Executive Summary

Generative AI is transforming how enterprises interact with knowledge. In the life sciences, the promise is enormous: faster drug development, sharper portfolio strategy, and more efficient evidence generation. But most current AI deployments are limited to retrieval-augmented generation (RAG) systems. These pair a large language model (LLM) with a document retriever, surfacing text passages in response to questions.

While RAG can be useful for simple search tasks, it is fundamentally insufficient for high-stakes scientific and regulatory decision-making. Scientific questions rarely live in text alone. They demand evidence that spans clinical trial registries, regulatory labels, publications, sequencing data, high-throughput screens, experimental readouts, Kaplan–Meier curves, tables, charts, imaging biomarkers and more.

Reliant AI takes a different path. We architected our platform for multi-modal integration and probabilistic reasoning by combining:

• Specialized multi-modal extraction models for text, tables, charts, images, and experimental outputs,
  
• A probabilistic knowledge graph (PKG) that captures structured, confidence-weighted relationships across modalities, and
  
• A decision intelligence layer that transforms this context into reliable, reusable, and actionable insights.

This structured approach transforms unstructured documents into a durable knowledge substrate. The result: decision systems, not just copilots, capable of powering high-stakes scientific and regulatory workflows with multi-modal evidence at their core.

The Limits of Generic RAG in Pharma

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RAG pipelines operate by breaking documents into chunks, embedding them into a vector database, retrieving nearest-neighbor passages, and passing those passages into a large language model. This approach has clear limitations in life sciences:

• Fragile grounding: Citations don’t equal correctness; RAG may present evidence that conflicts with other sources.
  
• Flat text context: Queries in pharma span multiple dimensions (drug, mechanism, indication, endpoint, geography). RAG treats all input as flat text, ignoring structure.
  
• Text-only blind spot: Tables, figures, survival curves, assay data, and imaging endpoints are excluded entirely.
  
• No cumulative knowledge: Each query starts from scratch; institutional and multi-modal knowledge do not persist.
  
• Hallucination risk: In regulated domains, even minor factual errors are unacceptable.
  

RAG is a search assistant. Pharma needs a structured, multi-modal reasoning system.

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Reliant AI’s Structured, Multi-Modal Approach

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1. Specialized Multi-Modal Extraction Models

• Domain-trained models parse trial registries, FDA/EMA labels, scientific literature, investor reports, sequencing datasets, assay readouts, Kaplan–Meier curves, and imaging data.
  
• They extract structured entities (drug, target, patient population, endpoint, geography) and relationships (e.g., “Trial X tested Drug Y vs. SoC in 2L NSCLC, OS endpoint, N=400”).
  
• Unlike general-purpose LLMs, these models are optimized for precision and recall in biomedical data.
  
  

2. Probabilistic Knowledge Graph (PKG)

• Extracted entities and relations are stored in a knowledge graph with confidence scores and provenance.
  
• Conflicting or uncertain evidence is preserved, not discarded, enabling nuanced reasoning.
  
• The PKG links across modalities—for example, connecting a biomarker signal from sequencing data to a trial arm outcome in a Kaplan–Meier curve and its subsequent regulatory label expansion.
  
  

3. Decision Intelligence Layer

• Builds on structured, multi-modal context rather than isolated text passages.
  
• Supports advanced reasoning: forecasting, scenario analysis, trial benchmarking, and predictive modeling that are impossible with RAG alone.
  
• Enables users to ask complex, multi-modal questions such as:
  “For PD-1 inhibitors in 1L NSCLC, what sequencing-defined subgroups show survival benefit, how do assay results align, and what does this imply for pipeline analogs?”

Example Workflows

Systematic Literature Reviews (SLR)

Traditional SLRs can take a year or more and cost upwards of $150K. Reliant automates search, screening, and extraction not only from text but also from tables, supplementary charts, and figures. The result: fully traceable, auditable reviews completed in weeks instead of months.

Standard of Care (SoC) Modeling

SoC evolves rapidly as trial data and label changes emerge. Reliant integrates registries, regulatory updates, publications, biomarker and sequencing results, imaging endpoints, and real-world evidence to dynamically map SoC evolution. Analysts can explore “what-if” scenarios, e.g.: “If biomarker-defined subgroups in Trial X show positive results, how does SoC shift in the next line of therapy?”

Trial Success Modeling

One of pharma’s most strategic questions is the likelihood that a pipeline asset will succeed. Reliant combines:

• Historical trial outcomes for analogs,
  
• Mechanism of action and target class data,
  
• Patient segmentation and biomarker/assay readouts,
  
• Imaging endpoints and sequencing results,
  
• Prior regulatory approvals and label expansions.
  

This multi-modal evidence feeds into the PKG and predictive models, producing probabilistic forecasts of phase transition and approval likelihood:

“Given analogs of Drug A, trial designs in Phase II, and historical benchmarks, what is the likelihood of success for Drug A advancing to Phase III or eventual approval?”

These insights provide BD&L, portfolio, and clinical strategy teams with quantitative evidence for go/no-go decisions — something RAG copilots cannot deliver.

Why This Matters for Pharma

Reliability & Traceability: Every data point—text, table, image, or numerical readout—is structured with provenance, ensuring auditability and reproducibility.

Scalability & Reuse: Knowledge compounds as more data and modalities are added; queries build on an expanding graph, not isolated documents.

Decision-Making Leverage: Structured knowledge enables forecasting, scenario modeling, and probabilistic reasoning — proper decision support rather than search assistance.

Future-Proofing: The PKG expands naturally as new data sources or extraction models are added. Reliant is not a point solution but a growing infrastructure.

Conclusion

RAG copilots are a useful starting point, but they cannot meet the complexity of pharma decision-making. Reliant AI is built for structured, probabilistic, and multi-modal intelligence.

By uniting specialized extraction models, a probabilistic knowledge graph, and a decision intelligence layer, Reliant enables pharma teams to move beyond search into true decision systems—reliable, reusable, and ready for the future of life sciences.

We invite you to explore co-development opportunities in your therapeutic areas—whether for SLR automation, SoC modeling, or trial success prediction.

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