Sepsis is defined as a syndrome of life-threatening organ dysfunction caused by a dysregulated host response to an infection. Early detection of sepsis and immediate treatment with antibiotics is critical for patient outcomes. Haemophilus influenzae (H. influenzae) is a gram-negative bacteria known to be a human-adapted pathogen that may cause a variety of communityacquired infections such as sepsis. A rapid increase in beta-lactam resistance in H. influenzae has been noticed and has become a major problem in clinical care. By implementing bacterial whole genome sequencing (WGS) in the clinical laboratory, it can provide a great amount of information such as species identification, serotype identification, antimicrobial resistance prediction, typing for epidemiologic purposes and tracking infectious disease outbreaks. The aim of this study was to analyze WGS data for clinical H. influenzae isolates using an in-house developed bioinformatic pipeline and an automated 1928 Diagnostics platform to evaluate and compare the predicted results in terms of species identification, prediction of resistance and virulence genes. Furthermore, the predicted genotypic antibiotic resistance genes were compared to the phenotypic antimicrobial susceptibility testing obtained from the clinical laboratory. For the in-house developed pipeline, the analysis of H. influenzae WGS data started with quality control and preprocessing (trimming) of FASTQ files. Following, de novo assembly and quality assessment of assembled contigs and lastly gene annotation tools were performed. For 1928 Diagnostics, the untrimmed FASTQ files were uploaded to the 1928 platform. Species identification resulted in a high agreement of predicted H. influenzae for both phenotypic and genotypic methods except for one sample that may have been contaminated. The analysis of antibiotic resistance genes resulted in both in-house developed pipeline and 1928 Diagnostics having a high agreement regarding the prediction of broad-spectrum beta-lactamase in six clinical isolates, all of which predicted bla TEM-1B. The four most common sequence types found in the MLST analysis from the in-house pipeline were ST159, ST388, ST14 and ST12. The analysis of virulence genes yielded a large number of different virulence genes and each of the identified virulence genes codes a specific function that is crucial to the pathogenesis of H. influenzae. In conclusion, the obtained results provide valuable insights into using WGS-based analysis as a reliable tool for determining the pathogen characteristics in clinical bacterial isolates.