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Technology that turns financial data into intelligent decisions.

Every project we build solves a real problem we've experienced firsthand — inside Brazilian companies, with Brazilian data, and Brazilian tax complexity.

Artificial Intelligence · Autonomous Agents

AI-Powered FP&A Analyst

A multi-agent system that reads, interprets, and analyzes financial statements automatically — like a senior analyst who works 24/7 without ever miscalculating.

Before
  • Analyst takes 3-5 days to process a P&L
  • Account mapping done manually
  • Misclassification errors go unnoticed
  • Period-over-period comparison takes hours
Now
  • Full processing in minutes
  • AI maps accounts automatically
  • Cross-validation between agents
  • Instant variance analysis
How it works
1
Data Engineer Agent
Receives the raw file (P&L, trial balance, cash flow) and transforms it into structured, normalized data ready for analysis.
2
FP&A Analyst Agent
Automatically maps each accounting entry to the management structure, calculates KPIs, and identifies anomalies in margin, profitability, and liquidity.
3
Coordinator Agent
Orchestrates collaboration between agents, validates data consistency, and generates the final management report with insights and recommendations.
Tech Stack
Python LangGraph Multi-Agent Bronze/Silver/Gold
3
Autonomous agents collaborating
~5min
To process a complete P&L
100%
Automatic account mapping
In active development
Financial Automation · Reconciliation

Smart Receivables Reconciliation

Automates the process of matching what you sold against what actually arrived in your bank account — cross-referencing invoices, bank statements, and receivables in seconds.

Before
  • 2-3 days per month spent on manual reconciliation
  • Fragile VLOOKUP-based spreadsheet matching
  • Penny differences accumulate into serious errors
  • Delinquency discovered far too late
Now
  • Complete reconciliation in minutes
  • Automatic matching across multiple keys
  • Discrepancies flagged with full context
  • Real-time visibility into receivables position
What it solves
→
Automatic source import
Ingests bank statements, ERP reports, and invoices in any format. Normalizes and standardizes automatically.
→
Intelligent matching
Cross-references records by amount, date, customer, and document number. Identifies partial payments, duplicates, and penny discrepancies.
→
Exception report
Generates a prioritized report of discrepancies by amount and aging. Finance teams focus only on items that need action — not rework.
Tech Stack
Python Pandas Automation ETL
95%
Reduction in reconciliation time
0
Spreadsheet rows to cross-check
Real‑time
Cash position visibility
In active development

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