Embeddings API Selling to the ML Engineer — 60-Min Training

Question

Pulse RevOps · The Machine · Accepted Answer

### Direct Answer

> **Embeddings API Selling to the ML Engineer** is a 60-minute training for AEs running $30K–$300K ACV cycles against **OpenAI text-embedding-3, Cohere embed-v4, Voyage AI, Google Gemini Embedding, BAAI bge open-source**. Qualify against **ML Engineer + Head of Data + CFO**, run discovery on **MTEB performance + multilingual + Matryoshka + cost**. Built on **MEDDPICC**.

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## Section 1 — Why Embeddings API Selling Is Different (5 min)

Embeddings are evaluated on **task-specific benchmark scores**, not marketing claims.

End with **Mark Roberge's** rule: *"Sell the customer's NDCG@10 lift on their corpus."*

---

## Section 2 — The 60-Minute Discovery (15 min)

> 1. **Opening (3 min):** "Walk me through your RAG corpus + retrieval architecture."
> 2. **MTEB benchmark posture (10 min):** "Aware of MTEB leaderboard? Current scoring?"
> 3. **Multilingual posture (10 min):** "Global product? Cohere multilingual leader."
> 4. **Matryoshka adoption (10 min):** "Cost-optimizing via dimension truncation?"
> 5. **Volume baseline (8 min):** "Monthly tokens embedded?"
> 6. **Self-host vs API (7 min):** "5B+ tokens/month tips toward bge self-host."
> 7. **Renewal posture (5 min):** "Existing contracts?"

```mermaid
flowchart TD
    A[AE Discovery] --> B[Pre-Brief]
    B --> C{ML Eng + Data + CFO?}
    C -->|No| D[Reschedule]
    C -->|Yes| E[MTEB + Multilingual 20 min]
    E --> F[Matryoshka + Volume 18 min]
    F --> G[Self-Host + Renewal 12 min]
    G --> H[POC 5 Days]
```

---

## Section 3 — The POC That Wins (15 min)

**Customer's labeled relevance set ingested.** **NDCG@10 + MRR side-by-side scorecard.** **Matryoshka cost-saver calculator.**

---

## Section 4 — Handling the Incumbent (10 min)

**MTEB wedge.** **Matryoshka wedge.** **Multilingual wedge.**

---

## Section 5 — Pricing Conversation (10 min)

**Landmines:** per-token vs per-call. Multi-year discount. No procurement-only meetings.

```mermaid
flowchart TD
    A[Joint ML + Data + CFO] --> B[Per-Token Proposal]
    B --> C{Discount?}
    C -->|Yes| D[MSA]
    D --> E{Procurement Solo?}
    E -->|Yes| F[Refuse]
    E -->|No| G[Joint Neg]
    F --> G
    G --> H[Onboarding 3 Days]
    H --> I[NDCG Lift Month 1]
    I --> J[Quarterly Cost Review]
```

---

## Section 6 — The Trap-Set for Renewal at Month 12 (5 min)

NDCG@10 lift, Matryoshka adoption, multilingual coverage, joint dashboard. Close with Blount: *"Renewal sold day one."*

---

## FAQ

**OpenAI or Cohere?** OpenAI ubiquity; Cohere multilingual.
**Voyage for domain?** Yes — code, legal.
**Self-host bge?** 5B+ tokens/month.
**Matryoshka critical?** Yes for storage cost.
**MTEB the benchmark?** For short-listing.

## Sources

- MTEB — Massive Text Embedding Benchmark
- OpenAI — text-embedding-3 Documentation
- Cohere — embed-v4 Documentation
- Voyage AI — Reference
- Google — Gemini Embedding 2
- BAAI — bge-large Reference
- Force Management — MEDDPICC
- Mark Roberge — Sales Acceleration Formula
- Andy Paul — Sell Without Selling Out
- Jeb Blount — Fanatical Prospecting

Embeddings API Selling to the ML Engineer — 60-Min Training

Direct Answer

Section 1 — Why Embeddings API Selling Is Different (5 min)

Section 2 — The 60-Minute Discovery (15 min)

Section 3 — The POC That Wins (15 min)

Section 4 — Handling the Incumbent (10 min)

Section 5 — Pricing Conversation (10 min)

Section 6 — The Trap-Set for Renewal at Month 12 (5 min)

FAQ

Sources

What does the score mean?