We built this because manual categorization was killing productivity
How we started
Back in 2020, we were working with a mid-sized consulting firm that spent roughly 40 hours each month just sorting through expense reports. Their finance team would sit there categorizing lunch receipts, hotel bookings, and taxi rides one by one, while their accountants waited to close the books. The bottleneck was obvious, frustrating, and expensive.
We looked at what was available then and found tools that either required too much manual setup or gave you accuracy rates that meant you'd still need to review everything anyway. So we started building something that could actually learn from how businesses naturally categorize expenses, adapt to their specific rules, and get better over time without constant retraining.
Our first prototype took about eight months to reach the point where it could handle 85% of transactions without human review. We tested it with three companies, gathered feedback on what broke their workflows, and rebuilt the classification engine twice. The current system handles mixed-language receipts, learns from corrections, and integrates with most accounting platforms without requiring API specialists to set it up.
2020
Year founded in Thailand
18
Team members across development, support, and operations
92%
Average accuracy rate on first categorization attempt
What drives how we build this platform
Every feature we add has to solve a real problem we've seen clients struggle with
Accuracy matters more than speed
We've tested categorization algorithms that could process 1000 transactions per second but got 30% of them wrong. That's useless because someone still has to review and fix everything. Our system prioritizes getting it right the first time, even if that means taking an extra second to analyze context from merchant names, transaction amounts, and historical patterns.
- Multi-factor validation checks before finalizing categories
- Confidence scoring that flags uncertain classifications
- Learning from manual corrections to improve future accuracy
Regional variations are built in
Expense categorization rules change depending on where your business operates and what regulations apply to your industry. We built the system to handle different tax codes, local merchant naming conventions, and regional expense categories without requiring you to maintain separate configurations for each location you work in.
- Support for mixed-currency transactions with proper classification
- Recognition of region-specific merchant types and services
- Adaptable category structures that match local accounting standards
Integration shouldn't require developers
Most platforms either force you to use their proprietary tools or make you hire someone to write custom code for basic connections. We designed our integration system so your finance team can link accounting software, import transaction feeds, and map categories themselves using a visual interface that shows exactly what's happening at each step.
- Pre-built connectors for common accounting platforms
- Visual mapping tools for custom category structures
- Test modes that let you verify integrations before going live
Transparency in how decisions happen
When the system categorizes an expense, you can see exactly why it made that choice instead of just getting a final result. We show which factors influenced the decision, what alternatives were considered, and how confident the system is about its classification so you can quickly verify or correct without guessing.
- Detailed reasoning for each categorization decision
- Confidence scores that indicate when manual review might help
- Audit trails showing how categories were assigned over time