# Building an AI Memory System for Home Services

One of the biggest challenges in home services is personalization at scale. How do you remember that Mrs. Johnson prefers lavender-scented products while Mr. Chen needs everything to be unscented? How do you ensure this knowledge transfers between different crew members?

At Maidly.ai, we solved this with a custom AI memory system built on Retrieval-Augmented Generation (RAG). Here's how we did it.

## The Architecture

Our memory system consists of four main components:

### 1. Preference Extractor
When customers provide feedback, our AI extracts structured preferences using GPT-4:

```typescript
interface Preference {
category: 'products' | 'access' | 'pets' | 'special';
label: string;
confidence: number;
context: string;
}
```

**Input:** "Please don't use any bleach products, they trigger my asthma"
**Output:**
```json
{
"category": "products",
"label": "No bleach products",
"confidence": 0.95,
"context": "Customer has asthma sensitivity"
}
```

### 2. Vector Storage
We use Pinecone to store preferences as embeddings, enabling semantic search:

```python
# Store preference with metadata
index.upsert([
{
"id": f"{customer_id}_{preference_id}",
"values": embedding,
"metadata": {
"customer_id": customer_id,
"category": preference.category,
"label": preference.label,
"active": True,
"created_at": timestamp
}
}
])
```

### 3. Briefing Generator
Before each cleaning, we query the vector database and generate crew briefings:

```typescript
const briefing = await generateBriefing({
customerPreferences: preferences,
homeDetails: homeInfo,
previousVisits: visitHistory
});
```

### 4. Feedback Loop
After each visit, we analyze outcomes to improve our system:
- Did the crew follow the briefing correctly?
- Were there any new preferences mentioned?
- How satisfied was the customer?

## Technical Challenges

### Challenge 1: Preference Conflicts
What happens when a customer says "use eco-friendly products" but later requests "the strongest cleaner possible"?

**Solution:** We implemented a confidence scoring system and temporal weighting. Recent preferences get higher priority, and we flag conflicts for human review.

### Challenge 2: Context Understanding
"Don't wake the baby" means different things at different times of day.

**Solution:** We store contextual metadata and use LangChain to build dynamic prompts that consider time, day of week, and household patterns.

### Challenge 3: Privacy & Security
Customer preferences contain sensitive information about their homes and families.

**Solution:**
- End-to-end encryption for all stored data
- Minimal data retention (preferences only, no personal details)
- Customer control over data deletion

## Results & Metrics

After 6 months of development and 3 months of beta testing:

- **95% accuracy** in preference extraction
- **2.1% rework rate** (down from 8.5% industry average)
- **4.8/5 satisfaction** rating from beta customers
- **47 preferences** learned per customer on average

## What's Next

We're working on several exciting improvements:

### Predictive Preferences
Using machine learning to predict preferences before customers explicitly state them:
"Based on your home and family profile, you might prefer..."

### Multi-Modal Learning
Incorporating photos from cleaning visits to learn visual preferences:
"Customer prefers books organized by height, not alphabetically"

### Crew Optimization
Matching customers with crews based on preference compatibility and past performance.

## Open Source Components

We believe in giving back to the community. We've open-sourced several components:

- **Preference Parser**: NLP library for extracting structured data from feedback
- **RAG Utils**: Helper functions for vector database operations
- **Briefing Templates**: Customizable templates for generating crew instructions

Check out our [GitHub repository](https://github.com/maidlyai) to contribute or learn more.

## Conclusion

Building an AI memory system for home services required solving unique challenges around privacy, context, and real-world reliability. The result is a system that truly learns and improves with every interaction.

We're just getting started. As we expand beyond Dallas, we're excited to see how our AI memory system performs across different markets and customer preferences.

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*Want to dive deeper into our technical architecture? Join our engineering team! We're hiring across AI/ML, full-stack, and data roles. [View open positions](/careers).*