What is the business case against a warehouse plus BI stack?
Roughly 80% lower analytics cost by removing pipelines, warehouse storage and duplicate copies, and around 40% higher operational efficiency because teams self-serve answers instead of queueing for analysts.
Two lines on the page, and they come from different parts of the budget.
Cost, around 80% lower
A warehouse stack charges you three times for the same fact: once to move it, once to store the copy, and once to query it. Removing the pipeline and the duplicate storage removes the first two, and the third runs on infrastructure you already pay for.
Efficiency, around 40% higher
The hidden cost of analytics is the queue. A question worth asking on Monday is worth much less by Thursday, so most of them are never asked. Self-serve turns the queue into a conversation, and the marginal cost of the next question falls close to zero.
Where the moat is, for an investor reading this
Not the model. Anyone can call a model. The defensible parts are the connectors and live schema discovery, the human-in-the-loop layer that refuses to guess when a question is ambiguous, the dual-gate authorization that makes the architecture reviewable, and the domain tuning for revenue cycle, supply chain and finance. Those compound. Prompt quality does not.
Where this comes from
See it against your own data
A pilot is scoped to one governed use case and time-boxed to eight to twelve weeks, with success criteria agreed before it starts.
Get started freeRelated questions
- What is dataface.ai?dataface.ai is on-source conversational analytics. Ask a question in plain English, and it generates read-only SQL from your schema metadata and runs it on the systems you already own. No warehouse copy, no pipeline.
- How do you run analytics without moving our data?dataface.ai reads your schema and a few example values per column, generates read-only SQL, and executes it on the source system itself. Your tables are never copied: no pipeline, no warehouse copy, no second place to secure.
- What does the LLM actually see from our data?By default the model sees your catalog: table names, column names, types, and a few example values per column. It never receives your tables, and it only sees query results if a user turns on AI analysis.
- How do you stop an AI from writing to or damaging our database?Generated SQL is SELECT-only. INSERT, UPDATE, DELETE and DDL are blocked, queries are parameterized against injection, and every request passes both a workspace gate and a datasource gate.