Project · Delivered
LLM SEO and RAG services
Two enterprise proofs of concept exploring governed content generation, webpage auditing, quality scoring, and BigQuery-powered retrieval.
- Role
- Solution design and implementation
- Context
- Enterprise proofs of concept
- Status
- Delivered
Problem
Enterprise content teams needed to understand where LLM generation could improve real workflows without separating generation from audit, retrieval, and quality control.
Insight
The strongest use case was not simply “write content.” It was a connected service that could inspect a webpage, generate a candidate, score quality, and use enterprise data to support the result.
Decision
Explore two bounded proofs of concept: an LLM-based SEO content service spanning audit, generation, scoring, and image-text generation; and a BigQuery-powered RAG workflow for high-volume text generation testing.
Implementation
Austin developed and deployed the SEO proof of concept and designed the BigQuery-powered RAG workflow. Public details are limited to approved scope and outcome evidence.
Evidence
- More than nine business units participated in the SEO content-service proof of concept.
- More than 15 users and teams participated.
- The workflow covered webpage auditing, content generation, quality scoring, and image-text generation.
- The BigQuery-powered RAG proof of concept generated more than 10,000 texts during testing.
Reflection
The work reinforced that generated volume is not the same as quality. Auditability, retrieval design, and scoring need to sit beside generation if the result is going to support an enterprise decision.
Evidence on record
What can be checked now.
- More than nine business units participated in the SEO service proof of concept
- More than 15 users and teams participated
- The BigQuery-powered RAG proof of concept generated more than 10,000 texts during testing