On the internet, many providers offer gut microbiome tests. The following summarizes ten technical and methodological reasons that distinguish InnerBuddies’ approach. For background on why gut microbiota matter, see What is gut microbiota and why does it matter. 1. Special liquid buffer to keep the sample stable The collection tube includes a preservation buffer designed to stabilize stool DNA long-term. Stabilization reduces degradation and potential shifts in composition during transport and storage, supporting reproducible downstream sequencing results. 2. Use of advanced Next Generation Sequencing (NGS) NGS provides broad, high-resolution detection of microbial DNA versus targeted assays (PCR/qPCR). InnerBuddies partners with established sequencing platforms and routinely validates laboratories using mock communities of known composition to assess accuracy and sensitivity. 3. Laboratory techniques tuned for diverse bacteria Extraction and processing methods can bias recovery of gram-positive versus gram-negative organisms. Protocols that explicitly address cell-wall differences improve the representativeness of measured community structure. 4. Proprietary AI-based analysis pipeline Raw sequence reads require taxonomic and functional annotation. InnerBuddies uses trained machine-learning models to map sequences to reference databases and to infer probable taxa and gene functions, improving identification beyond simple alignment heuristics. 5. Quantification of bacterial functions in addition to taxa Functional profiling (metabolic pathways, gene families) complements taxonomic profiles because multiple taxa can perform the same roles. Functional readouts tend to be more conserved across populations and can be more informative for host-microbiome interactions. 6. Integration of a 3-day food diary Diet is a primary modulator of the gut microbiome. Collecting a short food diary provides contextual metadata for interpreting results and for correlational analyses between recent intake and microbial features. 7. Personalized ingredient recommendations informed by population data By correlating diet logs with microbiome and functional profiles across many participants, machine-learning models can generate individualized dietary suggestions that reflect observed associations in the dataset rather than only general dietary guidelines. 8. Interdisciplinary team combining microbiology, dietetics, and AI Combining laboratory expertise, clinical nutrition knowledge, and computational methods supports rigorous sample processing, meaningful interpretation, and scalable data analysis pipelines. 9. Academic provenance and scientific oversight As a spin-off of Maastricht University, the project emphasizes methodological transparency and academic review. Scientific oversight aims to ensure that reported interpretations are consistent with current evidence. 10. A structured healthy cohort defined by metadata and pathways Rather than simple taxon ranges, the healthy cohort integrates structured health metadata and pathway-level features, which can provide more stable reference points across diverse diets and populations. For further methodological detail, see What is gut microbiota and why does it matter. For information on the specific test product and kit components, there is a product page: microbiome test product page. The technical choices above reflect considerations relevant to sample integrity, analytical breadth, and contextual interpretation. The full test description is available at [InnerBuddies’ gut microbiome test](https://www.innerbuddies.com/blogs/gut-health/10-reasons-why-innerbuddies-gut-microbiome-test-is-best).