We don't do generic human outsourcing. Instead, we deliver traceable structured datasets by domain: we first co-design the Schema with your algorithm team, then AI pre-annotates, domain experts refine, and three-tier QC signs off before it leaves the door. Below, you'll find the solutions we currently offer, organized by industry domain and by specific scenario.
Three forms—entities, structures, and reactions: normalization of chemical entities and their many names, structure diagram recognition and correction, and annotation of the raw materials, intermediates, products, reagents, and yield relationships in organic synthesis routes.
Chemical entitiesStructure diagramsSynthesis routesThree coupled classes of data—formulation, process, and performance: component contents with original bases preserved, preparation and processing parameters, and characterization data with performance metrics, all recorded together with their test conditions.
Formulation basisProcess parametersPerformance metricsThe full chain from target to compound, activity, clinical, and pipeline: compound–target relationship annotation, bioactivity assay data extraction, antibody sequences and affinity constants, and clinical trial fields with pipeline status updates.
Target relationshipsActivity dataPipeline monitoringProtein and nucleic acid sequence extraction, modification information decomposition, SEQ ID NO alignment, and database cross-referencing, as well as the cleaning and linking of cross-table entities such as traditional Chinese medicine knowledge bases.
Sequence normalizationModification decompositionKnowledge basesExtract and correct chemical structure diagrams from patents, literature, and lab records: full compound names, Markush formulas, non-specific structures, and complex substituents. AI pre-annotation + refinement by chemists + three-tier QC, delivering a structured SMILES / InChI dataset.
OCSRMarkushStructure drawingPull the activity data scattered across patents and literature out together with its test conditions: test substances, activity metrics, value units, test subjects, and methods. We handle mixed use of IC50 / EC50 / Ki and inconsistent units, delivering an SAR dataset with provenance information.
PK/PDUnit normalizationTable extractionExtract protein and nucleic acid sequences, normalize formats, decompose modification information, and align SEQ ID NO numbering with body-text citations. We handle cross-page continuation and database cross-references, delivering a sequence dataset ready for ingestion.
Sequence normalizationModification decompositionCross-page continuationStructure the formulations, process steps, characterization data, and performance metrics in patents into a comparable database. Original wt% / mol% bases and test conditions are preserved—no forced conversion, no converting range values into point values.
Formulation basisProcess parametersIonic conductivityUsing public research, this explains why a dataset containing only positive examples cannot define a model's boundary, why annotation disagreement is a signal rather than noise, why evaluation sets saturate with age, and what each of the three PEN (Positive / Edge / Negative) data categories solves.
2026-10-01Send us your data sources, target Schema, and acceptance criteria. We'll first determine which category the task falls into and where the boundaries are most likely to appear, then provide an executable annotation plan.