Who is this built for, and where does it already work?
Regulated, data-heavy operations: healthcare and revenue cycle, financial services, aviation, retail and e-commerce, and supply chain, with AI tuned for those domains rather than generic.
The verticals on this page
- Healthcare: patient analytics, clinical data, HIPAA-aware handling
- Revenue cycle management: claims analytics, denial tracking, A/R aging
- Financial services: risk analysis, transaction monitoring, compliance
- Aviation: fleet analytics, route optimization, safety metrics
- Retail and e-commerce: customer analytics, inventory, sales trends
- Supply chain: logistics tracking, demand forecasting, vendor analytics
The pattern underneath them
They share a shape rather than an industry. Data that is regulated or simply too large to keep copying, a small analytics team acting as a queue for a much larger operations team, and questions whose value decays within days. That is where removing the pipeline changes the outcome instead of just the invoice.
Why domain tuning matters
A denial rate, an OTIF number, and an exposure calculation are not generic aggregations. Industry tuning for revenue cycle, supply chain and finance is what makes the first question work rather than the fifth.
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.