A leading global investment intelligence company needed a way for business users to ask complex financial questions in plain language and get trusted, consistent answers back, whether that meant a table, a chart, or a written summary, without writing a line of SQL or waiting on an analyst to translate the question first. The existing setup meant every ad hoc question turned into a ticket, and by the time an answer came back, the question had often changed. Hoonartek built the solution around PeggyX, staging a semantic model directly in Snowflake so business terms mapped cleanly to the underlying data instead of requiring users to know table and column names. A Streamlit interface gave business users a place to ask their questions. At the same time, a Cortex Agent routed each query to the relevant semantic YAML definition, powered by the Mistral-Large 2 model, before Cortex generated a natural-language response grounded in that governed model rather than a raw guess at the schema. The result is business users getting answers instantly instead of waiting on an analyst or SQL development, with the same semantic layer keeping every answer consistent no matter who asks or how it’s phrased. That combination of a governed semantic layer and a conversational interface is what turns a natural-language tool into something finance teams actually rely on daily.

A communications, media, and technology company was running its core data estate on a legacy platform that had become the single biggest driver of its infrastructure costs. Slow queries delayed new product launches and a fragmented tech stack kept operational costs climbing. The scale of what needed to move was roughly 2 petabytes of data across 27,000 tables and 2,400 reports, which made a manual migration impractical. Hoonartek delivered the migration through MigFac, a factory-led migration model built around reconciliation checks at every stage, so nothing moved without first being validated against its source. Existing Ab Initio pipelines were repointed to Snowflake with minimal disruption to downstream consumers, and a secure, reliable data movement path was established from on-premises systems into AWS and Snowflake, replacing a brittle, manually managed handoff. The migration ultimately moved data at a sustained 50,000 transactions per second, and query times that used to run around 10 hours came down to about 5 minutes once the platform was live. That kind of performance shift is what actually justifies a migration this size: not just moving data to a newer platform, but making the platform fast enough that teams stop working around it.