PerformanceLabs Case Study

Industry: Performance and load testing services and platform
Delivery: Global, cloud and on‑premise
Goal: Win AI answer visibility for non‑branded buyer prompts, with a focus on “best performance testing services.”

Outcome

PFLB now appears in top results on ChatGPT for the query “best performance testing services,” with consistent inclusion for adjacent prompts such as “best load testing services,” “enterprise performance testing company,” and “SaaS performance testing partner.” This puts the brand in front of high intent buyers at the decision moment.

The Challenge

AI assistants favor entities that present clear scope, methods, and proof. Most vendors talk in generalities. Buyers ask for specifics, such as tools, test types, deliverables, time to value, and integration with CI/CD. PFLB had deep expertise and a strong platform, yet assistants were not consistently selecting it in top recommendations.

What RankedAI Did

1. AI Answers Audit
   Mapped 60 buyer‑intent prompts across ChatGPT, Gemini, Copilot, and Perplexity. Logged citations, phrasing patterns, and competitors that assistants preferred to cite.

2. Entity and Service Schema
   Implemented Organization, Service, SoftwareApplication, Offer, and FAQ schema. Unified naming for services, such as Load, Stress, Soak, and Spike testing. Linked platform capabilities and consulting to a single canonical entity.

3. Answer‑Ready Service Pages
   Reworked key pages to front‑load facts: who the service is for, tool stack options (JMeter, k6, Gatling), geo‑distributed load, CI/CD integration, observability hooks (Grafana), typical timelines, and sample deliverables. Short paragraphs and question headings mirror how buyers phrase prompts.

4. Proof and Signals
   Elevated years in operation, 300 plus clients across industries, testimonials, recognizable logos where permitted, and media mentions. Published concise case stories with before and after narratives and artifact screenshots. Synced facts across site pages and major directories to remove ambiguity.

5. Prompt Coverage and Comparison Content
   Built concise guides for “performance testing vs load testing,” “how to choose a performance testing vendor,” and “in‑house vs partner.” Added tool and framework explainers that assistants can cite when summarizing options.

6. Monitoring and Iteration
   Weekly checks tracked placement, citations, and language patterns. We refined headings, added missing facts, and tightened claims as assistant behavior shifted.

Results We Track

• Placement in AI answers: Presence in top results for the core query and stronger coverage for adjacent prompts.
• Answer share of voice: Higher citation frequency in list‑style recommendations.
• Commercial indicators: More qualified inquiries that reference AI recommendations and deeper engagement on service pages.

(AI results fluctuate. We monitor weekly and adjust content to maintain visibility.)

Sample Prompts That Now Surface the Brand

• “Best performance testing services”
• “Best load testing services”
• “Enterprise performance testing company”
• “Performance testing partner for SaaS”
• “CI/CD performance testing services”

Why It Worked

Clear entity and consistent facts. Precise, citation‑friendly pages that answer buyer questions directly. Proof that reduces uncertainty, which helps assistants select a trustworthy recommendation.

Timeline

Initial gains in weeks two to four. Stable presence for core prompts after iterative improvements over the first month.

Services Used

AI Answers Audit. Entity and Schema Engineering. Answer‑Ready Content. Proof Alignment. Monitoring and Iteration.