Perplexity
GrokThe useful return is measurement clarity: the team can see where answer engines recognize the brand and where citation confidence still needs repair.
Back to case studiesAI visibility measurement proof
How SolutionStream made AI search visibility measurable.
New Reward measured how SolutionStream appeared across ChatGPT, Claude, Google AI, Grok, and Perplexity, then turned the result into a clear map of where the brand was recognized, cited, and still under-supported.
SolutionStream citation mapA measurement problem: AI engines could often name SolutionStream, but citation support varied by engine and created the next authority work queue.
Visual boundary: New case-specific illustration, not a private client dashboard and not proof of traffic, lead, ranking, revenue, or schema-impact lift.
Traffic, ranking, lead, pipeline, revenue, and schema-impact lift need separate implementation and attribution evidence.
The current evidence is a measured scan window, not a before-and-after lift claim.
The journey
Problem to proof.
01
Problem
SolutionStream already had strong public recognition. The open question was whether AI answer engines could support that recognition with citation-backed trust.
02
Action
New Reward measured buyer-style prompts across five answer engines, separated mentions from citations, and mapped the engine-level citation gaps into the next authority work queue.
03
Result
The scan recorded 1,474 visibility snapshots, 90.8% mention coverage, and 52.5% citation coverage, making the trust split visible enough to prioritize.
04
Why it matters
Recognition alone does not make a buyer-ready AI answer. Citation gaps show where source, entity, content, and proof work still need to support the brand.
Sales use
How to use this proof in a buyer conversation.
Buyer question
Can New Reward make AI-search visibility measurable for a known B2B brand?
Use this when the buyer understands SEO but thinks AI visibility is too vague to inspect.
Objection handled: AI search feels fuzzy; the study separates mentions, citations, engines, and next authority work.
Best fit
Next proof needed before stronger claims
- Post-implementation scan using the same prompt families
- Search, traffic, and lead attribution before growth claims
Client context
SolutionStream
- Industry
- Software development and IT consulting
- Timeframe
- January 21 to January 30, 2026 scan window
- Starting point
- The measured question was whether SolutionStream appeared in AI answers and whether those answers cited supporting sources.
Proof boundary
This proves measurable AI visibility, mention coverage, citation coverage, and engine-level trust gaps. It does not claim traffic lift, search ranking lift, lead volume, pipeline, revenue, or deployed-schema impact.
Verified resultThe verified result is a measurable visibility and citation baseline. SolutionStream could see where the brand was recognized, where citation support was strong, and where citation repair should come next.
Inspectable work
What shipped and where the proof comes from.
What shipped
- Measured 1,474 AI visibility snapshots across five answer engines.
- Compared mention coverage against citation coverage by engine.
- Reviewed high-intent category and comparison prompts beyond branded searches.
- Packaged generated assets, schema records, data islands, and next authority fixes into a visible work queue.
Source notes
AI visibility scan
Internal SolutionStream AI visibility scan window dated 2026-01-21 to 2026-01-30.
Engine readout
Internal SolutionStream engine-level mention and citation coverage readout.
Generated asset queue
Internal SolutionStream package, data-island, and schema-record activity summary.
Cleared proof assets
Only sourced, permission-cleared proof appears here.
Next step
Turn this proof into the right offer lane.
Start from the evidence, then choose the work that matches the buyer path.