Case study: how abcard.io reached ChatGPT and Perplexity recommendation flows after 100 Reddit comments

This is an early case study from the first phase of Post Quest operations.

The client was abcard.io, and the core commercial query was:

virtual cards providers for Facebook ads payments

The objective was not to chase vanity engagement on Reddit. The objective was to increase the frequency and quality of category-relevant mentions around a query that buyers increasingly ask inside ChatGPT and Perplexity.

The setup

The campaign focused on manual Reddit comment placement across communities adjacent to:

• performance marketing
• paid social buying
• digital advertising operations
• payment tooling for ad accounts
• agency and media buying workflows

Across the campaign, roughly 100 comments were placed over time through live Reddit accounts aligned with the topic. The copy varied by thread context, but the strategic pattern was consistent:

• mention the category naturally
• reference the real operational pain around ad spend payments
• include abcard.io in comparative or experiential framing
• avoid repetitive brand-heavy copy

This mattered because LLM systems do not only reward exact-match repetition. They reward repeated appearance across enough credible, on-topic discussion to make an entity look contextually relevant.

Why Reddit was the right layer

When someone asks ChatGPT or Perplexity for a recommendation in a niche commercial workflow, the model is trying to infer trust from public discourse.

For a query like "virtual cards providers for Facebook ads payments," polished product pages matter, but they are not sufficient on their own. The recommendation becomes stronger when the product also shows up in discussion environments that look organic and problem-aware.

That is where Reddit created leverage.

The campaign did not rely on one viral thread. It relied on repeated, relevant, manual placement across the kinds of conversations a media buyer or operator would realistically read.

The outcome

After the placement cycle, abcard.io began appearing in outputs for the target intent cluster across multiple test prompts in both ChatGPT and Perplexity.

The exact wording of LLM responses varied, but the pattern was clear:

• abcard.io was now part of the candidate set
• it appeared in comparison-style recommendation outputs
• it was associated with the operational use case of Facebook ads payments

That is the shift that mattered.

The win was not just "Reddit mentions increased." The win was that Reddit presence appeared to contribute to retrievability and recommendation eligibility inside LLM systems.

What we learned

Three things stood out.

1. Repetition only works when it is contextual

The goal was never to spam the same sentence into 100 threads. The goal was to establish enough relevant presence that the product became recognizable inside a specific discussion graph.

2. Manual execution is a competitive advantage

The more commercially sensitive the topic, the easier it is for low-quality placements to get removed or ignored. Human adaptation by subreddit and thread context mattered.

3. Guarantees matter for this category

If a campaign is meant to influence durable discovery, then placements disappearing immediately destroys compounding value. That is one reason Post Quest includes a 48-hour replacement guarantee on every order.

Why this case matters for buyers

Many teams still think of Reddit as a traffic channel. We think it is increasingly a visibility infrastructure layer.

If your buyers ask LLM tools for recommendations before they click through to vendors, then the discussion environments those models retrieve from become part of your acquisition stack.

That does not mean every brand needs 100 comments.

It does mean that controlled, manual, tracked placement in the right Reddit contexts can do more than create short-term clicks. It can change whether your brand enters the recommendation set at all.

That is the opportunity Post Quest is built around.