How Taja Increased its Visibility Score to 32% within 60 days.

AI Visibility Score: 0% to 32% across ChatGPT, Perplexity, and Gemini in 60 days

Sentiment in AI answers: 89% positive when Taja is mentioned

Install conversion rate: 4.8x higher from AI-referred users vs. organic search

About Taja

Taja is a cross-border finance platform. The mobile-first app (iOS + Android) helps freelancers, remote workers, and digital nomads send, receive, convert, and spend money across 60+ countries.

We were invisible in the one channel where buyers actually discover products. Not ranked low. Not mentioned once. Zero.

- David Orovwiroro, CEO, Taja

Challenge: The three-layer problem

Layer 1: Zero visibility in the channel that matters

When a freelancer in London asks ChatGPT, "What's the best app to send money internationally?" Taja doesn't exist. Not ranked low. Not mentioned once. Zero.

A 2025 Bain and Company study found that 80% of consumers rely on AI summaries for at least 40% of their searches.

Taja's competitors were already showing up. They had content structured for AI citation signals, pages that answered the exact questions buyers ask inside ChatGPT, and structured data that LLMs could parse. Meanwhile, Taja was still optimizing for a search engine that fewer people were using each day.

80% of buyers are getting product recommendations from AI chatbots, and Taja was not appearing in those chatbots.

Even if Taja somehow appeared in an AI answer, the team had no idea what the platforms were saying about them.

Was the sentiment positive? Negative? Were AI assistants recommending competitors instead?

The problem cuts deeper than it sounds.

In traditional search, a bad ranking means you're on page 2 instead of page 1. In AI search, negative sentiment means the platform actively steers buyers away from you. One bad mention in a ChatGPT recommendation can cost hundreds of installs.

Layer 3: No way to prove ROI

Taja couldn't prove whether AI visibility drove installs, signups, or revenue. No attribution data. No conversion tracking. No way to justify investing more.

Solution: Building the three-layer stack

Taja partnered with Pierview to fix all three layers in a single 90-day sprint.

Layer 1: Visibility, from zero to 32%

Step 1: Baseline audit.
- Pierview analyzed how Taja appeared (or didn't) across 75+ prompts in their market.
- Broad queries like "best cross-border payment app" and specific ones like "virtual cards for freelancers."
- The results were sobering: Taja had zero visibility across every prompt, every AI platform, every market.
Step 2: Prompt prioritization.
- Using Pierview's Prompt Intelligence, the team ranked prompts by impact score and search volume.
- They found 12 high-impact prompts where competitors appeared but Taja had zero visibility.
- Prompts like "best app to send money internationally" and "virtual cards for online shopping."
Step 3: Content gap analysis.
- Pierview's Sources and Citation Analytics showed exactly which domains AI trusted for each prompt, and what content was missing from Taja's library.
- Competitors had specific comparison pages, use-case guides, and FAQ content that LLMs cited repeatedly. We made sure those pages were visible to AI search engines.
Step 4: Content creation.
- Using Pierview's Content Creation workflow, Taja built targeted content for the highest-gap prompts.
- Each piece was structured to answer the exact questions buyers ask in AI chatbots.

Within 30 days, Taja's AI Visibility Score climbed from 0% to 28%. By day 60, it hit 32%.

Layer 2: Sentiment, 89% positive

Step 5: Sentiment monitoring.
- Pierview tracked how AI platforms described Taja across ChatGPT, Perplexity, Gemini, Claude, and others.
- The team could finally see whether mentions were positive, negative, or neutral, and track how sentiment shifted over time.
Step 6: Reputation analysis.
- Pierview's citation analytics revealed which content AI platforms referenced when describing Taja positively.
- The team doubled down on those content formats and topics, creating more of what AI trusted.
Step 7: Content refinement.
- Based on sentiment data, Taja updated existing pages to reinforce the language AI platforms used when recommending them. Words like "fast," "affordable," and "reliable."
- They also addressed negative sentiment signals by creating content that countered outdated information.

Within 60 days, Taja's sentiment score hit 89% positive. AI platforms were describing the platform in exactly the terms their target buyers wanted to hear.

Layer 3: Conversion, 4.8x higher install rate

Step 8: Attribution tracking.
- Pierview connected AI visibility data to Taja's app install sources, letting the team track which installs came from AI-referred traffic. For the first time, they could see the direct line from ChatGPT recommendation to app install.
Step 9: Conversion analysis.
- The data showed something interesting: users who discovered Taja through AI search installed at 4.8%, compared to 1.1% from organic search.
- AI-referred users weren't just more numerous. They were higher quality, more intent-driven, and more likely to convert.

We finally had the data to prove AI search was our best acquisition channel. That changed how we allocate our budget.