# Héctor Moyano Vélez

> This file is written for AI agents and recruiters’ assistants. It holds the same facts as the website, in plain Markdown. Figures marked “estimate” are estimates; everything else is measured.

- **Role:** AI Engineer
- **Based in:** Madrid, Spain
- **Email:** hector@hectormoyanovelez.com
- **Website:** https://hectormoyanovelez.com
- **X:** https://x.com/hector14mv
- **GitHub:** https://github.com/hector14mv
- **LinkedIn:** https://linkedin.com/in/hector14mv
- **Other language:** https://hectormoyanovelez.com/perfil.md

## About

I build products with AI agents, from idea to production, and the system that makes what those agents write trustworthy.

I come from data: analyst, then data engineer. Today I run several AI engines that write and cross-review code across three products of my own and client work.

## Selected work

### 1. Respondo

*Own product · Year 2026 · Status: Pre-launch: onboarding the first businesses*

When a service business can’t pick up the phone, Respondo messages the customer on WhatsApp, books the appointment and sends the invoice.

**What it is.** A platform for service businesses that turns every missed call into a WhatsApp conversation that ends in an appointment and an invoice. The AI converses; appointments are confirmed by the system, never by the model.

**Problem.** Small businesses lose customers on every call they miss. Replying by hand, booking and invoicing eat hours they don’t have. And from 2027 invoices must comply with VERI*FACTU.

**Solution.** The missed call becomes a WhatsApp conversation from the business’s own number, on the official WhatsApp API, approved by Meta. A multi-tenant platform with each business isolated in the database, and its own VERI*FACTU invoicing engine, in validation with the tax agency.

**Results:**
- Meta: WhatsApp API approved
- 657: merged PRs
- 525 of 825: commits co-authored with AI

**Stack:** Next.js, TypeScript, Supabase, WhatsApp Cloud API, Twilio, Stripe

**Link:** https://respondo.es

https://hectormoyanovelez.com/proyectos/respondo

### 2. Bot for court agents

*Client work · Year 2026 · Status: In production since April 2026*

Files briefs and claims in four court portals for a firm of court agents.

**What it is.** Files briefs and claims in the court portals of the Basque Country, Aragon, Cantabria and Navarre for a firm of court agents. It prepares, validates and signs; the agent keeps control and responsibility.

**Problem.** The firm filed thousands of documents by hand on several regions’ justice portals: find the case, fill in, sign, submit, keep the receipt. Repetitive and error-prone.

**Solution.** The bot automates the whole portal flow, electronic signature included. Key decision: it only counts a filing as done once it holds the portal’s own receipt.

**Results:**
- 10,175: filings since April 2026
- 9,159: end to end, with the portal’s receipt
- 2,300–3,000 h: saved (estimate)

**Stack:** Python, Playwright, FastAPI, PAdES signing

https://hectormoyanovelez.com/proyectos/bot-procuradores

### 3. Gaela

*Own product · Year 2025 · Status: Web beta*

AI-assisted study for Spain’s prison service exams, anchored to the literal text of the law (BOE).

**What it is.** An AI study tool for exam candidates. Every question and explanation rests on the current BOE article, with its source.

**Problem.** In a public exam, a question with the wrong legal answer is worse than no question. AI generators invent with confidence, and the candidate can’t tell.

**Solution.** Every question is anchored to the current BOE article. An AI chain generates, reviews, explains, audits and adjudicates it. Key decision: model confidence never counts as proof that a question is correct.

**Results:**
- 22: BOE laws
- 5,386: articles
- 1,300+: official exam questions

**Stack:** React Native, Expo, FastAPI, Supabase, Claude API

**Link:** https://app.gaela.app

https://hectormoyanovelez.com/proyectos/gaela

### 4. Pentaho to dbt

*Client work · Year 2024–2026 · Status: StrateBI, for a major automotive finance company*

An 11-agent AI system that migrates Pentaho transformations into dbt models on Snowflake.

**What it is.** I design and lead a system of specialised agents, chained with Claude Code, that migrates a legacy Pentaho data warehouse to dbt and Snowflake.

**Problem.** Migrating a Pentaho data warehouse to dbt is manual, one transformation at a time: translate the SQL, resolve dependencies, validate. Weeks per batch.

**Solution.** Agents for analysis, dependencies, SQL translation, generation, validation and docs. Key decision: the system stops and asks at critical points (unknown variables, custom database functions, missing tables) and learns from every migration through a lessons log.

**Results:**
- 11: specialised agents
- 85+: dbt models (per the project)
- Weeks → hours: per migration (estimate)

**Stack:** Claude Code, Pentaho, dbt, Snowflake, SQL, Multi-agent systems

https://hectormoyanovelez.com/proyectos/migracion-pentaho-dbt

### Also

- **Agents hackathon · Causa Prima × Nova** — The Bazaar (Madrid, October 2026): autonomous agents negotiating and trading against 17 other teams’ agents. With Thiago Amaro: 4th of 18 teams and 1st in agent negotiation. https://github.com/thiagoamaro91/negotiation-agent
- **Premios Goya 2026** — A site to follow the Goya season at Sala Berlanga: programme, films watched and predictions. https://premios-goya-2026.vercel.app

## How I work

I direct AI agents from two model families. None of them approves its own work, and review grows with the damage a change can do.

### The four steps

1. **Specify.** Every night the plan is written. In the morning tickets are grouped, each with its damage tier (T0, T1, T2) and its engine. I only answer the product questions.
2. **Implement.** An agent implements from a preflight sheet, with tests that are first seen failing. When it is done it freezes the commit and stops: it never reviews its own work.
3. **Review.** The other model family reviews the frozen commit. A T2 change gets three layers: a gate, a model that tries to break the conclusions, and a final reviewer.
4. **Merge.** Only the approver merges, and only the exact SHA it reviewed. No review, no merge: silence never approves.

### One real ticket (replay of 28 September 2026)

RESP-800 · PR #621 · T2 · SMS consent and opt-out in Respondo

- night · **Planner** (Plan): The day’s briefing with the open tickets.
- **Conductor** (Plan): Tier T2: it touches message sending and consent.
- **Astra** (Finding): Attacks the plan’s tiers, criteria and routing before work starts.
- **Héctor** (Decides): A 30-second plan, five questions at most. OK.
- **Implementer** (Build): Preflight: surface, gates, tests and Héctor’s two-minute check.
- **CI** (Build): Every test is seen red under a mutation before it goes in. CI green.
- **Implementer** (Build): Ready for review: the commit is frozen and the session stops.
- 11:36 · **Gate · Codex** (Gate): Round 1: clean.
- 11:42 · **Astra** (Finding): P1 with an executed repro: if a customer opted back in and texted STOP again, that opt-out was lost.
- 11:50 · **Conductor** (Build): A fresh fix session, with a closed list of file and line.
- 12:18 · **Héctor** (Decides): The migration grows, so it comes back to me. I decide at 12:24.
- 12:40 · **Gate · Codex** (Gate): Round 2 on the new frozen commit.
- 13:40 · **Héctor** (Decides): OK to the production migration.
- 13:41 · **Fable** (Approves): APPROVE and merge on the exact SHA. The opt-out is no longer lost.

### Measured

- **126** PRs with a damage tier and cross-family review, 18–30 September 2026
- **311** real findings caught before merge in those PRs
- **22** PRs merged on 28 September, with 37 findings

## Training I give

- **Development teams.** AI-assisted development with Claude Code and multi-agent systems. The AI implements and an independent session reviews before merging. (525 of 825 Respondo commits co-authored with AI)
- **Business teams.** AI adoption workshops to spot inefficiencies and build solutions that stick. (In-house AI-assisted development workshop for the technical team and management at StrateBI)

## Tools

Claude Code, Claude Agent SDK, RAG, Multi-agent systems, Next.js, React Native, TypeScript, FastAPI, Python, Playwright, PostgreSQL, Supabase, Stripe, dbt, Snowflake, AWS Glue, Power BI

## Contact

Want to collaborate or have an idea? hector@hectormoyanovelez.com