Case Study
Case Study
Case Study
Scientific Data Platform
The Platform for Discoverability, Governance & AI-Driven Research.
Year:
2025
Year:
2025
Year:
2025
Industry:
Healthcare & Life Sciences
Industry:
Healthcare & Life Sciences
Industry:
Healthcare & Life Sciences
Team:
28 people
Team:
28 people
Team:
28 people
Approach
Approach
Approach
The engagement began with a focused audit and discovery phase to assess architecture, user experience, and operational maturity. In parallel, we analyzed data catalogs, APIs, governance layers, compute environments, and lineage to uncover integration bottlenecks and scalability constraints. Through qualitative research—including interviews, contextual inquiry, and journey mapping—we refined personas, identified pain points, and exposed process gaps. These insights revealed friction across discovery, validation, and sharing stages, guiding the prioritization of AI use cases such as intelligent recommendations, metadata enrichment, conversational exploration, and automated governance checks. This combined analysis balanced user value, technical feasibility, and strategic impact—forming the foundation for the subsequent agentic architecture design.
The engagement began with a focused audit and discovery phase to assess architecture, user experience, and operational maturity. In parallel, we analyzed data catalogs, APIs, governance layers, compute environments, and lineage to uncover integration bottlenecks and scalability constraints. Through qualitative research—including interviews, contextual inquiry, and journey mapping—we refined personas, identified pain points, and exposed process gaps. These insights revealed friction across discovery, validation, and sharing stages, guiding the prioritization of AI use cases such as intelligent recommendations, metadata enrichment, conversational exploration, and automated governance checks. This combined analysis balanced user value, technical feasibility, and strategic impact—forming the foundation for the subsequent agentic architecture design.
The engagement began with a focused audit and discovery phase to assess architecture, user experience, and operational maturity. In parallel, we analyzed data catalogs, APIs, governance layers, compute environments, and lineage to uncover integration bottlenecks and scalability constraints. Through qualitative research—including interviews, contextual inquiry, and journey mapping—we refined personas, identified pain points, and exposed process gaps. These insights revealed friction across discovery, validation, and sharing stages, guiding the prioritization of AI use cases such as intelligent recommendations, metadata enrichment, conversational exploration, and automated governance checks. This combined analysis balanced user value, technical feasibility, and strategic impact—forming the foundation for the subsequent agentic architecture design.
Architecture
Architecture
Architecture
Building on these insights, we defined the Agentic Architecture for the Data Platform—an orchestration framework where specialized AI Agents collaborate to fulfill user intents. Each agent operates autonomously within its domain while remaining contextually aligned through a central orchestrator. This architecture introduced multi-layer reasoning, policy enforcement, and contextual memory—enabling the system to adapt dynamically to user behavior and data context. The orchestration ensured workflow continuity, minimized redundancy, and embedded governance-by-design across all AI interactions.
Building on these insights, we defined the Agentic Architecture for the Data Platform—an orchestration framework where specialized AI Agents collaborate to fulfill user intents. Each agent operates autonomously within its domain while remaining contextually aligned through a central orchestrator. This architecture introduced multi-layer reasoning, policy enforcement, and contextual memory—enabling the system to adapt dynamically to user behavior and data context. The orchestration ensured workflow continuity, minimized redundancy, and embedded governance-by-design across all AI interactions.
Building on these insights, we defined the Agentic Architecture for the Data Platform—an orchestration framework where specialized AI Agents collaborate to fulfill user intents. Each agent operates autonomously within its domain while remaining contextually aligned through a central orchestrator. This architecture introduced multi-layer reasoning, policy enforcement, and contextual memory—enabling the system to adapt dynamically to user behavior and data context. The orchestration ensured workflow continuity, minimized redundancy, and embedded governance-by-design across all AI interactions.
Outcome
Outcome
Outcome
The collaboration established a validated foundation for AI-driven discoverability, governance, and user experience within the Data Platform. It evolved into an intelligent ecosystem capable of delivering personalized interfaces, explainable automation, and self-optimizing research workflows. This transformation positioned the Data Platform as a pioneering model for data-intensive organizations—demonstrating how agentic systems can unify governance, AI, and experience into a single adaptive framework that learns and improves continuously.rfaces, explainable automation, and self-optimizing research workflows. This transformation positioned the Data Platform as a pioneering model for data-intensive organizations—demonstrating how agentic systems can unify governance, AI, and experience into a
The collaboration established a validated foundation for AI-driven discoverability, governance, and user experience within the Data Platform. It evolved into an intelligent ecosystem capable of delivering personalized interfaces, explainable automation, and self-optimizing research workflows. This transformation positioned the Data Platform as a pioneering model for data-intensive organizations—demonstrating how agentic systems can unify governance, AI, and experience into a single adaptive framework that learns and improves continuously.rfaces, explainable automation, and self-optimizing research workflows. This transformation positioned the Data Platform as a pioneering model for data-intensive organizations—demonstrating how agentic systems can unify governance, AI, and experience into a
The collaboration established a validated foundation for AI-driven discoverability, governance, and user experience within the Data Platform. It evolved into an intelligent ecosystem capable of delivering personalized interfaces, explainable automation, and self-optimizing research workflows. This transformation positioned the Data Platform as a pioneering model for data-intensive organizations—demonstrating how agentic systems can unify governance, AI, and experience into a single adaptive framework that learns and improves continuously.rfaces, explainable automation, and self-optimizing research workflows. This transformation positioned the Data Platform as a pioneering model for data-intensive organizations—demonstrating how agentic systems can unify governance, AI, and experience into a
FEEDBACK-TO-ITERATION TIME ↓
2–3W → 1–2D
FEEDBACK-TO-ITERATION TIME ↓
2–3W → 1–2D
FEEDBACK-TO-ITERATION TIME ↓
2–3W → 1–2D
CONTAINMENT WITH COMPREHENSION ↑
60% → 88%
CONTAINMENT WITH COMPREHENSION ↑
60% → 88%
CONTAINMENT WITH COMPREHENSION ↑
60% → 88%
MANUAL INTERVENTION RATE ↓
40% → 20%
MANUAL INTERVENTION RATE ↓
40% → 20%
MANUAL INTERVENTION RATE ↓
40% → 20%
TRUST RETENTION INDEX ↑
55% → 80%
TRUST RETENTION INDEX ↑
55% → 80%
TRUST RETENTION INDEX ↑
55% → 80%
Cross-Stage Explainability Alignment
+40% → 75%
Cross-Stage Explainability Alignment
+40% → 75%
Cross-Stage Explainability Alignment
+40% → 75%
hours/month saved
+15%
hours/month saved
+15%
hours/month saved
+15%
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Whether you have questions or just want to explore options, I'm here.