GEO Workflow: Optimize GTM Content with Frase and Profound

The GTM content game shifted hard when buyers started asking Perplexity “what’s the best sales engagement tool for a 10-person SDR team” and reading the answer instead of clicking your blog post. I’ve been inside enough RevOps orgs to watch perfectly optimized SEO content get bypassed entirely because it wasn’t structured for citation by an LLM. Generative Engine Optimization (GEO) is the discipline that fixes this. And right now, there is almost no operational tutorial coverage for how to actually run the workflow. This post is that tutorial, anchored on Frase with Profound as the feedback layer.

Ranking in Google is a distribution problem. Getting cited in an AI answer is an authority and structure problem. Most GTM content teams are still solving the wrong one.

The Framework: Two Layers, One Loop

Think of this as a two-layer loop. Layer one is content production and optimization, where Frase does the heavy lifting. Layer two is visibility monitoring and iteration, where Profound gives you the signal. Without layer two, you are publishing into a black box and hoping. Without layer one, you have monitoring data and nothing to act on.

Semrush for keyword discovery and Surfer for on-page density signals still have a role, but they feed into Frase rather than replace it. Semrush’s Keyword Magic Tool is excellent for finding the question-format queries that AI engines are most likely to synthesize answers for. Pull those into Frase as your brief foundation. Surfer’s Content Score can serve as a secondary gut-check on NLP coverage, but Frase’s own scoring is more granular for the answer-box structure GEO actually demands. Don’t build your workflow around Surfer as the primary driver. It wasn’t designed for this.

# Minimal GEO monitoring setup in Profound
tracked_queries:
  - "best sales engagement tool for SDR teams"
  - "how to build a RevOps tech stack"
  - "HubSpot vs Salesforce for B2B SaaS"

tracked_engines:
  - chatgpt
  - perplexity
  - gemini
  - claude

alert_threshold:
  citation_drop_pct: 20
  check_frequency: weekly

output:
  slack_channel: "#revops-geo-alerts"
  report: weekly_digest

The Step-by-Step Workflow

Here is how I run this for clients at Homegrown Growth Co., from query selection through the feedback loop. This assumes you have active Frase and Profound accounts and a content team owning at least four to six GTM-focused pieces per month.

Where the Supporting Tools Fit

Frase and Profound are the core loop. Semrush and Surfer play supporting roles. I’m precise about this because I’ve watched teams overcomplicate this stack and end up with four tools doing overlapping jobs with no clear owner and no one accountable for outcomes.

Surfer’s Content Editor is genuinely useful as a secondary check after you hit your Frase score, particularly for catching NLP term density that tips into keyword stuffing territory. But it is a check, not a driver. Semrush’s Keyword Magic Tool is irreplaceable for question-format query mining in step one, especially the Questions filter combined with intent segmentation. After that, it goes back on the shelf until the next content planning cycle.

Making This Stick Operationally

The teams I’ve seen get real GEO traction treat this as a RevOps workflow, not a one-time content initiative. That means owning the Profound alert channel in Slack, assigning a named DRI to the weekly iteration review, and connecting GEO citation rate to pipeline metrics the same way you track organic traffic to MQL conversion. BrightEdge research on AI search impact consistently shows that teams with a structured feedback loop outperform those running ad hoc refreshes. The tooling is mature enough now. The differentiator is the operational discipline around it.

Build the loop. Run it for 90 days. You will have proprietary GEO performance data your competitors almost certainly do not have yet. That compounding advantage is the actual opportunity.