Pharma has outgrown linear pipelines. Nearly 90% of trials still fail, not for lack of science, but because decisions cannot keep pace with complexity. Evidence now evolves in motion, choices compound across functions, and rigor must extend beyond experiments into how decisions are framed, tested, and scaled.
Mu Sigma operates at the same level of discipline as the industry, building systems that convert uncertainty into advantage across discovery, development, and real-world evidence.
We formalize scientific intent before analysis begins. Ontologies and knowledge graphs encode domain logic with precision, removing ambiguity from endpoints, signals, and assumptions so reasoning remains consistent across assets, teams, and therapeutic areas.
Akashic Architecture preserves the relationships between questions, evidence, and methods as programs evolve. Teams move from exploration to execution without losing scientific nuance, even as data sources expand and regulatory expectations shift.
Coordinated AI agents plan, validate, and execute analytical workflows under explicit quality controls. Outputs remain explainable, traceable, and inspection-ready, matching the standards expected in clinical, regulatory, and safety environments.
Pharma lives on precision, reproducibility, and traceable logic. Our Art of Problem Solving frameworks apply the same discipline used in protocol design and statistical analysis to decision design itself. Every model, workflow, and recommendation is built to stand up to regulatory review and scientific challenge.
AI fails when meaning breaks. We build ontologies, knowledge graphs, and question networks that encode scientific intent directly into systems. Shared definitions and explicit relationships create consistency across assets, trials, therapeutic areas, and teams, eliminating ambiguity before it turns into risk.
High-stakes environments demand more than speed. Our agentic systems plan, validate, and execute analytical logic through coordinated AI roles with embedded quality checks. Outputs remain explainable, auditable, and inspection-ready across clinical development, real-world evidence, safety, and regulatory workflows.
Execution optimizes what is known. Exploration prepares for what changes. We design environments where teams can test hypotheses quickly, respond when evidence shifts, and build strategic optionality into portfolios. That capability shortens feedback loops and strengthens decisions across research and development.
Complexity compounds when learning does not. Our Continuous Service as a Software model fuses expert judgment, scalable platforms, and feedback-driven systems into a single operating engine. Teams ask better questions, reduce noise, reuse knowledge, and lower the cost of insight across programs.
A Fortune 500 pharma company replaced weeks of manual clinical concept mapping with automated, standards-aligned workflows. Mu Sigma compressed cohort creation from months to minutes, cutting error risk while unlocking scalable reuse across R&D programs.
reduction in concept set creation time
45 minutes per cohort
scalability across clinical phenotypes
Mu Sigma enabled cross-language scientific discovery by automating Key Opinion Leader identification across English and Japanese literature. AI-driven semantic search eliminated translation bottlenecks, accelerating insight flow and expanding global research reach.
increase in identified Key Opinion Leaders
reduction in manual search effort
To accelerate RSV vaccine approval, Mu Sigma applied prescriptive modeling to refine cohorts, surface hidden risk factors, and optimize trial execution. The result was faster insights, stronger evidence, and reduced time to regulatory confidence.
expansion in eligible patient cohorts
faster insight generation
high-density trial sites identified
The drug development pipeline is a hyper connected environment where scientific choices, operational constraints, and commercial pressures collide. Mu Sigma provides a semantic foundation supported by ontologies, knowledge graphs, and question networks so every decision point is grounded in logic that can scale. Our agentic AI systems accelerate planning, analysis, and execution across R&D and real world research, giving pharma teams a way to reason clearly in environments that evolve faster than traditional models can handle.
Below are our core capabilities across the drug development life cycle.
Understanding the true burden of disease is critical to shaping the future of healthcare solutions. Mu Sigma helps pharma companies assess disease prevalence, patient demographics, and healthcare utilization patterns to uncover gaps in treatment. By harnessing real-world evidence (RWE), we provide precise, data-driven insights into patient experiences, conditions, and care gaps, ensuring that research and development efforts are aligned with the most pressing healthcare challenges.
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