OUR METHOD
From invisible to indispensable.
Every engagement revolves around four commercial stages.
FOUND
We increase the places and moments in which your company can be discovered.
Search → AI → Media → Content → Paid → Social
TRUSTED
We establish the signals that make buyers—and increasingly machines—recognize your expertise.
Authority → Evidence → Expertise → Reputation
CHOSEN
We strengthen the message, offer and experience that turn attention into action.
Positioning → Experience → Conversion → Demand
GROW
We identify what works, improve it and build assets that become more valuable over time.
Data → Intelligence → Optimization → Growth
Found Advantage AI Dashboard
Our campaigns are powered by a proprietary SEO, AEO, and GEO growth system engineered for the next generation of digital visibility. Our current platform has evolved to Version 7.80, reflecting years of continuous development, refinement, and real-world deployment.
To give prospective partners a transparent view of our underlying approach, we provide access to the original Version 1.0 Python code at no cost—allowing you to evaluate the foundation of our technology before engaging our services.
To try the code:
pip install -r requirements.txt
python -m found_advantage demo # writes report.md
python -m found_advantage dashboard # http://127.0.0.1:8050
To run it on a real site, set ANTHROPIC_API_KEY and use python -m found_advantage run --name ... --domain ... --topics ..., adding a Search Console export so findings are weighted by real traffic. Before relying on live probe numbers, check that the current Claude model names and web-search tool version in Anthropic’s docs match the defaults in llm.py and probes.py.
Agents and What They Do
Strategy Director: Works out revenue per visit from your deal size, close rate and conversion rate. Flags missing conversion tracking, objectives with no number or date, and priority offers with no landing page.
SEO Strategist: Groups keywords that show the same search results into topics, labels each by buyer stage, and estimates the monthly revenue gap per topic.
Competitive Intelligence: Builds the Competitive Advantage Map: who leads each topic, your share, and the cheapest move to close the gap. Also finds rivals you aren’t tracking and compares backlink counts.
Editorial Director: Writes content briefs: question-shaped sections, the answer to give up front, the original data or example each page must include, internal links and schema.
LLM Visibility: Checks how AI engines describe the brand against your positioning, how many mentions are negative, and whether the brand is named but rarely recommended.
Citation Analyst: Finds sites that link to competitors but not you, with sites AI engines already cite ranked first.
Digital PR: Proposes original data studies, a statistics page and timely expert-comment pitches, with drafts. A person sends every pitch.
Local SEO: Checks Business Profile completeness, review pace and rating against local rivals, reply rate, and whether name, address and phone match across directory sites.
Ecommerce SEO: Checks product schema, product identifiers, reviews, out-of-stock pages, filter URLs that shouldn’t be indexed, and categories with no page.
CRO Specialist: Finds search-traffic pages that convert poorly, and pages with no call-to-action or proof.
Experiment Lead: Turns uncertain findings into experiment plans with the sample size and duration needed, and reads out the results of finished tests.
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Demo client. Run demo --vertical local for a fictional café with an online shop, which runs all 17 agents. The SaaS demo runs 15, since Local and Ecommerce don’t apply to it.
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Skipped agents explain themselves. When an agent’s data is missing, the run shows which file would switch it on.
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Bring your own data. Every input is an optional JSON file, documented in docs/DATA.md. export-sample writes filled-in templates to start from.
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Dashboard. Five new tabs: Council Run, Demand & Competition, Content & Authority, Local & Commerce, and Experiments.
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API. New /plan and /insights/{name} endpoints.
AI Visibility Agency Dashboard
Found Advantage: The Architecture of Being Found
A technical brief on white-label AI visibility
Search used to end in ten blue links. Increasingly it ends in one paragraph written by a machine, and a brand is either inside that paragraph or absent from the conversation. Found Advantage is built for agencies whose clients have noticed the difference.
1. Three layers of measurement
The platform scores visibility on three fronts. SEO tracks rankings, organic traffic and backlink health. GEO maps visibility across markets, country by country. AEO measures answer-engine coverage: the share of a client’s target queries that surface in ChatGPT, Google AI Overviews, Perplexity, Bing Copilot and Claude. Each resolves to a 0–100 score, so a client sees one number per frontier instead of a spreadsheet.
2. From signal to action
Tracking feeds the working modules. Content Optimization scores each page and exposes the weak ones. AI Insights turns movement into plain-language findings: content gaps in high-value topics, new top-ten keywords, shifts in backlink authority. Competitive Intel benchmarks the client against rivals chasing the same answers.
3. The white-label loop
The agency onboards a client, defines target queries and markets, then runs the cycle: audit, optimize, re-measure, report. Dashboards and reports carry the agency’s identity, so the client experiences one service and one relationship. The agency sells the outcome; the platform supplies the instruments.
3. The execution loop
Based on authority diagnosis, the Found Advantage AI system prepares hundreds to thousands of pieces of content for deployment into multiple web2.0 platforms. The objective isn't to be mere a blue link on Google's top ten. The aim is to get the brand on generative and answer SEO query responses.
4. Why the answer layer matters
Rankings show whether a page can be found. Answer and GEO coverage shows whether a brand is trusted: cited, summarised and recommended by a system the buyer believes more than any advertisement.
That is the quiet shift beneath the dashboard. Clients no longer compete only for attention.
They compete to become the sentence the machine chooses to say.
