Deep expertise in RAG architecture design optimized for domain-specific, large-scale knowledge systems
Research & Knowledge Services
Accelerating climate research with AI
A climate research institute managing a large and growing repository of peer-reviewed papers, datasets, and environmental reports, with researchers spending disproportionate time on manual literature review. JBS introduced AI scientific literature review to reverse that.
The challenge
The volume and pace of scientific output was outpacing researchers’ ability to efficiently extract value:
- Researchers spending the majority of review time reading papers for relevance before finding insights
- Keyword-based search returning too many irrelevant results, requiring extensive manual filtering
- Cross-referencing findings across thousands of documents difficult, time-consuming, and error-prone
- Synthesis of multi-source knowledge requiring significant analyst effort before insights could emerge
- Speed of insight generation failing to match the accelerating pace of data production in the field
The solution
JBS built a domain-specific AI research assistant on a vector retrieval foundation enabling:
- Natural language query interface enabling researchers to explore the corpus conversationally
- Semantic vector search identifying conceptually relevant content beyond exact keyword matches
- Automated summarization distilling key findings from large document sets around specific questions
- Contextual response generation synthesizing insights from multiple sources into coherent, attributed answers
- Scalable knowledge access enabling the full corpus to be queried without infrastructure constraints
The business impact
- Literature review cycles shortened dramatically as relevant content surfaces immediately through natural language
- Researcher capacity redirected from document searching to hypothesis development and substantive analysis
- Cross-document synthesis achievable in minutes rather than hours or days of manual work
- Knowledge discovery improved as semantic retrieval surfaces relevant content missed by keyword search
- Research productivity increased as the primary bottleneck of manual knowledge retrieval was removed
Results achieved
- Research literature review time reduced substantially through AI-assisted natural language querying
- Relevant findings surfaced significantly faster than through traditional keyword-based search
- Researcher productivity increased as manual synthesis effort was automated at scale
Why JBS
Experience implementing AI for science and research contexts with high accuracy requirements
Strong LLM engineering capability covering fine-tuning, retrieval optimization, and evaluation
Practical adoption focus ensuring tools are actively used by researchers rather than just deployed
Scientific literature review at scale
The assistant turns scientific literature review from a manual reading task into a conversational query. Researchers ask questions in natural language and receive synthesized, attributed answers drawn from thousands of papers — surfacing relevant work that keyword search would miss.
Frequently asked questions
Can AI do a scientific literature review?
AI can accelerate a scientific literature review by retrieving conceptually relevant papers and summarizing findings with citations, though researchers remain responsible for interpretation. JBS used this approach to cut review time substantially while improving discovery of relevant work.