CiteGap for Peec AI
A logic-first MVP that turns AI citations into actionable visibility opportunities. Built to demonstrate product understanding of the AI search analytics space.
Why I built this
In AI search, the answer is the surface layer. The real “ranking layer” is the set of sources/domains models retrieve and cite. If a competitor is showing up in AI answers, they’re usually present in the citation ecosystem that supports those answers.
I built CiteGap to answer one practical question for Peec AI: Where are competitors getting cited from that Peec isn’t? And what should we do next?
What I wanted to demonstrate
- Understanding of the AI search analytics / GEO space
- Ability to scope a tight MVP with a clear definition of “success”
- Turning messy AI outputs into structured analytics + an action plan
What I intentionally did (not) build
- No scraping or auto-querying AI engines
- No heavy LLM extraction pipeline
- No authentication, orgs, permissions, billing
- Just the smallest workflow that produces a decision-ready report
The problem
Founders and marketers can’t reliably “influence the AI answer” directly but they can influence the sources that shape the answer. What’s often missing is diagnosis: identifying which domains are driving competitor visibility and where the brand is absent.
What I built (MVP workflow)
CiteGap takes a small dataset (prompts + AI answers + citations) and produces three views: Top sources, gaps overall, and gaps by competitor + a memo export.
Inputs
- Prompt set (tagged by intent: best_tools, alternatives, comparison, how_to)
- AI answers (one engine is enough for an MVP)
- Citations / sources (URLs → normalized domains)
- Brand mentions per answer (manual tagging in MVP)
Outputs
- Top cited domains in the category
- Citation gaps for Peec AI
- Gaps by competitor (filterable)
- Recommendations for top gaps (manual mapping)
- Markdown export (founder-ready memo)
How “citation gap” is defined
A domain is a gap surface when it appears in citations for answers where a competitor is mentioned and Peec AI is not mentioned.
This definition is intentionally minimal: it maps directly to what you’d do next (improve presence on the domains that repeatedly shape competitor inclusion).
Data model (designed for analytics)
I modeled answers as structured product data rather than unstructured text so the system can answer analytics questions with simple queries.
Core entities
- Prompts (intent-tagged)
- Answers (per prompt & engine)
- Citations (URLs + normalized domains)
- Brand mentions (manual tags per answer)
Quality & action layers
- Domain classification (type + risk flag)
- Recommendations (domain → action + rationale)
- Exportable memo for quick sharing
Report preview
The founder should immediately see: “Where do we show up? Where don’t we? What do we do?”
Top cited domains
| Domain | Type | Citations | Prompts covered | Risk |
|---|---|---|---|---|
| searchenginejournal.com | SEO News | 8 | 8 | OK |
| peec.ai | Vendor Site | 6 | 6 | OK |
| moz.com | SEO Blog | 5 | 5 | OK |
Citation gaps for Peec AI
| Gap domain | Type | Answers (competitor present, Peec absent) | Risk | Recommended move |
|---|---|---|---|---|
| searchenginejournal.com | SEO News | 4 | OK | Contribute expert articles on GEO/AEO trends and AI search visibility |
| g2.com | Review Site | 4 | OK | Create and optimize G2 profile with customer reviews, case studies, and feature comparisons |
| reddit.com | Community | 2 | OK | - |
What this showcases
Product thinking
- Scoped a tight MVP around a defensible definition
- Designed a data model built for analytics queries
- Optimized for “time to insight” and shareable output
Analytics mindset
- Modeled activation/engagement via report actions
- Separated signal (sources) from noise (answer text)
- Added “risk flags” to avoid low-quality tactics
What I’d build next (if this lived inside Peec)
The MVP proves the concept. If integrated into Peec, I’d iterate in this order:
Iteration 1: Better prioritization
- Prompt clustering + intent weighting (buyer-intent higher weight)
- Opportunity score: frequency × intent × trust
- Show “quick wins” vs “long-term” surfaces
Iteration 2: Workflow integration
- Turn gaps into tasks (update listing / pitch inclusion / publish neutral benchmark)
- Track impact: does Peec appear more after closing a gap?
- Shareable client-ready reports