
At Heard, we built an AI-powered auto-categorization system that reduced manual bookkeeping time by 40%. The system uses Python with machine learning libraries to categorize transactions automatically.

#### The Problem

Mental health professionals spend hours each week categorizing expenses for tax purposes. This is tedious, error-prone work that takes time away from their practice.

#### The Solution

We built a classification system using:
- Scikit-learn for initial models
- Transformers for better accuracy
- FastAPI for the API layer
- PostgreSQL for transaction storage

#### The Architecture

```python
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

class TransactionCategorizer:
    def __init__(self):
        self.vectorizer = TfidfVectorizer()
        self.classifier = MultinomialNB()
    
    def train(self, transactions, categories):
        X = self.vectorizer.fit_transform([t.description for t in transactions])
        self.classifier.fit(X, categories)
    
    def predict(self, transaction):
        X = self.vectorizer.transform([transaction.description])
        return self.classifier.predict(X)[0]
```

#### Results

- 40% reduction in manual categorization time
- 92% accuracy on common categories
- Continuous learning from user corrections
- Significant user satisfaction improvement

#### Lessons Learned

1. Start with simple models, iterate based on feedback
2. User corrections are valuable training data
3. Explainability matters-users need to trust the system
4. Handle edge cases gracefully

> "AI should augment human judgment, not replace it."



ai

AI-powered auto-categorization in accounting software