AI Operations Lab

The business problem

An inbox mixes different kinds of work together. A ready-to-sign prospect sits beside a renewal complaint, an invoice question, and an ordinary follow-up. Reading every message is only the beginning. Someone still has to decide what matters, find the context, prepare a response, and check it.

The AI Operations Lab makes that transformation visible. It is an interactive demonstration of triage and drafting, not a claim of measured client savings. Try the lab to explore the fictional example or analyze your own redacted messages with an Anthropic API key.

What the visitor can do

• Load five fictional business messages and inspect a prewritten example of the resulting work queue.
• Run a live analysis on up to eight messages with their own provider key.
• Review actions grouped into commercial follow-up, customer retention, and internal operations.
• Inspect the original message and a supporting quote for every proposed action.
• Edit a response draft and mark it reviewed locally.
• Explore a capacity scenario by changing workload and handling-time assumptions.
• Export a Markdown work plan that includes drafts, evidence, source messages, and review state.

Verification outside the model

The live model returns structured data. Application code then validates its shape, field lengths, allowed classifications, source identifiers, and coverage. Each source must appear exactly once. Every supporting quote must be an exact passage from the source it names.

This is a narrow, useful check: it catches invented citations and unsupported source references. It does not establish that a sender's claim is true or that the model's interpretation is correct. The interface makes that distinction visible and keeps every action behind human review.

A bounded live integration

Live mode makes one Claude Haiku 4.5 request, uses the visitor's API key, caps output at 4,000 tokens, and disables automatic retries. The endpoint bounds request size, limits input length and message count, applies a timeout, and does not provide the model with tools or account access.

The application does not persist API keys, input messages, or generated plans. The key field clears when submitted. The server does not log model request bodies or provider error objects. The data handling page explains the limits of this design and the role of the hosting and model providers.

Honest business outcomes

The capacity calculator is deterministic arithmetic using visitor-controlled assumptions. It compares manual handling time with review time and shows the formula. If review takes longer, it shows a negative result. It does not turn a hypothetical improvement into a claim of recovered revenue, profit, or actual time savings.

That is how I would begin a business engagement: identify the work, measure the baseline, trial a bounded system, and compare the result. A production implementation would need authenticated integrations, agreed policies, shared monitoring, an evaluation set, and a carefully scoped action layer.

Bring this into your workflow

The lab demonstrates the middle of an operations system. A production project could connect the input to an inbox, CRM, or help desk and route reviewed output to the right person. The connections and permissions would be designed for the particular business.

Explore AI automation, LLM integration, or contact me to discuss your own workflow.