<div class="portada"><div class="meta">Menteia · Copiloto Aero · October 2026 · Confidential</div>

# Executive summary

**Copiloto Aero** — AI quoting cockpit for aerospace suppliers: reads requests and drawings, prepares the quote with the company's pricing engine, and the analyst approves every section.</div>


> Quote in hours what takes days today, without losing control.

**In one line:** AI quoting cockpit for aerospace machine shops


![Copiloto Aero: cover of copiloto.indumet.org](capturas/01.jpg)

## The problem
- Aerospace suppliers receive requests for quotation (RFQs) with drawings, specifications and materials; preparing each one takes an expert analyst days.
- The knowledge needed to quote lives in the heads of a few people and in thousands of past quotes that nobody looks up.
- Part number, material or tolerance errors cost orders or margin; and generic AI is not reliable with numbers.

## The solution
Copiloto Aero turns every request email into a quoting session: it triages the drawing with computer vision, looks up past quotes, runs the company's pricing engine and presents each section with its rationale so the analyst can approve, ask or adjust by text or voice. It generates the PDF and the email to the customer, but nothing goes out without human approval and the LLM never makes up a number.

- Each RFQ email becomes a session linked to the CRM (Pipedrive) and the company's quoting engine.
- Drawing triage with vision: detects discrepancies (e.g., the part number in the email differs from the one on the drawing).
- Knowledge base of past quotes (929 quotes, 4,600 line items at the anchor customer).
- The analyst approves, asks or adjusts each section by text or voice; human gates at technical review, pricing and sending.
- Customer-branded quote PDF, clarification email to the customer and a global command dashboard.

## Why we win
- Zero LLM-generated numbers: every figure comes from the pricing engine with its source; the AI explains.
- Human at every critical gate (technical, pricing, sending): designed for regulated industries.
- Learns from the company's history: past quotes as context, with opt-in learning.
- Sits on top of what the customer already uses (email, CRM, pricing engine) instead of replacing it.

## Traction
- In production at INDUMET (aerospace quoting): real inbox, live CRM and historical data ingested.
- 929 past quotes, 4,600 line items and 29 historical customers as the knowledge base.
- 7 INDUMET team members operating the cockpit.

## Market
| Level | Annual size | How it is calculated |
| --- | --- | --- |
| TAM | US$45 M | ~3k precision manufacturing and distribution suppliers in Mexico that quote against drawings (aerospace, medical, premium automotive) × US$15k/year |
| SAM | US$5.8 M | 386 aerospace companies in Mexico × US$15k/year |
| SOM | US$250k | ~11 companies in 24 months with a ticket of ~US$22k/year |

## Business model
One-time implementation (integration with email, CRM and pricing engine + ingestion of historical data) and a monthly subscription per company based on users and quote volume.

## The round
| Item | Proposal |
| --- | --- |
| Amount to raise | US$575,000 |
| Equity offered | 24.2% |
| Pre-money valuation | US$1,800,000 |
| Post-money valuation | US$2,375,000 |
| Runway | 24 months (excluding revenue) |
| Suggested instrument | Post-money SAFE (the YC standard and used in ~93% of US pre-seed rounds, according to Carta) |

**With these funds we will achieve:**
- Multi-customer product with a generic pricing connector
- ~11 aerospace suppliers in production
- First US customer and medical vertical
- Monthly break-even

## Team
- **Rafael Herrera** — CEO / CFO
- **Vladimir Díaz** — CTO
- **Francisco D'Angelo** — Head of Legal and Compliance

**Contact:** Menteia · copiloto.indumet.org