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Pharma Evidence Intelligence Suite

Pharma Evidence Intelligence Suite uses advanced AI to discover, analyze, and synthesize clinical, real‑world, and regulatory evidence across the global literature and key data sources. It automatically surfaces relevant studies, extracts critical endpoints and safety signals, and generates traceable, regulator-ready insights to support drug development and medical affairs. This accelerates evidence generation, reduces manual review effort, and improves decision quality across the pharmaceutical R&D lifecycle.

The Problem

“Pharma and biotech teams struggle to keep up with the exploding volume of clinical and preclinical literature needed for evidence generation (e.g., for trial d…”

The Shift

Before AI~85% Manual

Human Does

  • •Define evidence questions and review scope for development, safety, or medical affairs needs
  • •Search literature and data sources manually, then collect and organize relevant studies
  • •Read full texts and abstracts to extract endpoints, comparators, outcomes, and safety findings
  • •Discuss findings across stakeholders and decide what evidence supports next actions

Automation

    With AI~75% Automated

    Human Does

    • •Set evidence objectives, inclusion criteria, and acceptable use boundaries for each review
    • •Validate high-impact findings, adjudicate ambiguous evidence, and handle exceptions
    • •Approve evidence summaries, safety interpretations, and regulator-facing conclusions

    AI Handles

    • •Continuously scan approved literature and data sources to identify relevant new evidence
    • •Rank, cluster, and triage studies by indication, population, endpoint, and relevance
    • •Extract key study details, endpoints, safety signals, and real-world outcomes into structured summaries
    • •Generate traceable evidence syntheses and draft regulator-ready insight packages with citations

    Operating Intelligence

    How Pharma Evidence Intelligence Suite runs once it is live

    Humans set constraints. AI generates options.

    Humans choose what moves forward.

    Selections improve future generation quality.

    Confidence89%
    ArchetypeGenerate & Evaluate
    Shape6-step branching
    Human gates2
    Autonomy
    50%AI controls 3 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapebranching

    Step 1

    Define Constraints

    Step 2

    Generate

    Step 3

    Evaluate

    Step 4

    Select & Refine

    Step 5

    Deliver

    Step 6

    Feedback

    AI lead

    Autonomous execution

    2AI
    3AI
    5AI
    gate
    gate

    Human lead

    Approval, override, feedback

    1Human
    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    Humans define the constraints. AI generates and evaluates options. Humans select what ships. Outcomes train the next generation cycle.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Technologies

    Technologies commonly used in Pharma Evidence Intelligence Suite implementations:

    Compression force measurementOther
    4 mentions
    Machine learning surrogate modelOther
    4 mentions
    NIR spectraOther
    4 mentions
    API content predictionOther
    3 mentions
    HPMC content predictionOther
    3 mentions
    +1 more technologies(sign up to see all)

    Key Players

    Companies actively working on Pharma Evidence Intelligence Suite solutions:

    Conventional wet-chemistry composition assaysStandalone chemometric NIR calibration models

    Real-World Use Cases

    NIR-based prediction of API and HPMC content for downstream dissolution modeling

    AI reads near-infrared spectra to estimate how much drug and polymer are in a tablet blend, then uses those estimates to help predict final tablet performance.

    spectral inference plus chained regressionproposed as part of a validated research workflow supporting dissolution rtrt; not presented as standalone production deployment.
    10.0

    Automated rule-level assessment of trial exclusion criteria impact

    Instead of treating all trial rules as equally important, AI checks each rule one by one to see which ones exclude lots of people without changing outcomes much.

    rule attribution and what-if analysisdemonstrated in published retrospective analyses of completed oncology trials; suitable as a protocol-design decision support workflow.
    10.0

    Explainable protocol editing support for trial design review

    After scoring a new trial protocol, the system points to the specific eligibility rules and design features that pushed the prediction toward failure or success, so clinicians can edit the protocol and try again.

    human-in-the-loop decision support with local explanationexperimental decision-support workflow demonstrated in the research pipeline with shap examples on unseen test cases; no evidence of routine operational deployment in the source summaries.
    10.0

    Centralized statistical monitoring to flag atypical trial sites early

    An AI/statistical system watches data coming from many clinical trial sites and warns when one site looks unusually different from the others, so teams can check for data quality problems sooner.

    anomaly detectionproposed and evaluated in simulation; suitable as a routine adjunct monitoring workflow rather than a standalone replacement for conventional monitoring.
    10.0

    AI-assisted generation of disclosure-ready RWE submission packages for FDA meetings

    Use AI to turn a sponsor’s study idea into a clean FDA meeting package that includes the right study details while separating what can and cannot be publicly disclosed.

    document intelligenceproposed workflow with strong feasibility because the fda specifies structured submission content and disclosure categories.
    10.0
    +5 more use cases(sign up to see all)

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