Spend Less Time Searching for Evidence and More Time Advancing Science

Researchers and clinicians get faster access to critical evidence and AI-enabled answers they can trust.

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When Scientific Context Is Fragmented, AI Can't Be Trusted

Life science teams do not struggle because they lack data. They struggle because critical research and clinical and regulatory knowledge sit across disconnected systems, legacy repositories and spreadsheets. When that context is fragmented, AI doesn’t just slow teams down.

It produces results that are difficult to validate, explain or defend during regulatory review. Scientists spend more time hunting for and verifying evidence than focusing on science initiatives. AI helps this, but only when it is grounded in trusted scientific content with clear lineage and domain-specific meaning.

The Progress^® Data Platform helps life science organizations connect scattered knowledge, apply shared context and put governed AI into real research and decision workflows, so evidence moves faster and defensibly.

"Developing a new drug takes over a decade and costs an average of $2.67 billion per asset."

Deloitte, Measuring the Return from Pharmaceutical Innovation, 2025

Why Companies Trust the Progress
Data Platform

Proof That Science Teams Move Faster with Trusted Context

Success Story

Healthcare/Life Sciences

Pharmaceutical Leader Transforms R&D Search Powered by Progress Data Platform RAG Capabilities and Gen AI

73% more correct answers
41% reduction in poor-quality answers
85% relevance score

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Success Story

Healthcare/Life Sciences

Syngenta Accelerates Agricultural Innovation with Progress-Powered Enterprise AI Search Platform

40% reduction in search time
Faster, more informed R&D decisions
Previously 'lost' research made discoverable

Read Story

Validated by the Experts, Leading with Outcomes

Progress Named Exemplary in the 2026
ISG Buyers Guide for Data Platforms

Progress Data Platform Ranked #1 and Named a Champion in Metadata Management

Who This Is For

You need faster access to prior findings, publications and internal know-how so teams spend more time on science and less on search.

Trusted Knowledge Discovery

Research and discovery leaders in life sciences face a persistent challenge: critical scientific knowledge is spread across document management systems, legacy databases, electronic lab notebooks and internal publications, making it difficult for teams to find and build on prior work. Scientists routinely spend more time searching for evidence than analyzing it, and when they cannot locate relevant findings fast enough, they risk duplicating experiments or missing connections that could accelerate a program. A governed data foundation changes this by connecting internal research knowledge and making it semantically discoverable, so teams can validate hypotheses against what the organization already knows. As AI becomes part of the research workflow, it must be grounded in trusted, organization-specific scientific content, not general-purpose models that lack the domain specificity regulatory reviewers expect.

You need clearer visibility across study documents, evidence and workflows without adding another disconnected system.

Connected Content & Context

Clinical operations leaders manage study timelines, site performance and evidence workflows across increasingly complex trial portfolios. The challenge is rarely a lack of systems; it is the lack of meaningful connection between them. Study documents, protocol amendments, monitoring reports, safety signals and site-level data live in separate tools, and getting a unified view typically means manual reconciliation across teams and formats. A connected data foundation brings clinical content and operational data together in a single, queryable layer, giving teams real-time visibility across studies without adding another reporting tool to the stack. When AI is applied to clinical operations, it needs to surface relevant study context from governed sources, not approximate answers from incomplete or disconnected inputs.

You need AI outputs that trace back to source evidence and stand up to regulatory review.

Governed & Explainable AI

Regulatory and quality leaders carry the burden of making sure every submission, response and organizational decision can withstand external scrutiny. That requires clear, traceable lineage from any conclusion back to the source evidence that supports it, and it requires AI outputs that hold up under regulatory questioning. Today, assembling that evidence trail is still largely manual, with source material scattered across authoring tools, submission archives and quality management systems. A governed data platform preserves context, lineage and provenance across research and quality workflows, so when a regulatory reviewer asks why a decision was made, the supporting evidence is accessible, auditable and tied directly to its source, not reconstructed after the fact.

You need scalable data foundation that works with existing systems while improving meaning, reuse and control.

Shared Semantic Context

Chief data and digital leaders in life sciences are tasked with building the data infrastructure that research, clinical and regulatory teams depend on, while navigating legacy system constraints, competing vendor ecosystems and accelerating AI ambitions across the organization. The core challenge is not AI adoption itself. It enables AI that has a trustworthy, governed data foundation to work from. Most enterprise scientific data sits in siloed repositories with inconsistent schemas, limited shared meaning and minimal governance. A scalable data platform addresses this by unifying structured and unstructured scientific data, applying semantic context across it and supporting governed AI use cases, all without requiring a disruptive, multi-year migration that takes teams away from the work they are already doing.

What Life Science Teams Can Do with the Progress Data Platform

Scientific Answers Reusable Knowledge Defensible Evidence Research, Quality and Regulatory

Move From Search to Scientific Answers

Helps researchers reach relevant evidence, prior experiments and supporting context faster, across disconnected repositories.

How It Works

Connects structured and unstructured scientific content, adds shared meaning and grounds AI responses in authoritative sources.

Explore

73% more correct answers and 41% reduction in poor-quality answers reported by pharma customer

Turn Legacy Research into Reusable Knowledge

Brings historical R&D into current workflows so teams build on what the organization already knows, instead of rediscovering it.

How It Works

Makes archived content findable, reusable and interpretable across repositories and formats.

Explore

RWE catalog built in 6 months vs. industry average of 18+ months for comparable initiatives

Put Defensible Evidence Behind Every Answer

Gives teams answers they can review, cite and defend in regulatory and clinical settings, not probabilistic guesses.

How It Works

Preserves context, lineage and governance across research workflows so outputs stay tied to source material.

Explore

Fortune 50 pharma replaced manual evidence catalog with governed, searchable RWE platform spanning millions of patient records

Align Research, Quality & Regulatory on One Picture

Connects research, quality and regulatory knowledge so teams act on the same trusted view, not competing versions of the truth.

How It Works

Applies shared context across content and data so teams are not working from isolated versions of the truth.

Explore

Faster signal detection through semantic cross-source correlation vs. manual keyword review

How the Progress Data Platform Delivers for Pharma & Life Sciences

Provides life science organizations a governed data foundation for AI, analytics and knowledge discovery. It connects enterprise data, applies shared context and governance and delivers trusted information into research, clinical and regulatory workflows, working alongside the systems teams depend on today.