Use case · Finance · BI · Financial Control

From fragmented Excel to predictive intelligence

In finance, wrong or delayed data isn't just inconvenient — it's a direct loss. Yet teams still waste days cleaning spreadsheets and waiting for IT to release yesterday's urgent report.

With MyDatAgent's AI Ecosystem, you don't just see the past. You automate ETL, converse with your Data Lake in real time, and predict your next portfolio move — with the governance and security your CISO demands.

Impact · Real results

When finance speaks the language of AI, numbers scale

Results achieved from implementing DataOps, BI Analyst, and MLOps agents across Brazilian mid-market companies.

+35%
Conversion · Cross-sell

Intelligent recommendations

Hybrid system (collaborative filtering + content) suggests the right product for each customer. Direct lift in adoption via AI-guided cross and up-selling.

−60%
CAC · Customer acquisition cost

Focus on predictive leads

Sales effort directed to leads with high predictive potential instead of spray-and-pray. Marketing and sales spend less to close more.

+340%
Productivity · BI & Financial Control

End of manual ETL

Automated pipelines eliminate consolidation work. Your BI team goes back to analysis — instead of copying Excel to Excel.

The trio of agents · DataOps · BI · MLOps

It's not a dashboard. It's an AI orchestra on three fronts

Three specialist agents, each with its own role — communicating via A2A to deliver from raw data to executive decision.

Agent · DataOps

Automated ETL

The end of manual data work. Applies the Medallion Architecture (Bronze → Silver → Gold) with Airflow, dbt, and Python.

  • Collection from legacy sources (CRM, ERP, NPS, spreadsheets)
  • Pipelines with tests and data contracts
  • Centralized Data Lake and 100% governed
AirflowdbtPythonBigQueryS3
Agent · BI Analyst

Conversational virtual CFO

No more waiting days for reports. The agent connects directly to your Data Lake (BigQuery / Postgres) via SQL — and answers in natural language.

  • Ask in English · get a graph in seconds
  • Input/output validation · zero hallucinations (Aporia)
  • Conversation history · natural follow-ups
SQLBigQueryPostgresAporiaRAG
Agent · MLOps

Predictive analysis

Knowing what happened is table stakes. Knowing what's coming next is the edge. ML applied directly to your portfolio.

  • Hybrid recommendations: collaborative + content
  • Churn in clusters: High >0.7 · Medium 0.3-0.7 · Low <0.3
  • Automatic triggers for your sales team
scikit-learnXGBoostMLflowFeature storeA/B
Medallion Architecture

From raw data to analytics-ready, in 3 moves

The processing layer that separates data-driven companies from Excel-trapped ones. Implemented via Airflow + dbt + Python, with governance and lineage at every stage.

Bronze Layer

Raw data · ingestion

Collects raw and inconsistent data from multiple sources — CRMs, ERPs, NPS, scattered spreadsheets. No cleaning, with timestamp and lineage.

Schema-on-readEvent logAudit trail
Silver Layer

Cleaning & standardization

Automated pipelines apply business rules: deduplication, normalization, cross-source joins, derived metric calculation. All tested and versioned.

dbt modelsData contractsTestsLineage
Gold Layer

Analytics-ready

Data structured, enriched, and ready for consumption by the BI agent, ML models, and executive dashboards. The gold of your data lake.

Star schemaAggregatesML featuresDashboards

Result: your centralized Data Lake, updated in near real time with 100% governance — ready for the BI agent to converse with and ML models to train on.

Spotlight · Predictive dashboard

Doesn't show data. Indicates what's next

The window into the ecosystem for finance and product leaders. Live admin panel — portfolio performance, gaps, contextual recommendations, and predictive churn alerts.

Effectiveness & engagement

Score combining frequency, recency, diversity, and hours used. Identifies the "Active Explorers" and the "Selective Veterans".

Portfolio gaps

AI identifies new product opportunities based on unmet business pain points. Product roadmap becomes a testable hypothesis.

Contextual recommendations

The dashboard suggests the next product/service for each customer — maximizing LTV via hybrid-model-guided cross-selling.

Predictive churn alerts

Real-time flagging of accounts with declining engagement — red/yellow/green semaphore with triggers for your sales team.

Security & data sovereignty

Financial data has vault-grade weight

We treat your spreadsheets, ERPs, and CRMs with the rigor of a financial institution. Nothing leaves the country. Nothing trains public models.

Private cloud · Brazil

Your dedicated VPC in a Brazilian datacenter. Zero exposure to international public APIs. Native LGPD compliance, with data residency and tenant isolation.

No external training

Your company's data is never used to train third-party models. SLMs run isolated in your VPC — you own the context, inference, and audit trail.

SSO + RBAC + DLP

Unified SSO, granular control of who queries what, LGPD rules enforced via Guardrails (DLP and PII). CPF and sensitive data never appear in prompts or responses.

Strategic box · MDA Consulting

Finance team drowning in Excel, but IT has no bandwidth?

Most mid-market companies have the data but don't have the architecture to turn it into intelligence. A structured Data Lake, tested pipelines, recommendation models, and a predictive dashboard don't happen by accident.

MDA Consulting designs and implements the entire pipeline. We map your sources (CRMs, ERPs), build Medallion ETL pipelines, train recommendation and churn models, and deliver a production-ready predictive dashboard your CFO can use from day one.