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TCT-003 · Internal tooling · Content ops · Live · in production

TC Tools

Backend toolkit inside TechCentral for content creators — AI image generation, copy analysis, analytics, and debug.

Cuts content-production time and gives editors visibility into what techs actually use.

Context

TC Tools lives inside TechCentral as the surface where content editors and operations staff actually do their work — drafting procedures, generating supporting imagery, analyzing copy for clarity and brand fit, and seeing what’s landing with the technicians using the platform.

The premise: the people writing the procedures shouldn’t need to file an engineering ticket every time they want a diagram, or want to know whether anyone actually reads what they ship.

What’s in the toolkit

  • AI image generation for procedure illustrations, with reusable templates and brand-consistent prompts. Editors get usable imagery in seconds rather than waiting on a designer for one-off explainers.
  • Copy analysis — readability, voice, and brand-fit checks before content goes live. Surfaces drift before the publish button rather than after.
  • Analytics — which articles are being read, by whom, when, and how often. The unglamorous metrics that tell you whether the content actually helps the technicians using the platform.
  • Debug — internal-only views for diagnosing access issues, content state, and edge-case rendering without involving engineering for every ticket.

AI image generation — procedure-illustration prompts and brand-consistent outputs.

How the AI piece is built

The AI surfaces are intentionally narrow. Image generation runs against a small, vetted set of prompt templates rather than a free-text playground — output is more predictable, brand fit is enforced, and the cost surface is bounded. Copy analysis uses a lightweight evaluation pipeline so the same checks run consistently between drafting and publishing. The whole AI layer is provider-agnostic by design — the underlying model is a configuration switch, not a rewrite.

Article analytics — read counts, role breakdown, and which procedures are getting silently ignored.

More coming

The full case study is being written. Status, stack, and headline are accurate. For specifics on the AI integration approach, the analytics model, or what debug surfaces look like in practice — book a call.

Got a project like this?

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