Christopher Silva — GTM Engineer & Growth Specialist

I'm a technical growth builder. I ship outbound systems that run — and repair — themselves.

Self-healing, autonomous pipelines for outreach and outbound: they enrich, score, draft and follow up on their own, built with the same rigor as production data platforms — evals, observability, audit trails. The machine handles the reading and the follow-up. The humans only do the part humans are good at.

If you're hiring a pure SDR or a pure platform engineer, I'm not your guy. If you want one person who can run the discovery call and ship the pipeline behind it — keep scrolling.

One hard rule in every system I ship: a human approves every word that goes out.

Get in touch →

01 — The story

It started with cold calls.

Before any of the systems, I was on the phone — cold calling, finding the real pain point, closing. Every campaign hit the same wall: the systems behind it. Lists went stale, follow-ups slipped, data lived in ten places. I got tired of waiting for engineers to fix my pipeline, so I became one.

Then it got technical.

I went deep: data engineering on $5M Meta and eBay contracts — Snowflake, dbt, governed reporting platforms used to steer real budgets. That's where I learned that growth systems deserve the same rigor as production data systems: lineage, monitoring, evals, and rollbacks.

Now I build the machine.

Today I build autonomous GTM systems end to end — enrichment, ICP scoring, drafting, publishing, performance loops — operated with AI agents and gated by human judgment. The seller decides what to say. The system makes sure it happens, at scale, every time.

02 — Systems I've built

Discloser

AI · Real estate Visit ↗

AI disclosure analysis for California real estate. Reads 200-page disclosure packets and returns audit-ready findings with page-level citations — risk severity, cost estimates, Q&A.

2–4 minper packet — was 45–90 min by hand

200+ pagesread, every finding cited to its page

GTM Pipeline

Outbound · Data

Lead enrichment, ICP scoring and orchestration over a three-layer knowledge base — raw, structured, insight. Every claim traces back to a source. Self-healing ingestion, deterministic scoring.

165brokerages scored & enriched — 26 priority targets

436Klicensed-agent records pipelined for outbound

Content Engine

Autonomous · Content

An autonomous GTM content system: ingest sources → grade ideas → draft → human approval → publish → learn winners. A bounded background worker runs 6-hour cycles unattended; only A-grade ideas advance, and nothing publishes without sign-off.

6 hrautonomous cycles on a bounded worker

100%of published words human-approved — hard rule

Visibility Agent

AEO · Monitoring

Brand monitoring across ChatGPT, Perplexity, Claude and Gemini answers. Replaced browser scraping with direct LLM calls — tracks citations, mentions and source types per platform.

2sscan latency — down from 30s

−51%cost per 5K company scans ($594 → $290)

03 — Client engagements

Meta & eBay via DEPT

ongoing

Data engineering on $5M contracts. Built Masumi — a governed, code-first dashboard plane the reporting layer runs on. This is where the engineering rigor behind my growth systems comes from.

MattressStore.io AEO engagement

12 weeks · results-based

Answer-engine optimization with no retainer — I get paid when the numbers move. But that means I only take engagements I'm certain I can win. Built a knowledge graph from 28 of their YouTube videos, ran a first-party citation study across 60 AI-cited pages, and shipped a content engine that writes what AI engines actually cite.

California Real Estate field research → pipeline

2026

Two weeks shadowing licensed agents on live disclosure packets before writing a line of automation. That research became a 165-brokerage scored pipeline and a product agents now use in the field.

04 — Working with me

"Three trained agents skimmed past the same issue in a 200-page packet. The system I built flagged it in five minutes — she called the inspector on the spot."
Field session with a CA listing agent, May 2026

I show up in person.

I sat with agents in the field for two weeks before automating anything. Understanding is manual; execution is autonomous.

I love the phone.

Discovery, objections, closing — the parts most engineers avoid are the parts I'm best at. The systems exist so there's more time for this.

I ship with guardrails.

Evals, observability, draft gates, audit trails. Autonomy you can hand to a client without holding your breath.

What clients say

Testimonials are being collected — this space is reserved for the people I've worked with, in their own words.

Hello ✳ Chris