Making an AI RevOps GPT
A GPT tailored for RevOps, your RevOps
I’ve authored 50+ articles on revenue strategy, GTM architecture, compensation planning, attribution, and AI in RevOps.
All those insights are embedded into a local FAISS vector database.
When I need a polished client-facing Statement of Work, I type a quick prompt, like “B2B SaaS SOW for AI onboarding + GTM cleanup”.
The system retrieves the most relevant content from the library.
GPT drafts the full SOW—ready for customization.
About two to three cents per SOW, and just minutes to produce. The longer I publish, the smarter it gets.
Here’s What I Built
FAISS for fast similarity search
OpenAI embeddings for secure, local vectorization
Python (or LangChain) for the retrieval layer
GPT generates the final deliverable—easily swapped for another LLM
Hosted privately behind a Flask API, protected with rate limits when needed
Modular: drop new content into an input folder, run python append.py, and you’re updating your system with no retraining.
I use one vector DB to generate scalable SOWs—but this stacks for many other use cases:
Department handbooks & onboarding guides
Sales, CS, or marketing processes and example playbooks
High-impact email templates & personalization hooks
Pricing benchmarks and positioning strategies
Every new win embedded = evergreen intelligence. No fine-tuning, just incremental updates. It’s cheap, fast, local, and easy to transfer to a VPS.
Want to try it?
The full repo is open-source, runs locally, or can be hosted in the cloud. Fork it, drop in your own content and you’ve got your internal GTM brain in a weekend.
Google Drive won’t forget. Hype won’t fade. This is about RevOps working smarter—with speed, rigor, and scalability.
Email: dm@gtmharmony.com to learn more or explore customizing for your team.
Repo: github.com/DurangoDavid/SOW
AI doesn’t have to be complicated. If you’re still in the theoretical, you’re already behind those who ship.
