI Client Execution Review Generate institutional execution analysis.
→ Another workflow in mind? The same intelligence layer can be shaped around your market and your obligations. Discuss your use case.

Institutional workflows for Market Makers

01 AI Liquidity Performance Analyst Analyze quoting performance by asset, venue and client.
02 AI Inventory & Market Risk Assistant Identify market conditions where quoting risk increases.
03 AI Client Reporting Agent Automate reporting on service quality.
04 AI Quote Optimization Analyst Analyze where quoting parameters may need adjustment.
05 AI SLA Breach Investigator Reconstruct market context around SLA breaches.
→ Another workflow in mind? The same intelligence layer can be shaped around your market and your obligations. Discuss your use case.

Ask the Market

From question
to investigation.

Natural language becomes an interface to live, historical and onchain market intelligence.

Market data→ Signal→ Context→ Explanation→ Action

Every answer is structured as an institutional investigation: finding, drivers, supporting evidence and the next step to examine.

Illustrative analysis

Market investigation example

Question

Why did liquidity deteriorate over the last 12 hours?

Data sources
Market dataOrder booksHistorical contextOnchain flows

Sun Zu AI

Finding

Liquidity deterioration detected across multiple venues.

Drivers

  • Reduced displayed depth
  • Wider quoted spreads
  • Lower liquidity-provider participation
  • Correlated onchain movement

Evidence

Market dataOrder booksHistorical contextOnchain activity

Next investigationReview liquidity-provider activity and venue concentration.

Example output
Not a production result

Monitoring investigation example

Question

What caused this pricing anomaly and should we investigate?

Data sources
Cross-venue pricesDepth profileReference priceOnchain activity

Sun Zu AI

Finding

Pricing dislocation concentrated on a subset of venues.

Drivers

  • Venue-level depth reduction
  • Order book imbalance
  • Reference level stable elsewhere
  • No matching onchain flow

Evidence

Cross-venue priceDepthReference priceOnchain activity

Next investigationReconstruct the order book around the deviation window and check venue status.

Example output
Not a production result

Execution investigation example

Question

Where is the deepest available liquidity for this transaction?

Data sources
Venue liquidityOrder booksHistorical impactMarket structure

Sun Zu AI

Finding

Available liquidity is concentrated on a small number of venues.

Drivers

  • Uneven depth distribution
  • Spread differentials across venues
  • Historical impact profile
  • Time-of-day liquidity pattern

Evidence

Order booksVenue liquidityHistorical contextMarket structure

Next investigationCompare execution schedules against expected impact per venue.

Example output
Not a production result

Liquidity investigation example

Question

Which market maker underperformed this week and why?

Data sources
Quote historyDepth at levelsUptime windowsVenue comparison

Sun Zu AI

Finding

One liquidity provider fell below expected quoting quality.

Drivers

  • Reduced quoting presence
  • Wider quoted spreads
  • Thinner depth at benchmark levels
  • Venue-specific gaps

Evidence

Quote historyDepth at levelsHistorical contextVenue comparison

Next investigationReview obligations for the affected venue and period.

Example output
Not a production result

Asset Coverage

Workflows across every tokenized asset class.

StablecoinsLiquidity · Peg · Flows · Supply
Tokenized TreasuriesSupply · Holders · Transfers · Liquidity
Tokenized FundsOwnership · NAV · Flows · Secondary markets
Tokenized EquitiesPrice · Basis · Liquidity · Cross-market activity
Private CreditIssuance · Ownership · Lifecycle events · Risk
Digital AssetsLiquidity · Market structure · Venues · Risk

Agent-Native Infrastructure

Built for humans.
Designed for agents.

Sun Zu exposes structured market intelligence through human interfaces, APIs and agent-native tools, allowing AI systems to investigate tokenized markets using the same institutional intelligence layer.

One intelligence layer.Three interfaces.

SUN ZU INTELLIGENCE LAYER Market data · Onchain data · Referential · Analytics · Monitoring
Dashboard Institutional web interface for analysts, risk, treasury and market structure teams. Consumer: human
API Programmatic access to the same normalized intelligence for internal systems and pipelines. Consumer: system
Agent tools Agent-native capability model and MCP-ready interface design for agentic workflows. Consumer: AI agent

Agent capability model

Example agent capabilities · architectural model, not a published endpoint

get_asset_snapshot

Retrieve the current market and onchain context of an asset.

Input
asset · venues · networks
Output
market state · liquidity state · onchain state

analyze_liquidity

Analyze spread, depth, volume, fragmentation and market quality.

Input
asset · venues · window
Output
liquidity profile · market quality · fragmentation

investigate_market_event

Investigate an event using market, historical and onchain context.

Input
asset · event window
Output
finding · drivers · evidence

compare_venues

Compare liquidity and market quality across venues.

Input
asset · venue set · window
Output
venue ranking · spread and depth comparison

monitor_market_makers

Evaluate liquidity-provider performance against defined market-quality objectives.

Input
asset · providers · objectives
Output
performance view · breaches · root cause

generate_intelligence_report

Generate structured institutional market intelligence.

Input
asset · scope · period
Output
structured report · evidence trail

Query→ Retrieve→ Analyze→ Evidence→ Answer

An agent should never receive an opaque generated answer. Sun Zu is designed to return grounded intelligence: the finding, the analytics behind it and the market and blockchain observations used to produce it.

Integration architecture available for institutional deployments. Agent and API access is described here as an architecture, not as a public endpoint.

Provenance

  • AI / agent answer
  • Sun Zu intelligence
  • Analytics
  • Market / onchain observation
  • Source
Explore agent architecture →

Built for Institutions

Infrastructure designed for
financial-grade intelligence.

Sources
MarketsExchangesBlockchainsToken data
Data layer
IngestNormalizeReconcileStore
Intelligence
AnalyticsMonitoringRisk
Sun Zu AI
InvestigateExplainCompareReport
Access
DashboardAPIAgent tools

Intelligence without a black box.

Every Sun Zu insight can be traced back to the underlying market, venue, wallet or blockchain observations used to generate it. The same rule applies when the consumer is an AI agent rather than a person.

  • AI / agent answer
  • Sun Zu intelligence
  • Analytics
  • Market / onchain observation
  • Source
P / 01

Multi-Source

Market, exchange and blockchain data unified into one infrastructure.

P / 02

Real-Time

Continuous monitoring across assets, markets and networks.

P / 03

Historical

Institutional-grade historical datasets for research and analysis.

P / 04

Independent

Independent analytics across venues and liquidity providers.

P / 05

Auditable

Trace insights back to underlying market and blockchain observations.

P / 06

Programmable

APIs and AI-native access to the intelligence layer.

Built with institutions operating digital asset markets.

Get Started

Bring institutional intelligence to your tokenized markets.

Connect market data, onchain activity and AI through one institutional intelligence layer.

Talk to the CEO → Discuss Your Use Case

Explore agent architecture →