Designing a Free AI Reputation Report
DBB Software delivered the design for a free reputation report any clinician will be able to request, whether or not they are the client's customer. It reads a provider's online presence across search engines and AI assistants, scores it on five dimensions against comparable peers, and states where its own data ran thin.
Industry
Healthcare & Biotech
Service
AI Development
Team
1 Product Owner, 1 AI Engineer, 1 Solution Architect
Project State
July 2026 - Ongoing
Country
United Kingdom
About the Client
A UK-based healthcare technology company operates a global platform that enables patients to review and rate healthcare providers while accessing reliable information to make informed care decisions. With a presence in markets like London, Germany, Austria, Australia, Dubai, and Ireland, the company aims to improve transparency and trust in healthcare through patient feedback and data-driven insights.
The Client's Initial Request
DBB Software designed something that answered two questions at once: give any provider a useful read on their own online reputation, and give the company a reason for a stranger to engage with it before anyone mentions a subscription.
Invisible Online Presence
No view existed of how discoverable a consultant is, or whether their professional standing turns into anything a prospective patient can find. The information sat scattered across search engines, directories and review platforms.
01
Peer Benchmark for Every Score
A review count on its own tells a consultant nothing. Every score had to sit against comparable consultants in the same specialty and market.
02
Ranked Next Steps
The audience is clinicians, not marketers. The report had to return a ranked, specific list of next steps in plain English.
03
Reports From Public Data
The prospects who mattered were providers whose profiles the client did not hold. The product had to produce a credible report from public information alone.
04
Solutions We Delivered
DBB Software delivered the design for a diagnostic that discloses its own limits. It reads a provider's presence across search engines and AI assistants, scores it on five separate dimensions, sets that against comparable peers, and turns the result into a ranked list written for a clinician.
Search and AI Assistant Signals
The report gathers reputation signals from conventional search and from AI assistants. A growing share of patients ask an assistant to recommend a specialist instead of typing into a search box.
What an assistant can see and say about a provider is a different question from where that provider ranks.
Signals from every configured source are gathered and passed to scoring within an agreed time budget.
Five Scored Dimensions
Scoring covers discoverability, profile strength, review volume and quality, expertise signal, and network presence, alongside an overall result. Each score is set against grouped data for the provider's specialty and market.
A consultant who is highly visible and thinly reviewed has a different problem from one with excellent reviews nobody can find. Separate scores show which.
The report turns the scores into a ranked list of specific actions in plain language, ordered so the reader knows what to do first. The list was specified as a requirement in its own right.
Where a provider already scores well, the report still returns constructive suggestions.
Bounded Report Claims
A score and a recommendation are machine-made judgments about a named professional, delivered to that professional. What the report may assert is bounded.
A point goes in only where the research returned something to base it on. Where the evidence is not there, the report flags the assumption or leaves the point out.
Superlatives and ranking language never appear.
Comparisons are drawn from grouped data alone. At no point does one clinician's report expose another named clinician's.
Declared Gaps and Fallbacks
The system searches existing platform profiles for a match to the submitted details. A single clear match links the report to that profile and makes it substantially richer. No match, or several ambiguous ones, and the report proceeds on submitted and public data alone. It never attaches the closest available profile.
Where benchmarking data is unavailable for a specialty and market, the report falls back to the closest available peer group and states on the page that it has done so.
Thin peer groups were recorded as the project's highest-likelihood risk.
If some research sources fail or hit a rate limit, the report is generated from the ones that succeeded and carries a disclosed reduced-confidence note. If every source fails, the provider gets a clear failure message and a retry.
Public Intake With a Cost Cap
The intake form asks for name, specialty, location and a contact email, and it is open to any provider whether or not they are on the client's platform.
Invalid or incomplete submissions produce inline validation and no job. Repeated submissions in quick succession produce exactly one job.
Third-party search and AI usage is metered and capped for each report, counted in one place so the cap holds across the whole system.
The cap holds the unit economics of a free tool at any volume of demand.
PDF Delivery and Data Protection
The finished assessment is generated as a PDF and delivered by email through a transactional provider with bounce and complaint handling, which protects the sending domain from the volume a public tool can generate. Progress is shown to the submitter while the report generates.
If delivery fails, the failure is logged and retried and the provider is offered another route, such as a download link. If the PDF fails to generate, the provider is notified and can retry, and no email goes out carrying a broken attachment.
Submitters who are not customers have never signed the client's terms. Their data is handled under GDPR with a defined lawful basis, a retention limit and a working route to erasure.
Legal and compliance review of the intake form and the retention policy sits ahead of public launch as a gate.
Success Metrics and Named Risks
Three measures were named: the number of reports generated by providers who are not customers together with downstream signup conversion, the report generation success rate across all configured research sources, and qualitative feedback on whether the recommendations are actionable.
Three risks were registered with likelihood and impact:
Third-party search and AI costs scaling with usage, answered by the per-report cap and by monitoring before wide promotion
Sparse benchmarking data for niche specialties and markets, rated the highest-likelihood risk on the project, answered by the disclosed cohort fallback
Personal data from people who are not customers being under-specified, rated high impact, answered by legal and compliance review as a launch gate
Three things were ruled out of scope: automated remediation of a provider's profile, scheduled re-scoring, and any public comparison or leaderboard of provider scores.
Results Achieved
5 scored dimensions
Discoverability, profile strength, review volume and quality, expertise signal, and network presence, each benchmarked separately.
3 degradation paths with defined behavior
An unclear identity match, a thin peer group, or a failed research source with specified outcomes.
4 fields to a full report
Name, specialty, location and contact email produce a scored, benchmarked PDF delivered by email.
A spend ceiling on every free report
Third-party search and AI usage is metered against a per-report cap, counted in one place.
Put a Free AI Tool at the Top of Your Funnel
DBB Software designs AI tools that create demand by being useful and hold their credibility by disclosing what they could not see.
Contact Us
"Most of our work starts with a 30-minute call where someone describes a product they're trying to ship and one part of the engineering picture they can't get around.
If that's where you are, let's set one up; I'll tell you straight whether we're the right fit.”
Mina Morkos
Business Development Manager
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