Applied AI Practitioner

Ana C. Sarmiento

I build AI products that drive growth and sharpen decisions.

I identify, build, and deploy custom AI that moves the P&L — turning public signal into revenue on one side, and into faster, better-informed calls for the people running the business on the other.

See the agents ↓ How I work ↓

The same engine, pointed two ways: public signal in, an agent layer that classifies and reasons with a human editorial check, and two things out — revenue growth and sharper decisions.

Agents

AI products, built and shipped — not slideware

Production-grade AI agents I've personally designed, built, and deployed at a national media network — spanning revenue intelligence, GTM engineering, and rapid prototyping. Hands on the keyboard, not directing from the sidelines. Every one of them exists to do one of two things: grow the top line or make a decision faster and better.

Outcome 01 — the top line

Growth I can point to

New SKUs, qualified pipeline, and content strategy that offsets platform disruption — agents that surface opportunity and turn it into revenue the business can book.

Outcome 02 — the judgment

Decisions made with more signal

Curated intelligence streams that keep newsroom and revenue leaders ahead of market moves — so the call gets made on evidence, in hours instead of weeks.

Production-grade

Live agents running against real workflows, not demos on a laptop.

Public data only

Built on free, open sources — capability that scales without a data bill.

Human-in-the-loop

AI classification paired with editorial judgment where it counts.

Memory-backed

A memory layer so briefings never repeat and context compounds over time.

The pattern

One engine, many jobs

Every agent runs the same underlying pattern — only the source and the judgment criteria change.

· Ingest a free, public signal

· Classify with AI, verify with a human

· Route to the P&L or to a decision-maker

Repeatable · Scalable · Auditable

Pillar 01 · Decisions

Intelligence Briefings

Automated, curated insight streams that keep newsroom and revenue teams ahead of industry and market signals.

· Trending News Agents

· Entertainment Intelligence Briefing

· Distributed Content Strategy

· Bot Monetization Strategy

Sharper, faster judgment

Pillar 02 · Growth

GTM Engineering

Sales-enabling agents that surface qualified leads, accelerate outreach, and drive SKU expansion.

· New Qualified Business Opening

· New Flight Route Alerts

· Information as a Service

Revenue the business can book

Pillar 03 · Validation

Rapid Prototypes

Testing new AI capabilities in short sprints to validate value before broader, more costly investment.

· The Programmatic Opportunity

· AI Contextual Chat

Prove it before you fund it

Applied AI

Where Strategy Meets Execution

Strategy that survives contact with production. Each of these is a lever I pull to move growth or improve the quality of a decision — hover any card for the receipts.

01 — AI Strategy

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AI Strategy

Offensive and defensive AI roadmaps built for organizations navigating platform disruption, algorithmic volatility, and P&L pressure. I personally develop and manage AI agents end-to-end — hands on the keyboard, not directing from the sidelines.

Hover to see focus areas

AI Strategy — Focus Areas

Selected Engagements

Offensive tactics to offset Google Zero and bot scraping at a leading publisher. Implemented new SKUs and architected autonomous content workflows.
Improved ML algorithms for personalization and recommendation engines by enabling v1 data warehouse, data lake, and data labeling migrations. Merged linear channels into a singular app codebase across web, mobile, CTV, and wearables.
Positioned compliance feature as a key product differentiator in a highly saturated digital signature market.

02 — Product Literacy

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Product Literacy

Equipping leadership teams with the mental models and vocabulary to make confident product bets in an AI-native media landscape.

Hover to see focus areas

Product Literacy — Focus Areas

Career Highlights

15+ years spearheading product development across 0-to-1 global products in media and publishing.
Built a retention framework adopted company-wide based on personalization ML. Pioneered the first A/B experimentation team focused on the reconnect cohort — boosting paid subscriber rates, conversion, LTV, and ARPU across the in-app experience.
On the original Disney+ Product launch team, leading the GTM approach for the US, CA, PR, and LATAM initial rollout. On the original HBO GO Product launch team, defining the early streaming strategy on first generation Apple, Roku, Samsung, and Xbox devices.

03 — Implementation

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Operational Implementation

From concept to production. Building the agentic pipelines, tooling, and cross-functional processes that make AI strategy real inside your organization.

Hover to see focus areas

Implementation — Approach

How I Work

Develop a cross-functionally aware proprietary Use Case Analysis Framework that maps to your operations to find the highest-impact AI opportunities.
Design and deploy agentic workflows, LLM pipelines, and data infrastructure that directly improve unit economics.
Build internal AI literacy programs so teams sustain and evolve solutions after engagement closes — no dependency created.

In The Room