AI Visibility/GEO – Redefining brand visibility for AI search

Rankfor.ai is an early-stage technology company pioneering the concept of AI Visibility/GEO (Generative Engine Optimization), a new approach to understanding how brands appear and are narrated across AI-powered interfaces. As large language models and generative AI systems reshape the way people engage with companies, traditional tools like SEO or SERP monitoring are no longer enough. Visibility now depends on how AI assistants summarise a brand and whether narratives align with audience expectations. Source citation is also an emerging factor in trust-building.

Rankfor.ai’s mission is to provide enterprises with actionable insights in this space. Their platform is designed to scan a brand’s digital footprint and map it against personas, then simulate prompts across multiple AI models to reveal trust gaps, coverage blind spots, misaligned narratives and new opportunities. These insights are translated into prescriptive dashboards and playbooks, helping marketing, communications and product teams strengthen brand voice and align digital presence with strategic goals.

At the time of engagement, Rankfor.ai was in the process of defining and validating an entirely new category. They needed to prove their approach could work technically and resonate with customers and investors. Because no off-the-shelf solutions existed and the problem space was evolving quickly, the company sought an engineering partner who could co-create a robust proof of concept to balance exploratory R&D with the structure, security, scalability and governance required for enterprise adoption.

Once the scope and delivery pathway were agreed, the engagement moved quickly into execution. We set a roadmap with Rankfor.ai that focused on one outcome: a credible proof of concept that could withstand investor scrutiny while leaving space for a version-one release.

The first stream centred on backend R&D. We built a versioned Scan Controller and pipeline capable of analysing domains and extracting ontology clusters. These clusters became the backbone of later steps, providing a structured way to interpret a brand’s digital footprint.

The second stream focused on persona generation. We created an orchestrated service that regenerates personas atomically and recalculates coverage while protecting curated demo personas from being overwritten.

To manage the volatility of large language models, we introduced prompt governance and observability. Centralised templates, QA checklists and output schemas kept results stable across changing APIs. Correlation IDs and refresh metrics added transparency into model behavior over time.

Finally, we turned insights into a narrative dashboard. Built in React with Ant Design and Tailwind, the interface presented an AI Territory Map, persona coverage and progress indicators like Semantic Match Rate. A supporting website with blog functionality helped communicate the emerging AI Visibility/GEO category externally.

Building a proof of concept in a volatile AI landscape meant facing challenges on several fronts. The most immediate was the instability of LLM surfaces. Endpoints, models and behaviours changed frequently, making consistent results difficult to achieve. To address this, DO OK implemented a proxy layer and centralised prompt governance reinforced by QA rubrics. This gave Rankfor.ai a buffer against upstream volatility and allowed outputs to remain stable across runs.

Another challenge was data integrity and idempotency. Persona regeneration involved multiple steps, including clustering, prompt execution and coverage recalculation, which could produce duplicate or stale states. We solved this by designing regeneration to be atomic and cache-aware, creating outputs that maintained accuracy while curated demo personas stayed intact.

Maintaining architecture discipline was an important consideration. The risk of circular dependencies and orphaned modules was high in a rapidly evolving NestJS codebase. We enforced strict module boundaries, applied dependency checks and allowed exceptions only where absolutely necessary, preserving maintainability without slowing iteration.

Security and privacy were non-negotiable requirements. We implemented OAuth2/OIDC with JWT verification, Redis-backed sessions, rate limits and token redaction, alongside a “no raw content in logs” rule. The PoC could now be confidently demonstrated to investors and early customers.