Map data lineage
Show ingestion, transformation, storage, features, training, serving, and downstream use with ownership and governance in context.
- Batch and streaming pipelines
- Stores, catalogs, and feature flows
- Quality and ownership metadata
Data and AI solution
Show how the AI system actually works.From source data to human review.
Map data, models, retrieval, agents, evaluation, and human oversight in the same view. Link operating and governance decisions to the exact part of the system they affect.
- Data + ML vocabulary
- Six agentic patterns
- Evaluation context
- Linked governance
Data pipeline · governed flowSource-rendered starter
Rendered from the checked-in data pipeline starter used by the application.
InputProduct questionUser and policy context
RetrievalKnowledge indexGoverned sources
AgentReasoning loopTools · memory · limits
ControlHuman reviewEscalation and feedback
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Eval plan linkedQuality · safety · drift
Explain the complete intelligence loop
Architecture, evidence, and accountability in one view.
Turn a collection of models and services into an engineering system that product, data, security, and operations teams can inspect and improve.
Explain model and agent behavior
Model RAG, tool use, memory, orchestration, evaluation, guardrails, and human review without reducing the system to generic boxes.
- Agentic architecture patterns
- Model serving and evaluation
- Fallback and escalation paths
Link governance to implementation
Reference policies, evaluation criteria, risk decisions, and operating thresholds from the exact component or flow they govern.
- Bidirectional spec references
- Versioned team review
- Durable audit context
Data and AI in Struct
Trace data, model, agent, tool, and evaluation paths.
Represent runtime behavior and governance as engineering structure—not a generic flowchart—then link tests and human controls to the affected components.
- 01AI-native componentsAgents, LLMs, tools, skills, routers, memory, evaluators, guardrails, and human approval points.
- 02Six agentic patternsChecked-in starters for common orchestration and decision-loop structures.
- 03Data-system vocabularySources, ingestion, transformation, stores, feature and vector systems, serving, and monitoring.
- 04Linked evaluation contextAttach acceptance criteria, evidence, risk controls, ownership, and escalation requirements.
Responsible system design
Trace the path from source data to user outcome.
Make the entire lifecycle visible so teams can reason about performance, safety, cost, and ownership before production surprises.
Frame the outcome and boundaries
Define users, decisions, sensitive data, external dependencies, and human responsibilities.
Model the data and intelligence path
Connect sources, retrieval, models, agents, tools, outputs, feedback, and monitoring.
Attach evaluation and governance
Link acceptance criteria, risk controls, test evidence, and escalation policy to the implementation.
Review across the organization
Give data, product, security, legal, and operations specialists one shared system record.