Stage 01
Data Ingest & normalize
Market, onchain and token data normalized into one institutional-grade referential.
Market dataBlockchain dataToken data
Historical dataOrder booksTransfers
SupplyOwnership
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.
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 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.
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.
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
Institutional workflows for Stablecoin Issuers
Institutional workflows for Token Issuers
Institutional workflows for Exchanges
Institutional workflows for Asset Managers
Institutional workflows for Banks and Brokers
Institutional workflows for Market Makers
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.
Market investigation example
Question
Why did liquidity deteriorate over the last 12 hours?
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?
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?
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?
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
Agent capability model
Example agent capabilities · architectural model, not a published endpointget_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
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
Multi-Source
Market, exchange and blockchain data unified into one infrastructure.
Real-Time
Continuous monitoring across assets, markets and networks.
Historical
Institutional-grade historical datasets for research and analysis.
Independent
Independent analytics across venues and liquidity providers.
Auditable
Trace insights back to underlying market and blockchain observations.
Programmable
APIs and AI-native access to the intelligence layer.
Get Started
Bring institutional intelligence to your tokenized markets.
Connect market data, onchain activity and AI through one institutional intelligence layer.