Anonymized client work
From a short product brief to a production AI visibility SaaS platform
What began as an early GEO monitoring concept evolved into a workspace-based SaaS connecting AI visibility, site readiness, competitive intelligence, actionable workflows and historical validation.
Context
The initial brief described a client portal for GEO monitoring: scan a website using AI, produce a basic score and report, then expand into recommendations, rescans and paid plans.
Approach
The platform evolved around two complementary analytical perspectives.
Outcome
What began as a short concept for GEO monitoring became a much broader operating system for AI visibility and readiness.
Technical model
How the system carries the work from one boundary to the next
- 01
Workspace layer
Workspace layer
Team structure, access control and operational scope across multiple websites.
- Sites
- Roles
- Billing
- 02
Analysis engines
Analysis engines
Two complementary systems for understanding how a brand appears in AI-driven environments and how prepared its website is for machine understanding.
- AI Presence
- AI Readiness
- 03
Intelligence layer
Intelligence layer
Page-level context, competitive signals and supporting evidence turn raw scan output into usable findings.
- Competitors
- Evidence
- URL snapshots
- 04
Execution loop
Execution loop
Findings move into implementation work, then back through the system to verify whether changes produced a measurable result.
- Actions
- Rescan
- Historical validation
The problem
The context and technical tension behind the work
Context
The project started with a direction, not a specification
The initial brief described a client portal for GEO monitoring: scan a website using AI, produce a basic score and report, then expand into recommendations, rescans and paid plans. There was no finished product model, detailed technical specification or predetermined architecture.
Challenge
Build more than another AI score
The core challenge was turning an open-ended monitoring idea into a product capable of answering a much wider set of operational questions. A useful system needed to explain not only whether a brand was visible, but where it appeared across relevant AI-oriented queries, which competitors were taking its place, which pages represented realistic opportunities and whether the site itself was technically and semantically prepared for machine understanding.
The approach
Technical decisions that kept the work coherent
Decision 01
AI Presence focuses on how a brand appears across AI-oriented queries, pages and competitive scenarios.
It builds site context, plans queries, evaluates results and turns them into a visibility landscape containing competitor pressure, gaps, displacement opportunities and prioritized findings.
Decision 02
AI Readiness approaches the problem from the opposite direction: whether the website itself is structured clearly enough for machine understanding.
The analysis considers structured data, crawl and discoverability signals, content structure, entity clarity and emerging AI-oriented signals.
Decision 03
Keeping those concerns separate made it possible to distinguish external visibility from the underlying state of the site while still connecting both into the same workflow.
The solution
Implementation evidence and technical capability
Solution
A multi-system SaaS built around continuous analysis
The resulting platform combines a Next.js application, Laravel backend and asynchronous processing layer. The architecture separates interactive product behavior, backend domain logic and long-running analysis so that complex scans can progress independently without turning the user experience into a fragile synchronous workflow.
Technical summary
- Workspace layerTeam structure, access control and operational scope across multiple websites.
- Analysis enginesTwo complementary systems for understanding how a brand appears in AI-driven environments and how prepared its website is for machine understanding.
- Intelligence layerPage-level context, competitive signals and supporting evidence turn raw scan output into usable findings.
- Execution loopFindings move into implementation work, then back through the system to verify whether changes produced a measurable result.
The implementation supports
- Workspace operations with multiple sites, collaborators and role-based permissions
- Manual and discovered URL sets
- AI Presence analysis
- AI Readiness analysis
- Query, page and competitor-level visibility analysis
- Competitive pressure, overlap, gaps and displacement modeling
- Snapshot Intelligence
- Persistent Action Lists
- Evidence-backed recommendations
- Targeted rescans
- Historical Validation
- Scheduled scans
- PDF and email reporting
- Billing
- API access
- Collaborator management
- Activity history
- Support workflows
Outcome
A technical system with a clearer path forward
From monitoring concept to a closed improvement loop
What began as a short concept for GEO monitoring became a much broader operating system for AI visibility and readiness. The platform can now move from understanding how a site appears, to identifying where competitors are winning, to determining which technical or content changes deserve attention.
Built to fit into agency delivery
The agency retains control of the client relationship, commercial scope and presentation while technical implementation, testing, documentation and handoff remain behind the scenes unless direct communication is explicitly requested.
Start with a bounded scope
Need senior technical support that fits behind your agency?
The engagement can start with a bounded technical task, a complete implementation or an existing codebase that needs stronger ownership.