How Kinescope Increased Its AI Visibility By Fixing Content It Already Had

Kinescope is a B2B video platform for hosting, streaming, protecting, and analyzing video. Its product spans several technical areas, including a customizable video player, content protection and DRM, streaming, and video infrastructure.

That makes AI visibility unusually fragmented. Someone looking for Kinescope may never search for the brand itself. They may ask ChatGPT about protecting business video, compare video platforms, or look for a solution with DRM. Tracking visibility across those different product and buying contexts was therefore much harder than following a single brand query.

Kinescope CMO Denis Konnov wanted to understand two things: which companies AI systems were recommending for those questions, and why Kinescope wasn't appearing in more of the answers.

Within the first weeks of working with Findrix, the generated prompt set surfaced a surprisingly simple problem: Kinescope had relevant product capabilities, but some important details were not described clearly enough on its existing pages.

Findrix showed Kinescope which buyer questions were not being answered by its existing pages, helped prioritize the most actionable content and technical gaps, and gave the team a weekly measurement loop for checking what changed.

The team began fixing those pages during its first month with Findrix. By the end of the first month, measured brand visibility had increased across all four tracked AI surfaces. The largest move was in Google AI Overviews, from approximately 2.4% to 8.6%; ChatGPT moved from approximately 10.5% to 14.0%. No new pages were required for the first round of improvements.

Before Findrix

Kinescope had already invested heavily in SEO, used Ahrefs, and made its first attempts to monitor how AI systems talked about the company.

The team tested custom API-based queries, used a service that exported ChatGPT answers, and watched Google AI Overviews to validate whether particular query types reflected real search demand.

But the process was fragmented, and the AI visibility category still felt too immature and expensive for systematic work at Kinescope's scale. Tracking was especially difficult because the product spans several distinct areas and competes with global video platforms such as Vimeo and Wistia: one company-wide number could not explain which product, prompt pattern, or competitor was driving a change.

How we measured AI visibility

Kinescope tracks 95 prompts every week across frontier AI models, split between discovery prompts that never name the brand and prompts that name it directly.

The set covers four product clusters — DRM and content protection, video APIs and infrastructure, the white-label player, and CDN delivery — across discovery, problem-led, comparison, and reputation queries.

Brand mentions are measured as the share of answers in which Kinescope is named. Each AI surface is analyzed separately rather than blended into one score, because the engines do not move in sync.

What Kinescope changed

Kinescope worked in a weekly cycle. Over four weeks, the team deployed 26 production releases across English, Brazilian Portuguese, Dutch, Japanese, and German pages.

Kinescope was not starting from scratch. Its site was already well structured, with extensive schema markup and a strong technical foundation.

Even so, Findrix surfaced a small set of high-priority improvements the team could tackle first and begin seeing results:

• Clarifying the heading hierarchy across the blog
• Substantially expanding FAQ coverage
• Correcting structured data on 35 pages that presented the product as costing zero

The opportunity was not to rebuild the site, but to remove a few obstacles that could keep search and AI systems from interpreting otherwise strong content correctly.

What changed

Across the four-week period, the share of prompts in which Kinescope was mentioned ended higher on all four tracked AI surfaces.

• 2.4% → 8.6%Google AI Overviews mention share, the largest move of the four surfaces.
• 10.5% → 14.0%ChatGPT mention share over the same four-week period.

The paths were not linear. Gemini and Perplexity peaked earlier and declined in the final run, while ChatGPT first fell and then recovered. The month-end snapshot also showed that ChatGPT cited a Kinescope URL whenever it mentioned the brand, while Perplexity sometimes mentioned Kinescope without linking to it.

For Kinescope, stronger visibility meant appearing more often while buyers were defining their requirements and comparing video infrastructure options.

How to interpret the results

These movements should be read as a four-week directional trend rather than proof that any single release caused a specific increase. There was no control group, the engines moved differently between runs, and the comparable prompt set changed slightly on some surfaces. What we can say is that after Kinescope identified and shipped a substantial set of fixes, measured brand visibility ended the month above its starting point across all four tracked engines.

In Kinescope's words

For Nikita Vikhrov, Kinescope's SEO/GEO specialist, the biggest shift was a new way of working with pages that already existed:

"A pretty serious push that quickly changed our approach to how we fill our pages."

What's next

Kinescope's next priority is product-level prompt clustering, which would let the team separate visibility for DRM, the player, CDN, competitive comparisons, reputation, and different stages of the buying journey.

The next step is deeper attribution: showing what changed, why it changed, and which action or product area produced the movement. That would let the team build more isolated GEO experiments — define a cluster, make a specific change, and measure that cluster again instead of relying on one company-wide graph.

See what AI systems are missing on your existing pages

Findrix connects buyer prompts to specific content and technical gaps, so your team knows what to fix first.