From Raw Scientific Data to AI-Ready Assets
Stop wrestling with messy data. Excelra’s automated curation pipelines convert unstructured publications, trials, and regulatory documents into clean, connected datasets that power your ML models and GenAI applications.
The “Data Gap” in Life Sciences
Your organization is drowning in valuable but unusable data. Most remains:
- Unstructured: Trapped in PDFs, forms, and tables.
- Fragmented: Inconsistent, duplicated, or incomplete across systems.
- Inaccessible: Difficult to feed into modern analytics, ML models, and RAG/GenAI.
Data scientists spend 80% of their time on data preparation instead of analysis. ML models underperform due to poor input quality. GenAI hallucinations stem from incomplete context. Strategic insights remain buried in inaccessible documents.
You can’t build intelligent systems on broken data foundations.
Excelra closes this gap. We combine deep domain expertise with advanced AI/ML to turn messy data into high-quality, structured, and linkable assets.
Excelra’s Automated Data Curation Solutions
We build end-to-end curation pipelines that intelligently read complex content, extract key entities, apply rigorous quality standards, and deliver outputs ready for immediate use in dashboards, ML models, and RAG-powered GenAI.
What Makes Our Curation Different
Domain Intelligence Built In
Our curation engines understand life sciences context—not just generic text patterns. We know the difference between a clinical endpoint and a business objective, between a molecular target and a sales target.
AI + Human Expertise
We blend automated extraction with configurable human-in-the-loop workflows. Subject-matter experts review, correct, and approve AI suggestions—creating feedback loops that continuously improve accuracy.
Purpose-Built for AI
Curated outputs are designed from day one to feed ML models, GenAI systems, and advanced analytics—eliminating the friction between data preparation and AI deployment.
Enterprise-Grade Quality
Every pipeline includes validation rules, quality checks, complete traceability, and audit trails—meeting the standards that regulated industries demand.
Explainability & Trust
Every model includes built-in explainable AI techniques, validation reports, and governance workflows designed for regulated environments where transparency isn’t optional.
Automated Curation Solution Accelerators
Tailored pipelines deployed as a service or platform module.
Publication & Evidence Curation
Transform scientific literature into structured evidence that accelerates discovery and competitive intelligence.
Key Capabilities:
- Automated extraction of targets, diseases, interventions, and outcomes
- Study design classification and key results structuring
- Entity normalization to standard ontologies
- Structured evidence tables ready for analysis
- Citation and provenance tracking
Clinical Trial Landscape Curation
Create unified, queryable views of the clinical trial landscape from fragmented public and internal sources.
Key Capabilities:
- Harmonization across ClinicalTrials.gov, EudraCT, and internal registries
- Normalization of sponsors, sites, indications, and endpoints
- Trial status tracking and timeline extraction
- Competitive positioning and gap analysis
- Competitive positioning and gap analysis
Safety & Regulatory Data Curation
Extract intelligence from regulatory documents and safety reports to accelerate signal detection and compliance.
Key Capabilities:
- Structured extraction from labels, PSURs, DSURs, and safety narratives
- Adverse event normalization and harmonization
- Indication, population, and dosing regimen standardization
- Warning and precaution extraction
- Regulatory variation tracking
Real-World Data & Operational Curation
Clean and standardize operational data from RWD sources, registries, and internal systems for reliable analytics.
Key Capabilities:
- Multi-source data integration and deduplication
- Patient, site, study, and product entity linkage
- Business rule application and data quality validation
- Temporal tracking and historical versioning
- Master data management for key entities
Knowledge Graph & Ontology-Enriched Curation
Build comprehensive knowledge graphs that connect drugs, targets, diseases, trials, and outcomes for advanced AI applications.
Key Capabilities:
- Entity-centric relationship mapping
- Integration with curated ontologies and knowledge bases
- Multi-hop relationship discovery
- Semantic enrichment for improved search and retrieval
- Feature engineering support for ML pipelines
End-to-End Curation Platform Architecture
Multi-Source Ingestion
- Comprehensive Connectivity- Seamless integration with document repositories, SharePoint, data lakes, APIs, public registries, and internal databases. Support for all content types—structured tables, semi-structured forms, and unstructured documents.
- Advanced Processing- OCR for scanned PDFs, table extraction from complex layouts, form recognition, and multi-language document processing.
Workflow & Human Review
- Configurable Review Queues- Smart routing of extraction results to appropriate subject-matter experts based on confidence scores and domain area.
- Collaborative Interfaces- Intuitive UIs for curation scientists to review, correct, approve, and provide feedback on automated extractions.
- Quality Metrics- Real-time dashboards tracking extraction accuracy, review throughput, inter-rater agreement, and pipeline performance.
AI + Rules Hybrid Engine
- Intelligent Extraction- State-of-the-art NLP, machine learning, and GenAI models for entity extraction, relationship identification, and text classification.
- Deterministic Precision- Rules-based validation and domain logic ensure high precision in regulated contexts where errors have consequences.
- Continuous Learning- Models improve over time through feedback from human reviewers and validation against ground truth.
Data Delivery & Integration
- Flexible Output Formats- Curated data delivered as REST APIs, database tables, data marts, CSV/Parquet files, or direct integration with your platforms.
- AI-Ready Structures- Schemas optimized for ML feature engineering, RAG indexing, graph databases, and analytical queries.
- Continuous Updates- Automated pipelines that refresh curated data as new sources become available or existing data changes.
From Raw Data to AI-Ready Assets: Our Methodology
Discover & Scope
Identify priority curation domains—publications, trials, labels, safety reports, or RWD. Assess current data sources, formats, quality issues, and curation bottlenecks.
Design the Curation Blueprint
Define target schemas, entity models, ontology mappings, and quality rules. Select an optimal mix of AI models, deterministic rules, and human review workflows.
Build & Pilot the Pipeline
Implement ingestion connectors, extraction models, normalization logic, and review interfaces. Execute a pilot on a representative dataset with SME feedback and iteration.
Industrialize & Integrate
Scale pipelines to full data volumes and additional sources. Integrate curated outputs with data lakes, warehouses, ML platforms, and GenAI systems.
Operate & Evolve
Establish continuous monitoring, quality reporting, and model improvement cycles. Extend to new therapeutic areas, use cases, and data domains as needs expand.
Use Cases Transforming Life Sciences Operations
Discovery & Pre-Clinical
Evidence landscaping automation | MOA and pathway curation | Competitive target intelligence
Clinical Development
Trial benchmarking datasets | Protocol comparison libraries | Historical study reuse
Regulatory & Safety
Signal detection data preparation | Label change tracking | Regulatory intelligence feeds
Medical Affairs & Commercial
Evidence library automation | Competitive landscape curation | Medical information databases
Why Industry Leaders Choose Excelra Curation
Life Sciences Focus
Built on decades of scientific curation, not generic data processing.
Unified Data & AI Expertise
Combines curated datasets, domain specialists, and AI/ML engineering for scalable solutions.
Human-Assisted AI
Curated data powers ML, GenAI/RAG, and advanced analytics with minimal preparation effort.
Enterprise-Ready Platform
Secure, compliant architecture with auditability and seamless integration across major cloud platforms.
Knowledge hub
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Ready to Build Your AI-Ready Data Foundation?
Great AI requires great data. Generic data quality tools weren’t built for the complexity of life sciences—but Excelra’s automated curation was.
Transform fragmented scientific and clinical information into reliable, structured, reusable assets that unlock the full potential of your analytics, machine learning, and GenAI investments.
In a focused discovery session, we’ll:
- Assess your most critical data curation challenges
- Demonstrate relevant curation accelerators and capabilities
- Review sample outputs and quality metrics
- Discuss integration with your existing data and AI platforms
- Map a clear implementation roadmap from pilot to production
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