Case studies

What was wrong, what we did, and what changed.

These anonymized stories keep the proof simple. Each one shows the starting problem, the work New Reward performed, the verified result, and the claim boundary we will not cross without evidence.

Problem

Work

Result

Professional coaching and leadership development

Leadership coaching AI visibility plan

This coaching brand was not showing up in the AI answer checks we measured. New Reward turned that blank starting point into a clear work plan with buyer questions, competitors, gaps, recommendations, and ready-to-use visibility assets.

Problem

The brand had a 0/100 AI visibility score. In plain English: the measured AI tools were not naming the business when buyers asked relevant questions.

Work

New Reward tested 50 buyer-style questions, mapped competitors and content gaps, and built a package of recommendations and machine-readable assets.

Result

The client moved from an unknown AI visibility problem to a documented action package with 23 readiness asset groups and 8 recommendations.

Why it matters: A buyer cannot choose a business they never see. The package gave the client a clear path to make the brand easier for search engines, AI tools, and prospects to understand.

View case study Download PDF

What changed from baseline to package

AI visibility baseline

Measured starting score at audit time.

0/100

Benchmark questions

Buyer-style questions tested.

Competitors mapped

Competitive set included in the package.

Content gaps

Gaps found for follow-up work.

Recommendations

Action steps included in the package.

Readiness asset groups

Schema, data, training, review, and site-root groups.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

Roofing contractor

Roofing contractor local search gap

This local contractor had almost no AI visibility signal. New Reward measured the gap, mapped competitors, found service-content holes, and showed what needed to be fixed before stronger local claims could be made.

Problem

The business had a 1/100 AI visibility score. It had a small signal in one measured engine, but it was mostly missing from the broader answer landscape.

Work

New Reward mapped local competitors, found service-page gaps, prepared recommendations, and flagged that the question set needed more depth before final measurement.

Result

The client received a clear local action map: what was weak, what content was missing, and what measurement issue had to be fixed first.

Why it matters: Contractor leads often go to the company buyers can find fast. Weak local visibility, thin service pages, and slow proof make it easier for competitors to win the job.

View case study Download PDF

Local search gap evidence

AI visibility baseline

Measured starting score at audit time.

1/100

Benchmark questions

Fallback question set; needs expansion.

Competitors mapped

Local competitor set included in the package.

Content gaps

High gap count for local service coverage.

Recommendations

Action steps included in the package.

Readiness asset groups

Schema, data, training, review, and site-root groups.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

AI consulting and professional services

AI consulting entity readiness

This AI consulting brand had no measured AI visibility at the start. New Reward used competitor and gap evidence to build a clear entity-readiness plan for future authority work.

Problem

The measured AI visibility score was 0/100. The business was not being surfaced in the tested AI answers, even though the market had clear competitor signals.

Work

New Reward mapped competitors, found content gaps, prepared recommendations, checked package quality, and flagged the question set for recalibration.

Result

The client received a complete competitor and content-gap map plus 23 readiness asset groups for future implementation.

Why it matters: AI consulting buyers need trust before they book. If the brand is missing from answer engines, the business has to build clearer entity, content, and proof signals.

View case study Download PDF

Entity-readiness evidence

AI visibility baseline

Measured starting score at audit time.

0/100

Benchmark questions

Fallback question set; needs recalibration.

Competitors mapped

Full competitive set included.

Content gaps

Full content-gap queue identified.

Recommendations

Action steps included in the package.

Readiness asset groups

Schema, data, training, review, and site-root groups.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

Information technology and services

IT services quality gate

This package had clean files, but the story was not strong enough to send. New Reward caught the weak recommendation depth and fallback question set before the package could be mistaken for complete proof.

Problem

The package looked technically intact, but it had only 1 recommendation and a 6-question fallback set. That was not enough for a strong client handoff.

Work

New Reward checked file quality, mapped competitors and content gaps, flagged the weak recommendation set, and created a staging calibration plan.

Result

The package was stopped from being oversold. The next work was clear: improve recommendation depth and validate scoring before production rollout.

Why it matters: Quality control protects trust. A weak package can hurt the client relationship even when the files are technically valid.

View case study Download PDF

Quality gate evidence

AI visibility baseline

Measured starting score at audit time.

0/100

Benchmark questions

Fallback question set; needs expansion.

Competitors mapped

Competitive set included in the package.

Content gaps

Gaps found for follow-up work.

Recommendations

Insufficient depth; caught before handoff.

Readiness asset groups

Schema, data, training, review, and site-root groups.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

Cross-industry SEO, GEO, and AI discoverability

Technical visibility automation layer

New Reward does more than create audit files. Where the right integrations are connected, the platform can publish and verify technical visibility assets instead of leaving clients with a manual handoff.

Problem

The risk was manual follow-through. A package could produce useful files, but those files still needed a safe path to become live website improvements.

Work

New Reward defined safe asset families, scoped Cloudflare Edge as the automatic publishing lane, kept WordPress and CMS bridges as manual lanes, and documented staging checks.

Result

The platform promise became clearer: connected clients can move from package output to verified technical visibility assets with fewer manual steps.

Why it matters: A plan only helps when it ships. The stronger operating promise is that New Reward can prepare, publish, and verify core machine-readable assets when the client setup supports it.

View case study Download PDF

Automation coverage evidence

Auto-publish families

Safe technical visibility families identified.

Automatic channel

Cloudflare Edge is the automatic website artifact lane.

Cloudflare

Manual compatibility lanes

WordPress and CMS bridge remain compatibility lanes.

Staging route families

Expected route families in the SEO validation matrix.

Live deploy blockers tracked

Artifact activation remained tracked instead of hidden.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

Proof boundary

Clear stories. No inflated claims.

These case studies separate activity, readiness, and results. If a number does not prove revenue, rankings, leads, reviews, or AI citations, the page says that plainly.

See offerings Start audit

Case studies

What was wrong, what we did, and what changed.

These anonymized stories keep the proof simple. Each one shows the starting problem, the work New Reward performed, the verified result, and the claim boundary we will not cross without evidence.

Problem

Work

Result

Professional coaching and leadership development

Leadership coaching AI visibility plan

Problem

The brand had a 0/100 AI visibility score. In plain English: the measured AI tools were not naming the business when buyers asked relevant questions.

Work

New Reward tested 50 buyer-style questions, mapped competitors and content gaps, and built a package of recommendations and machine-readable assets.

Result

The client moved from an unknown AI visibility problem to a documented action package with 23 readiness asset groups and 8 recommendations.

Why it matters: A buyer cannot choose a business they never see. The package gave the client a clear path to make the brand easier for search engines, AI tools, and prospects to understand.

View case study Download PDF

What changed from baseline to package

AI visibility baseline

Measured starting score at audit time.

0/100

Benchmark questions

Buyer-style questions tested.

Competitors mapped

Competitive set included in the package.

Content gaps

Gaps found for follow-up work.

Recommendations

Action steps included in the package.

Readiness asset groups

Schema, data, training, review, and site-root groups.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

Roofing contractor

Roofing contractor local search gap

Problem

The business had a 1/100 AI visibility score. It had a small signal in one measured engine, but it was mostly missing from the broader answer landscape.

Work

New Reward mapped local competitors, found service-page gaps, prepared recommendations, and flagged that the question set needed more depth before final measurement.

Result

The client received a clear local action map: what was weak, what content was missing, and what measurement issue had to be fixed first.

Why it matters: Contractor leads often go to the company buyers can find fast. Weak local visibility, thin service pages, and slow proof make it easier for competitors to win the job.

View case study Download PDF

Local search gap evidence

AI visibility baseline

Measured starting score at audit time.

1/100

Benchmark questions

Fallback question set; needs expansion.

Competitors mapped

Local competitor set included in the package.

Content gaps

High gap count for local service coverage.

Recommendations

Action steps included in the package.

Readiness asset groups

Schema, data, training, review, and site-root groups.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

AI consulting and professional services

AI consulting entity readiness

This AI consulting brand had no measured AI visibility at the start. New Reward used competitor and gap evidence to build a clear entity-readiness plan for future authority work.

Problem

The measured AI visibility score was 0/100. The business was not being surfaced in the tested AI answers, even though the market had clear competitor signals.

Work

New Reward mapped competitors, found content gaps, prepared recommendations, checked package quality, and flagged the question set for recalibration.

Result

The client received a complete competitor and content-gap map plus 23 readiness asset groups for future implementation.

Why it matters: AI consulting buyers need trust before they book. If the brand is missing from answer engines, the business has to build clearer entity, content, and proof signals.

View case study Download PDF

Entity-readiness evidence

AI visibility baseline

Measured starting score at audit time.

0/100

Benchmark questions

Fallback question set; needs recalibration.

Competitors mapped

Full competitive set included.

Content gaps

Full content-gap queue identified.

Recommendations

Action steps included in the package.

Readiness asset groups

Schema, data, training, review, and site-root groups.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

Information technology and services

IT services quality gate

Problem

The package looked technically intact, but it had only 1 recommendation and a 6-question fallback set. That was not enough for a strong client handoff.

Work

New Reward checked file quality, mapped competitors and content gaps, flagged the weak recommendation set, and created a staging calibration plan.

Result

The package was stopped from being oversold. The next work was clear: improve recommendation depth and validate scoring before production rollout.

Why it matters: Quality control protects trust. A weak package can hurt the client relationship even when the files are technically valid.

View case study Download PDF

Quality gate evidence

AI visibility baseline

Measured starting score at audit time.

0/100

Benchmark questions

Fallback question set; needs expansion.

Competitors mapped

Competitive set included in the package.

Content gaps

Gaps found for follow-up work.

Recommendations

Insufficient depth; caught before handoff.

Readiness asset groups

Schema, data, training, review, and site-root groups.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

Cross-industry SEO, GEO, and AI discoverability

Technical visibility automation layer

Problem

The risk was manual follow-through. A package could produce useful files, but those files still needed a safe path to become live website improvements.

Work

New Reward defined safe asset families, scoped Cloudflare Edge as the automatic publishing lane, kept WordPress and CMS bridges as manual lanes, and documented staging checks.

Result

The platform promise became clearer: connected clients can move from package output to verified technical visibility assets with fewer manual steps.

Why it matters: A plan only helps when it ships. The stronger operating promise is that New Reward can prepare, publish, and verify core machine-readable assets when the client setup supports it.

View case study Download PDF

Automation coverage evidence

Auto-publish families

Safe technical visibility families identified.

Automatic channel

Cloudflare Edge is the automatic website artifact lane.

Cloudflare

Manual compatibility lanes

WordPress and CMS bridge remain compatibility lanes.

Staging route families

Expected route families in the SEO validation matrix.

Live deploy blockers tracked

Artifact activation remained tracked instead of hidden.

Bars show evidence coverage or measured baseline values. They are not revenue or ranking lift unless stated.

Proof boundary

Clear stories. No inflated claims.

These case studies separate activity, readiness, and results. If a number does not prove revenue, rankings, leads, reviews, or AI citations, the page says that plainly.

See offerings Start audit