# Introduction ## *Accelerate your data ingestion and cleanup using AI-powered intelligence.* ### How Does Osmos Work? Osmos uses AI to automate the messy parts of ingestion: schema detection, mapping, transformation, and validation. Our AI suggests mappings, generates transformations from natural language, applies data quality checks, and asks for approval where confidence is low. You stay in control with approvals, versioned instructions, and audit logs. Osmos integrates with your existing stack (files, APIs, apps, and warehouses) to provide a scalable, secure, and observable ingestion layer that fits batch or streaming workflows. ### What is Osmos used for? Osmos makes data onboarding accessible across teams. Everyday use cases include: * **Customer, supplier, & partner onboarding:** Map and clean incoming CSV/Excel/API payloads into your destination schema. * **Data cleanup & normalization:** Standardize dates, addresses, SKUs, currencies, and categorical values at ingest time. * **Unifying messy sources:** Merge multi-vendor files into a single high-quality dataset with deduping and survivorship rules. * **Governed self-serve ingestion:** Let business teams load data safely with AI guardrails, approvals, and logs. ### Why use Osmos for AI-Powered Ingestions? * **Speed & accuracy:** Cut onboarding from days to minutes with AI-assisted mapping and transformations. * **Human-in-the-loop controls:** Confidence scores, preview diffs, and approval steps keep you in charge. * **Scales with your data:** Works for one-off files or recurring pipelines with millions of rows. * **Observability & trust:** Run history, lineage, and audit artifacts for every job. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.osmos.io/welcome-to-osmos/introduction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.