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Study Intelligence Agentic AI RAG + Tool Mesh

Crown Study Intelligence Agent Suite

Building on the Agentic AI concept page, the Study Intelligence Agent (SIA) connects every study milestone with autonomous reasoning, governed data access, and proactive decision support. The suite stands on the same look-and-feel as the concept blueprint while diving into the protocol, execution, reporting, and QA scenarios that sponsors ask for most.

Explore Scenarios View Architecture

Why “Study Intelligence Agent”?

Combines the clarity of “Study Agent” with an intelligence-first positioning that resonates with scientific, operations, and QA leaders.

Protocol Composer

Drafts protocols from quote context plus historical wins and therapeutic models.

Execution Conductor

Activates schedules, model requests, inventory, and risk alerts once a protocol locks.

Insight Author

Turns curated study data into sponsor-ready reports with citations.

QA & Compliance Sentinel

Checks every step against SOPs, audits, and regional regulations.

From Quote to Submission Under One Agentic Canopy

Inspired by the original concept page and the CrownBio WeChat architecture reference, this page zooms in on study execution. Each scenario inherits the LangChain-style orchestration, secure RAG fabric, and AI service mesh while clarifying domain objectives, inner workings, and measurable benefits.

4

Study scenarios orchestrated end-to-end with cross-pod memory.

8+

Toolchains per scenario, combining LIMS, CRM, ELN, and GenAI services.

3

Governance layers: access policy, QA lineage, and human oversight.

Scenario Deep Dive

Protocol Composer

Purpose Quote → Protocol

Generates a protocol draft from quote context, historical studies, and therapeutic intelligence, looping Study Directors for quick approvals.

How it works

  • Retrieves prior protocols, SOP clauses, and biomarkers using attribute RAG.
  • Auto-populates study design blocks, visit schedules, and assay panels.
  • Captures reviewer edits to fine-tune prompts and guardrails.

Technical frame

  • Structured memory from CRM, LIMS, and submission libraries.
  • LangChain graph nodes for drafting, critique, and compliance check.
  • Co-authoring UI embedded in Teams/Confluence.

Benefits

  • 50% faster protocol approval loops.
  • Traceable use of precedent studies.
  • Built-in compliance commentary for QA.

Protocol Execution Conductor

Study schedule Task graph

Once protocols lock, the conductor generates study schedules, allocates models, and manages tasks, alerts, and telemetry.

How it works

  • Builds a multi-week orchestration plan with dependencies.
  • Pushes tasks to Agent Force, LIMS, Jira, or ELN work queues.
  • Streams sensor and assay data for predictive risk scoring.

Technical frame

  • Event-driven architecture with Kafka/Service Bus.
  • Agent graph nodes for scheduling, resource checks, and mitigation.
  • Digital twin of facilities for capacity simulations.

Benefits

  • Automatic SLA tracking with proactive alerts.
  • Reduced overbooking or idle resources.
  • Faster escalation routing to Study Directors.

Report Generation Companion

Narratives Sponsor ready

Transforms curated data, images, and annotations into sponsor-ready reports with citations, charts, and regulatory language.

How it works

  • Connects to study data marts, imaging archives, and QA notes.
  • Drafts executive summaries, methodology, and result narratives.
  • Packages annexes for different agencies (FDA, CFDA, PMDA).

Technical frame

  • Template-aware LLM chains with parameterized sections.
  • Visualization microservices for charts and histology panels.
  • Version control plus e-signature hooks.

Benefits

  • Days instead of weeks to publish sponsor deliverables.
  • Audit-ready citations and lineage tags.
  • Consistent branding across studies.

Study QA & Compliance Sentinel

Governance Risk radar

Guards every milestone with SOP validation, deviation detection, and readiness evidence for audits or submissions.

How it works

  • Cross-checks actions against SOPs, WI, and CAPA records.
  • Flags missing documentation, training, or assay certifications.
  • Scores compliance risk and suggests mitigations.

Technical frame

  • Graph reasoning over SOP knowledge bases.
  • Policy engines for role, geography, and sponsor constraints.
  • Immutable audit log plus eDiscovery export.

Benefits

  • Continuous inspection instead of batch audits.
  • Regulator-ready evidence packages.
  • Lower deviation-related study costs.

Dynamic Architecture Reference

The layout mirrors the Concept page visuals and extends the WeChat architecture (knowledge fabric + service mesh + orchestration brain). Select a scenario to highlight which layers are activated end-to-end.

Unified overview of personas, agent reasoning, scenario pods, and data backbone.

Persona Workspaces Study Directors • QA • PM Teams/CRM/Portal surfaces Study Agent Reasoning Core LangChain graph • Guardrails • Memory Planner-executor • Feedback loops Policy & Monitoring Brain SOP graph • Observability Risk scoring • Overrides Protocol Pod Composer • Reviewer loop SOP alignment Execution Pod Task graph • Agent Force IoT telemetry Reporting Pod Narratives • Visualization Sponsor portals QA Pod SOP checks Audit kit Knowledge Fabric SOPs • Protocols • Literature Operational Spine LIMS • ELN • MES • ERP Analytics & Compliance Vault Data marts • QA evidence Partner Mesh MRS-AI • Agent Force

Benefits Across Stakeholders

Study Directors

Faster protocol turnaround, single source of truth for tasks, and automated risk cues reduce cognitive load.

QA & Regulatory

Continuous monitoring with lineage tagging mirrors the reference architecture highlighted in the WeChat article, easing inspections.

Sponsor Experience

Reports, dashboards, and communications share one narrative with tracked acknowledgments and alerts.

Operations

Digital twin planning, dynamic resource allocation, and IoT integration minimize delays and rework.