Business answers, not just reports.
IV Advanced analytics and AI
We define the metrics that move the business and bring them to executive dashboards. On that base we build AI models and assistants that answer questions in natural language.
Services
2 serviciosAdvanced analytics
KPIs, dimensional modeling, executive dashboards and tailored analysis for management control.
Deliverables
- KPI tree
- Dimensional model
- Executive dashboards
- Ad hoc analysis
Applied AI
Prioritized AI use cases taken to production with control: predictive models, assistants and agents on your data.
Deliverables
- Use case prioritization
- Models in production
- Analytics assistants
- Model monitoring
In code
AI with engineering guardrails.
The assistant queries only what is allowed, read-only, with every answer traceable.
def responder(pregunta: str) -> Respuesta: # Answers business questions over the semantic model contexto = semantica.buscar(pregunta, dominio="ventas") sql = llm.generar_sql(pregunta, contexto) # Guardrails: read-only, allowed tables, row limit validar(sql, solo_lectura=True, tablas=contexto.tablas, limite=10_000) datos = warehouse.consultar(sql) return Respuesta( texto=llm.resumir(pregunta, datos), grafico=sugerir_grafico(datos), fuente=sql, )
PYTHON · 14 lines● main · reviewed
Questions we answer
Are we ready for AI?
AI works if the data underneath is trustworthy; we assess that first.
What questions can it answer?
The ones that today take days of manual reconciliation across departments.
How is it controlled?
With monitoring, access limits and business oversight.
Related
Do you have a data or AI challenge?