AI SEO Agency Deliverable: The Margins Math

An r/AI_Agents post asked: how do agencies integrate AI SEO services into automated workflows? The answer that keeps margins healthy is a productized monthly deliverable, not a custom build per client. The math depends on the cost basis being a fraction of the recurring price.

The deliverable

Per client, monthly: keyword tracking across 30-50 brand and category terms, AI Overview citation audit (which AI Overviews cite the brand), Reddit thread mention tracking, and 5 content gap outlines based on competitor coverage the brand has not addressed.

The cost basis

• Scavio: $30/mo for 7,000 credits. Daily 50-keyword pull = 1,500/mo, leaves headroom.
• LLM (Claude Haiku 4.5 or DeepSeek): $5-15/mo per client at typical gap-analysis volume.
• Otterly dashboard: $29/mo for the polished visualization layer.
• Total cost basis per client: ~$65-75/mo.

The pricing tiers

Solo agencies sell at $99/mo for one-time audits or $199-499/mo for monthly retainers. Mid-market agencies that bundle with content production sell at $1,500-3,000/mo. The data layer is the cost base; the labor (review, client comms, content production) is where revenue scales.

Why $499/mo works

$499/mo recurring with $75/mo cost basis is 85% gross margin before labor. After labor (1-2 hours per client per week at agency rates), net margin lands in the 60-70% range. Scalable to 30-50 clients per agency lead before the labor breaks down.

Why $99/mo Profound does not work for solo agencies

Profound's Lite tier starts at $499/mo for the agency seat. If your client deliverable is also $499/mo, your margin is gone before labor enters the picture. Profound wins for enterprise clients where the dashboard polish is part of the sale; it loses for solo agencies on cost grounds alone.

The Scavio piece

include_ai_overview: true on a Scavio search query returns the AI Overview citation list inline with SERP results in one call. /reddit/search returns Reddit thread mentions with engagement metadata. Two endpoints cover 80% of the deliverable's data layer.

import os, requests
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}

def audit(client_keywords):
    out = {}
    for k in client_keywords:
        s = requests.post('https://api.scavio.dev/api/v1/search',
            headers=H, json={'query': k, 'include_ai_overview': True}).json()
        r = requests.post('https://api.scavio.dev/api/v1/reddit/search',
            headers=H, json={'query': k}).json()
        out[k] = {
            'serp': s.get('organic_results', [])[:10],
            'citations': (s.get('ai_overview') or {}).get('citations', []),
            'reddit': r.get('posts', [])[:10],
        }
    return out

The labor that does not automate

Reviewing the LLM's gap-analysis output before it ships to the client. Client comms (kickoff, monthly call, tactical Q&A). Content production if you bundle that. Quality control on the dashboard before each report goes out. These eat 1-3 hours per client per month and are the actual constraint on how many clients an agency lead can run.

The honest tradeoff

Productized AEO deliverables work for clients in the mid-market range ($5K-50K/mo marketing budgets). Smaller clients churn because $499/mo is a meaningful chunk of their budget; bigger clients want custom strategy that does not fit a productized template. Niche down to one industry to keep the deliverable repeatable.

What this means for the OP

Pick one industry. Build the audit deliverable in a week with Scavio + LLM + Otterly. Sell at $299-499/mo. Run 5-10 clients before scaling. The integration question the OP asked is real; the answer is "productize, do not custom build per client."