A prototype takes weeks. Production ready consulting AI takes much longer.
Every firm can build an impressive AI demo. The harder problem is making it reliable enough for live client work, across Practices, workflows and changing models.
What actually has to be built
The hard part is the consulting layer. A production system needs more than an LLM and PowerPoint. It needs consulting workflows, Practice knowledge, research and analysis skills, evaluation, QA, revisions and reliable PowerPoint execution.
These are connected product problems.
- Consulting workflows
- Practice-specific knowledge
- Research quality
- Information hierarchy
- Complex layouts
- Evaluation and QA
- Iterative edits
- PowerPoint output
What you have to maintain
The work does not stop after launch. LLMs keep changing. Practices evolve. New edge cases appear. Standards need updating. Outputs need testing across workflows and revisions.
With an internal build, your team owns the AI product, infrastructure, evaluations and maintenance as they evolve.
What about a Claude Skill, or python-pptx?
A Claude Skill can provide instructions but it does not solve the harder questions: what the system should build, how consulting workflows should connect, how Practice standards should be applied, or how output should stay reliable.
python-pptx can generate PowerPoint files. It does not solve the consulting workflow, reasoning and evaluation layer. python-pptx also does not train by default on your unique style of decks.
Build if
You want AI itself to become strategic firm IP, have the engineering capacity to own, maintain and upgrade it long term, and want full control over the roadmap.
Buy if
You want consulting AI in production without building and maintaining the underlying workflow, domain layer and infrastructure yourself.