Core Service

Applied AI Systems

An applied AI system is a lightweight, production-ready tool built around a specific business outcome. It is not a chatbot, a generic automation, or an AI feature bolted onto existing software.

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What Is an Applied AI System?

It is a system that takes a real input, runs it through AI inference, applies your business logic, delivers a useful output, and learns from what happens next.

Every applied AI system has five components:

Input

A user, customer, or process provides a signal: a form response, an email, a purchase record, an uploaded document, or a selection.

Inference

AI interprets that signal. It classifies, extracts, scores, summarizes, or recommends based on what it reads.

Decision Logic

Your business rules shape the result: route this, escalate that, approve if X, flag if Y.

Outcome

Something useful happens: a recommendation is delivered, an order draft is created, a ticket is routed, or a salesperson is notified.

Feedback

The system tracks what worked through completion rates, response behavior, and conversion signals so it improves over time.

The result is not AI for AI's sake. It is a working system tied to a result.

Why Most AI Projects Fail

Most teams approach AI by starting with the model. They pick a tool, run a demo, get excited, and try to find a problem it solves. The project stalls because there is no clear outcome, no defined inputs, and no way to measure whether it is working.

Applied AI systems start from the opposite direction: outcome first, then inputs, then logic, then model. The technology serves the system, not the other way around.

This is why applied AI systems ship, perform, and improve over time when most AI projects do not.

What This Looks Like in Practice

B2B Replenishment Agent

A B2B distributor needed to recover reorder revenue without adding sales headcount. We built an AI agent with its own email address that monitors purchase history, calculates each customer's replenishment window, checks live inventory, and sends a personalized reorder email with a pre-built draft order at the right time.

When customers reply, the agent reads the response, handles routine questions autonomously, and routes anything requiring judgment, such as discount requests, complaints, or edge cases, to a salesperson.

That is the difference between an automation and an applied AI system. A standard replenishment flow sends an email at day 45. This system makes decisions.

What I Build

Applied AI systems fall into repeatable types. The architecture varies, but the methodology is the same: outcome first, lightweight by design, instrumented from day one.

Guided Decision Systems

Help users choose the right option through AI-assisted recommendations and structured paths.

Classification + Routing Systems

Turn messy, unstructured inputs like emails, form responses, and tickets into clean next steps.

Recommendation Systems

Match users to products, services, or resources based on intent, behavior, or context.

Content Interpretation Systems

Transform complex documents, records, or inboxes into useful actions, summaries, or comparisons.

Instrumented AI Pilots

Fixed-scope deployments with tracking, testing, and clear success metrics from day one.

Who This Is For

Applied AI systems are built for operators and founders who want to apply AI in a specific, practical way, not for teams chasing innovation theater or looking for a generic chatbot.

Operational teams with complex, repetitive decisions that currently require manual judgment.
Organizations with a clear workflow problem and a measurable outcome in mind.
Founders who want AI that runs in production, not just in a demo.
Teams that have tried automation and hit the ceiling of what rules-based logic can do.

If you can describe the decision you want AI to make, we can build the system around it.

How We Work

Systems Discovery

We identify the workflow, decision points, and where AI creates measurable improvement. You get problem framing, system scope, outcome definition, and recommended architecture.

Pilot Build

I design and deploy a lightweight applied AI system with clear boundaries and instrumentation. Production-ready, documented, and handed off cleanly.

Optimization + Expansion

We improve performance using real usage data and expand the framework into adjacent workflows with test plans and iteration roadmaps.

Articles: Applied AI Systems

Deep dives into what applied AI systems are, why most AI projects fail, and how to scope and deploy one that works.

The Operator Perspective

Why operators build better AI, what operations teaches about system design, and how to think about AI as infrastructure.

Ready to Build?

If you have a workflow that requires consistent judgment at scale and you want a system that handles it without adding headcount, let's talk.

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