Independent Research · Applied AI · Document Intelligence
ParancU Lab is an independent research and development initiative investigating how artificial intelligence can retrieve factual information from documents, identify supporting evidence and recognize the limits of what a source can establish.
The work combines released applications, experimental prototypes and ongoing research.
The research question
A document passage may be closely related to a question without containing the facts needed to answer it.
ParancU Lab investigates this distinction, exploring methods to retrieve evidence, preserve its connection to the original source and evaluate whether the available information is sufficient.
This remains an open research problem, with promising experimental results as well as unresolved technical challenges.
A growing body of experimental work.
A mobile application for document retrieval using multilingual embeddings and on-device search. An initial implementation that provided a foundation for further experimentation.
Retrieval runs on-device after corpus preparation, which currently sends source chunks to OpenAI using a user-supplied API key.
Explore the iOS app →An exploration of document retrieval combined with AI-assisted answer generation, evidence inspection and verification workflows.
Imported prepared corpora support browser-local retrieval. Server-assisted preparation, generation and verification use OpenAI; verification does not guarantee correctness.
Explore Agent Lab →Investigating factual representations and question-oriented retrieval methods, with the aim of improving the identification of answer-bearing passages.
Explore ParancU 2.0 research →Exploring how to evaluate evidence sufficiency, identify missing information and select complementary sources.
Explore Decision Lab research →Research approach
The laboratory develops and evaluates approaches to document retrieval and evidence assessment.
Experiments examine both successful cases and failures, including misleading semantic similarity, missing conditions, unsupported conclusions and incomplete evidence.
Results are treated as experimental findings rather than proof of general reliability.
Read about the research →Exploratory collaborations
Complex technical and organizational documents present challenges that may benefit from more reliable information retrieval.
ParancU Lab is interested in exchanging ideas with researchers, engineers and organizations working with such documentation.
Potential collaborations may help identify relevant problems, evaluate experimental approaches and explore future directions.
No specific commercial model or deployment architecture is assumed at this stage.
Explore potential collaborations →Exchange ideas
