When Reading Becomes the Bottleneck
For one of the world’s top biopharmaceutical research leaders, the problem wasn’t imagination — it was information. Every new molecule, clinical trial or protocol design needed to be grounded in existing scientific research. For a biotech powerhouse working at the cutting edge, grounding innovation in biomedical literature wasn’t optional — it was survival.
Yet the deluge of data made this nearly impossible to manage. A single protein query on PubMed could generate 500–600 documents, each stretching 10–50 pages. Researchers lost 10–12 working days every month just to sift, filter and annotate 10–12 usable references. Highly trained scientists were spending more time reading and sorting than innovating or experimenting.
The result was a strategic slowdown. Innovation pipelines faltered, operational costs rose and teams faced a choice between burnout or expanding headcount for low-value tasks. Neither option was sustainable in a pharmaceutical race where time-to-discovery is time-to-market.
When scientists spend half their time sorting papers instead of solving problems, discovery doesn’t just slow — it risks stalling altogether.
Enter AccelProSearch: From Noise to Knowledge
Persistent partnered with the client to co-create AccelProSearch — a domain-specific GenAI and ML-powered assistant designed exclusively for biomedical research. Unlike generic chatbots, AccelProSearch was built to work like a research co-pilot, capable of scanning millions of papers, clustering them into themes and generating human-like summaries in minutes.
The architecture rested on AWS Cloud with Python APIs and Bedrock LLM embeddings, combining retrieval, topic modeling, clustering, summarization and Q&A into a single workflow. Scientists could move seamlessly from raw data to actionable insight — without leaving the platform.
Speed is not just a metric in pharma — it’s the difference between leading the market and lagging behind.
Smarter Searches, Sharper Science
AccelProSearch redefined the researcher’s workflow through a set of integrated capabilities:
- PubMed at Scale: Direct connections pulled millions of articles instantly, filterable by keywords, proteins, journals, or authors.
- Topic Modeling and Clustering: Thousands of documents were automatically grouped under themes like protein expression or clinical outcomes.
- Quality-First Ranking: Articles were prioritized by journal reputation, author prominence and PDB identifiers.
- Summarization at Scale: Thirty-page studies were condensed into concise abstracts or 10–20 papers compared in tabular overviews.
- Protocol Extraction: Experimental methods were highlighted directly, enabling rapid replication of prior research.
- Confidential Document Q&A: Secure ingestion of proprietary data meant internal knowledge stayed safe while still queryable.
Persistent’s differentiator was hybrid intelligence — fusing ML clustering and named-entity recognition with GenAI reasoning. This ensured speed plus scientific accuracy, not one at the expense of the other.
From Weeks to Minutes: Business Impact
The results were immediate and measurable:
- 70–80% Faster Discovery: Literature review cycles collapsed from 10–12 days to under 10 minutes.
- 2–3x Productivity Gains: A 10-person team could deliver the same output with two-to-three researchers, or take on triple the projects.
- Lower Costs: No additional hiring was needed for repetitive tasks; bandwidth was redirected to innovation.
- Accuracy and Consistency: Blending ML with GenAI removed human bias while raising confidence in sources.
- Refocus on Research: Scientists could finally return to hypotheses, experiments and trial design instead of administrative work.
AccelProSearch didn’t just speed up literature review — it gave scientists their time back to focus on discovery.
McKinsey estimates that genAI could add up to $110 billion annually in value to the pharmaceuticals and medical products sector through productivity gains and accelerated R&D. AccelProSearch positioned the client to capture this value early.
Scaling Science without Limits
While the immediate gains were clear, Persistent and the client deliberately built for future-proof scale. The architecture was designed to evolve with biomedical AI, supporting domain-specific LLMs, new biomedical models and continuous improvements.
The roadmap also extended across the R&D value chain — from early discovery to protocol generation, clinical trials and patient recruitment. EY highlights that 69% of life sciences executives see AI as essential to accelerating clinical development and trial operations. With AccelProSearch, the client not only addressed today’s literature bottleneck but also laid the foundation for tomorrow’s trial acceleration.
Why Persistent
The success of AccelProSearch rested on a set of hallmarks that defined Persistent’s contribution:
- Domain-Specific Focus: Purpose-built for biomedical research workflows
- Hybrid Intelligence: ML + NLP + GenAI fused for precision and reasoning
- Security-First: Confidential internal research queryable within a safe enterprise environment
- Custom Engineering: Independently developed on AWS for full flexibility and control
- Scientist-Centric Design: Features like protocol extraction and multi-paper summaries aligned with real researcher needs
- Future-Ready Architecture: Flexible enough to integrate next-generation biomedical LLMs
- Proven Outcomes: Delivered 90%+ time savings, higher accuracy and improved throughput
Re(AI)magining Research: The Bigger Picture
AccelProSearch did more than accelerate literature review — it redefined how science gets done. What was once a manual, error-prone bottleneck is now an AI-driven engine of discovery.
With Persistent’s AI-first engineering and the client’s scientific rigor, the transformation unlocked:
- Faster discoveries
- Stronger pipelines
- Accelerated trials
- A future-ready foundation for biomedical innovation
Turn bottlenecks into breakthroughs. Build AI-ready R&D engines with Persistent.
