Frequently asked questions

A traditional content agency gives you people—writers, editors, project managers—who produce content using their own judgment and your briefs. Output quality depends on who's assigned to your account, and the institutional knowledge walks out the door when the engagement ends.

The Foundry is different in three specific ways. First, it's built around your brand—the system is trained on your voice, your history, your standards, so output is consistent in a way that doesn't depend on which writer picks up the brief. Second, the infrastructure is yours. When the engagement ends, you take the system—the brand documentation, templates, workflow, quality standards—and run it in-house. You're not renting capacity, you're building an asset. Third, the output per engagement is higher because of the multi-format production model—one brief produces a full case study, a short version, social copy, an award submission, and an RFP pull. An agency bills for each of those separately.

Yes, on both counts. The content produced in every engagement is yours. So is the infrastructure built around it—the brand voice documentation, templates, editorial standards, and workflow design. Everything is built for handoff. You can run it in-house whenever you're ready.

It depends on your environment and workflow. For content generation I lean on Claude. For automation—publishing workflows, approvals, content routing—I work with whatever your team already uses: Power Automate for Microsoft shops, n8n or similar for others. A second LLM handles adversarial quality audits, checking output independently before anything gets published. If you have strong tool preferences or an existing stack, I'll work within it.

Giving your team ChatGPT access gets you faster individual output—not a content operation. Everyone produces their own material slightly faster, but nothing accumulates. No consistent voice, no shared standards, no institutional knowledge building over time. AI at the edges isn't infrastructure—it's freelancers moving a little quicker.

What makes The Foundry different isn't the AI—it's the editorial judgment built around it. Twenty years of writing for enterprise software companies informs every brief, every template, every quality standard in the system. The AI executes. The understanding of what enterprise content needs to do—for human buyers and for the AI agents now shaping what buyers find—isn't something a ChatGPT license provides.

It means AI takes a back seat to human judgment. The briefs, the templates, the quality standards, the editorial review—those come from twenty years of writing for enterprise software companies, not from a model. The AI executes within a structure built by a writer who understands what enterprise content needs to do. When output doesn't meet the bar, a human catches it—not another prompt.

Every engagement is built on a detailed brand voice and style guide loaded into the context of every writing project—so the AI is working within your standards from the first word, not approximating them. I also maintain an active list of common AI writing habits—the phrases, rhythms, and constructions that signal machine-generated copy—and the system is directed to avoid them from the start.

From there, every draft goes through four stages before it's published. I read it first. A verification phase checks factual accuracy against provided source materials only—nothing is inferred or hallucinated. An adversarial audit using a second LLM tests for voice violations and flags anything that doesn't meet the bar. Flagged content is corrected, either automatically or by hand, before the piece moves forward. The short answer: I read everything, and the system is designed to catch what I miss.

Capacity scales with the engagement. For Foundry engagements—where the system is built, the brand voice is loaded, and source materials are organized—output exceeds one finished asset per day, across multiple formats. For standalone long-form work like white papers and technical guides, typical turnaround is two days per piece. For larger programs, I bring in a network of writers, a technical specialist, and a strategist as needed. The core editorial judgment and quality standard stays consistent regardless of scale.

My deepest experience is in enterprise software and technology—SAP, HP, SUSE, Unisys, Teradata, and others. I also have substantial work in life sciences and pharmaceuticals, financial services, and manufacturing. For other industries I evaluate on a case-by-case basis depending on the domain.