An autonomous agent that mines $200M+ of yearly invoice data and surfaces margin levers a BI team would never find
HomoDeus's Sibiu agent runs continuously over a global invoice and sales database, generates hypotheses on margin and pricing levers, and writes the analyses the operators would otherwise commission by hand.
Multi-continent factories across Asia and the Americas. Naming withheld.
The client sits on a gigantic internal database that consolidates global invoice and sales activity across its supplier and customer base. The database is the financial reporting source of truth every month. It is also the place where margin levers hide. Finding them today requires a BI and tech team to manually build each report, which limits the manufacturer to the routine KPIs everyone already watches.
Outcomes
The dashboard team stops being the discovery layer. The agent runs continuously and surfaces the levers the routine KPIs cannot see.
The problem
The data is all there. The discovery loop is the problem. Routine BI refreshes the same dashboards the same way. Asking the database a new question takes weeks of tech-team work, by which time the lever has moved. The manufacturer needed something that asks new questions of the same data, continuously, without scheduling.
What we built
HomoDeus deploys Sibiu, an agent normally sold to investment funds and adapted here for industrial margin work. It sits on the manufacturer's ontology, continuously mines the invoice and sales database, generates hypotheses about margin and pricing levers, and writes the analysis a senior operator would commission. The interface is conversational. A prompt runs live in about eleven minutes against the full database and returns a written, cited analysis the team can argue with. Routine BI keeps running. Sibiu adds the questions the routine never asks.
Stack
- Internal manufacturing ontology
- Continuous database mining engine
- Hypothesis-generation agent
- Conversational analyst interface
- Live prompt-to-analysis runtime
- In-environment deployment
Time to production
6 weeks to first production agent, productised "Sibiu" runtime
In their words
"Consider us the Brazilian Palantir."— HomoDeus, in-call positioning to the prospect
Questions a C-suite leader will ask
What does an autonomous data agent do for a manufacturer?
It mines the firm's invoice and sales database continuously, builds hypotheses about margin and pricing levers, and writes the analysis a senior operator would otherwise commission. The routine BI keeps running for the recurring KPIs; the agent adds the questions the routine never asks.
How is this different from a BI dashboard?
A dashboard answers the question you wrote a year ago. Sibiu reads the same data and generates new questions, then writes the analysis. A live prompt against the full database returns a cited piece of analysis in about eleven minutes, not a refreshed chart.
Does the data leave our environment?
No. Sibiu deploys inside the manufacturer's ontology and infrastructure. The agent runs against the database in place, not against a copy uploaded somewhere else.