Yeah — your instinct is solid here, Brett.
For the kind of **multi-category, fast, local, high-volume** classification system you’re building, training your own model on your own labelled data will almost certainly give **better and more controllable results** than trying to stretch an off-the-shelf model.

Let’s break this down clearly — especially what **sentence-transformers** brings to the table and why it’s a good fit for your hybrid architecture.

---

## 🧠 What Sentence Transformers Actually Do

A **sentence transformer** is a model that converts a piece of text (e.g. subject line, short body snippet, sender info) into a **dense vector embedding** — basically a fixed-length numerical representation that captures its semantic meaning.

Once you have embeddings, classification becomes a **simple, fast** downstream problem:

* Instead of training a giant deep network,
* You train a **small, lightweight model** (like Logistic Regression, XGBoost, or a simple neural head) on top of those embeddings.

---

## 🚀 Why This Is a Great Fit for Your Email Sorter

| Sentence Transformers                                       | Why it matters for you                                                        |
| ----------------------------------------------------------- | ----------------------------------------------------------------------------- |
| **Pretrained models** already “understand” general language | So you don’t need massive datasets to get good results.                       |
| **Fast inference**                                          | Embedding generation can run on CPU or GPU and is easy to parallelise.        |
| **Stable + deterministic**                                  | Embeddings are consistent across runs (unlike LLM zero-shot answers).         |
| **Lightweight training**                                    | You can train a classifier on top with a few thousand labelled samples.       |
| **Supports multi-class** easily                             | Perfect for your 12 category types.                                           |
| **Re-usable**                                               | One universal model for all inboxes; just retrain the top layer occasionally. |

---

## 🏗️ How It Would Work in Your Pipeline

### 1. **Use the LLM once** to label your initial batch (e.g. 1.5k–5k emails).

This gives you your **bootstrapped labelled dataset**.

### 2. **Generate embeddings**

* Take **subject**, **sender domain**, and optionally a short **body snippet**.
* Pass through sentence transformer → get 768-dimensional vector (typical).
* Save those embeddings alongside labels.

### 3. **Train a classifier** on top

A lightweight model like:

* **Logistic Regression** (fastest),
* **XGBoost / LightGBM** (slightly heavier, more accuracy),
* or even a shallow **MLP** if you want.

This becomes your **universal email classifier**.

### 4. **Run in production**

* New email comes in → embed text → run classifier → get category + confidence.
* If below threshold → send to LLM for re-classification.

This gives you **LLM semantic power** at training time, and **ML speed** at runtime.

---

## 🧪 Practical Setup (Minimal)

```python
from sentence_transformers import SentenceTransformer
from sklearn.linear_model import LogisticRegression

# 1. Load a pre-trained embedding model
model = SentenceTransformer("all-MiniLM-L6-v2")  # fast, lightweight

# 2. Convert texts to embeddings
texts = [f"{subject} {sender}" for subject, sender in your_email_data]
embeddings = model.encode(texts, batch_size=64, show_progress_bar=True)

# 3. Train a lightweight classifier
clf = LogisticRegression(max_iter=1000)
clf.fit(embeddings, labels)  # labels come from your LLM bootstrap

# 4. Predict
new_embedding = model.encode(["Subject from new email"])
pred = clf.predict(new_embedding)
conf = clf.predict_proba(new_embedding)
```

* `all-MiniLM-L6-v2` is a fantastic starting model — small, fast, and surprisingly accurate.
* You can fine-tune the sentence transformer later if you want **extra precision**.

---

## 🧠 Why It’s Easier Than Full Fine-Tuning

You *don’t* need to train the transformer itself (at least not at the start).
You’re just training the **top layer** (classifier). That means:

* Training takes minutes, not hours.
* You don’t need huge GPUs.
* You can refresh or retrain easily with new data.

Later on, if you want to **fine-tune the transformer itself** (so it “understands emails” more deeply), that’s an optional next step.

---

## ⚡ Typical Results People See

* With 2–5k labelled samples, sentence transformer embeddings + logistic regression can hit **85–95 % accuracy** on email category tasks.
* Inference time is **<5 ms per email** on CPU.
* Works well for both generic and user-specific inboxes.

---

## 🪜 Suggested Path for You

1. Use your **LLM pass** to generate labels on your first big inbox.
2. Generate embeddings with a pretrained MiniLM.
3. Train a logistic regression or XGBoost model.
4. Run it on the next inbox → see how it performs.
5. (Optional) Fine-tune the transformer if you want to push performance higher.

---

👉 In short:
Yes — sentence transformers are **perfect** for this.
They give you **semantic power without LLM overhead**, are **easy to train**, and will make your hybrid classifier **extremely fast and accurate** after that first run.

If you want, I can give you a **tiny starter training script** (30–40 lines) that does the embedding + classifier training from your first LLM-labelled dataset. Would you like that?