I Build Applied AI Systems for Measurable Outcomes
I design lightweight AI systems that help organizations make better decisions, improve workflows, and become discoverable to language models. Every system is built for real-world use: clear inputs, useful outputs, built to improve over time.
Book a Discovery CallMost teams are stuck between two extremes: AI hype with no practical application, and basic automations with no strategic value.
I build the middle layer: applied AI systems. Lightweight, production-ready tools that combine user input, AI inference, decision logic, and measurable outputs into something that actually works.
The result is not AI for AI's sake. It is a working system tied to an outcome.
Four Areas of Practice
Custom, lightweight tools built around a specific business or operational outcome: classification systems, recommendation engines, guided decision tools, intake and triage systems, and AI-assisted workflows.
I help organizations structure their content so modern AI models can correctly understand what they do, what they offer, and when to recommend them. Semantic structure, entity clarity, and machine-readable content patterns.
Visibility is being readable. Discoverability is being chosen. I design systems that improve how your organization appears in AI-assisted search, answer engines, and agent-driven workflows.
Every applied AI system should produce learning, not just output. I build instrumentation in from the start so you can measure usage, drop-off, confidence, and recommendation performance, then test and improve.
The Applied AI Systems Framework
This is the repeatable architecture I use across every project.
If language models cannot understand you, they cannot recommend you.
Search behavior is changing. Users are increasingly asking AI systems to compare options, recommend vendors, and guide decisions. If your content is not structured for LLMs, you lose visibility. If your information is not clear and retrievable, you lose discoverability.
I help organizations build for both.
Built for Teams That Want Outcomes, Not AI Theater
I work with organizations that want to apply AI in practical ways without bloated infrastructure or vague innovation projects.
What I Build and Deploy
Help users choose the right option through AI-assisted recommendations and structured paths.
Turn messy inputs into clean next steps using AI and business rules.
Match users to products, services, or resources based on intent, description, or context.
Transform large or complex content into useful actions, summaries, or comparisons.
Improve how your organization is interpreted and surfaced by AI search and answer engines.
Fixed-scope AI deployments with tracking, testing, and clear success metrics built in from day one.
Built Around the Outcome. Not the Model.
A lot of AI work fails because it starts with the technology. I start with the decision, the workflow, and the measurable result.
Three-Phase Engagement
We identify the workflow, decision points, and where AI can create measurable improvement.
I design and deploy a lightweight applied AI system with clear boundaries and instrumentation. Production-ready, documented, and handed off cleanly.
We improve performance using real usage data and expand the framework into adjacent workflows.
David Valencia
I'm a full stack developer and systems thinker focused on applied AI systems, LLM visibility, and discoverability.
I build lightweight systems that help organizations apply AI in the real world with clear inputs, practical decision logic, and feedback loops that improve over time. My background in technical execution, analytics, and digital systems shapes how I approach AI today: not as a trend, but as infrastructure.
Book a Discovery CallBuild an AI System That Actually Improves an Outcome
If you're done with AI demos, vague automation talk, and disconnected experiments, let's build something operational.