Client: Deckard Technologies
How BrandViz.AI Helped Deckard Technologies Capture Half of Its AI Search Category in the United States in 8 Weeks
When Deckard Technologies, the US leader in short-term rental compliance, launched Rentalscape LTR - a new product extending their patented Forensic AI engine into long-term rental compliance - they built AI visibility into the launch from day one. After an 8-week sprint with BrandViz.AI, AI models had learned the new product category and Deckard was the dominant vendor in AI search for LTR compliance in the United States: capturing 45.9% of all category mentions and 50.4% of all category citations across ChatGPT, Claude, Perplexity, and Gemini.
“At Deckard, we’ve been AI native from the beginning, and AI visibility was one part of the marketing funnel I knew we needed to be ahead of the curve on - especially heading into a new product launch.
Part of me was like ‘are they trying to trick me with these numbers - they’re too good!’ But the journey to get there impressed me as much as the results.
What impressed me most was how fast the BrandViz team understood our product and positioning, and how they really worked with me as though we were all on the same team - versus a vendor trying to upsell. I felt like a VIP client; my only constructive feedback is that they were both too generous with their time!
The content side stood out for its quality, clarity, and volume. If I had briefed an internal team, this would have been several weeks if not months of work; with an agency, thousands in investment and a lack of product or positioning understanding. It was just multi-useful - beyond what I was hoping for.
With LLM optimisation, the positive feedback loop comes faster - which means more motivation to put the work in once in maintenance mode. Before, I knew there was a gym and I needed to exercise. Now I know what exercises to do.”
8 weeks. By the numbers.
Deckard ended the sprint owning 45.9% of all category mentions and 50.4% of all category citations, more than the next four competitors combined. The before/after on some of the individual metrics:
| Metric | Before the sprint | After the sprint | Change |
|---|---|---|---|
| ChatGPT recommendations of Deckard | 2 | 22 | 11× |
| ChatGPT mention rate | 8.0% | 22.7% | nearly tripled |
| Recommendation rate in vendor evaluation queries | 7.3% | 23.4% | tripled |
| Recommendation count (across all platforms) | 41 | 80 | doubled |
| Total brand mentions | 94 | 147 | +56% |
| Total own-site citations | 94* | 140 | +49% |
| ChatGPT citation rate | 6.0% | 14.7% | 2.4× |
Baseline: April 8, 2026. Final report: May 19, 2026. 150 questions covering the US LTR compliance buyer journey, asked across each of the four major AI engines.
* At baseline, every brand mention happened to come with a website citation, so the two counts match. By the latest report, additional brand mentions had been generated in AI responses that didn’t always link back to the site.
Who is Deckard?
Deckard Technologies is a global govtech platform serving 500+ jurisdictions across the United States and Australia. Their flagship product, Rentalscape, uses patented Forensic AI to identify unregistered rental properties for cities, counties, and states, and has been the fastest growing short-term rental (STR) compliance platform in the US for several years.
In early 2026, Deckard launched Rentalscape LTR: a new product extending the same Forensic AI engine to long-term rental compliance. STR compliance is a mature, contested category; LTR is largely greenfield, with most jurisdictions still running landlord registration on spreadsheets.
Chloe Sasson, Chief of Staff and Director of Go To Market at Deckard, was leading the go-to-market for the launch. What was missing was AI visibility for a brand-new product in a category most buyers didn’t yet know existed.
The moment it clicked
Chloe had been a BrandViz platform customer since January 2026. Her early reaction set the tone:
In just the first 20 minutes of accessing BrandViz and working through the report, I was super impressed. I had cancelled my very expensive subscription to another market leading tool a few months back, and I can already see BrandViz filling this gap, and with more actionable recommendations.
Chloe Sasson, initial BrandViz platform feedback, January 2026In March, Chloe reopened the conversation with a sharper alternative than the original Australia plan - a forward-looking launch motion for the US market, rather than a retrospective on an existing product:
We are about to launch a new product in the market around Long Term Rentals (currently we do Short Term Rentals). You can see from our reports that we have limited visibility, so I’d love to set up what a baseline looks like, and how we can increase this over the next few months.
Chloe Sasson, email, 9 March 2026BrandViz proposed a done-for-you sprint pairing technical foundation work, content production, and the LTR launch baseline. Chloe approved on the spot.
What was the new product’s AI visibility before the sprint?
At baseline, Deckard’s brand already carried real weight in AI search, anchored by Rentalscape’s established STR position. But for the new LTR product specifically, the picture was thin. AI models routinely conflated Rentalscape LTR with Rentalscape STR, and in high-intent vendor evaluation queries where buyers compare platforms, Deckard was being recommended only 7.3% of the time. The job for the sprint was to make the new product visible on its own terms while extending Deckard’s category lead.
What did BrandViz.AI do for Deckard’s product launch?
BrandViz.AI’s sprint had two jobs. Defend the existing STR position, where Deckard was already contested. Build the LTR position from scratch, where buyers’ first AI-mediated impressions were still forming. The work covered two layers, in 8 weeks.
Layer 1 - Technical foundation
Building the AI-extractable foundation is detailed, unglamorous work most brands skip - yet it determines whether AI models can attribute anything to you at all. BrandViz shipped the foundation end-to-end: JSON-LD schema (Organization, Service, FAQPage, Article) across every key page, automated through BrandViz’s HubSpot integration so new content shipped with proper structured data from publication. Meta descriptions rewritten for accuracy and category positioning. About Us and core product pages restructured for AI entity resolution. Mid-sprint, a canonicalisation issue on the LTR product page was diagnosed and resolved without disrupting Deckard’s parallel website refresh.
I had no idea how to approach fixing the schema part of this, and my own searches still left me confused. Having the team manage this was painless and filled me with confidence. It was like ‘We’ve got this’ and the team resolved.
Chloe Sasson, from the case study interviewLayer 2 - Content authority
Each piece is deeply researched - drawing on internal documentation, sales conversations, and broader category research - and structured to perform for both AI extraction and human readers. That dual-audience balance is hard to strike: AI-optimised content reads thin to humans, human-written content reads invisible to crawlers. Getting it right shifts both AI visibility and buyer perception at once.
For Deckard as a govtech platform serving 500+ jurisdictions, the bar was even higher: content had to be accurate to regulatory frameworks and careful with compliance language.
As a marketer, what impressed me about the content workstream was the quality, clarity and volume produced. If I had briefed an internal team, this would have been several weeks if not months of work. With an agency, this would have been thousands in investment and a lack of product or positioning understanding.
Chloe Sasson, from the case study interviewFor Deckard, BrandViz shipped 16 LTR-focused pieces across the sprint, each targeting a specific buyer-journey question that AI models were being asked but no one was authoritatively answering. Examples included definitional content (What Is LTR Management? A Plain-Language Guide for Local Governments), practical guides (The Local Government Guide to Long-Term Rental Compliance and How AI Discovers Unregistered Long-Term Rentals), and problem-awareness pieces (Tracking Rental Licenses in Spreadsheets? Here’s Why That System Is Already Broken). Three core product and company pages (LTR Solutions, About, Careers) were also rewritten for AI extraction.
Each piece went through Chloe’s thorough internal review process - sent to her sales team and head of sales before publishing, with rapid iteration cycles across every article.
It’s just multi-useful - beyond what I was hoping for.
Chloe Sasson, from the weekly call, 30 April 2026What were the results?
By the final report on May 19, Deckard had widened its US LTR category lead to 45.9% of mentions and 50.4% of citations - more than the next four competitors combined - and ChatGPT recommendations of Deckard had grown 11×, from 2 to 22. Three things shifted that matter for a B2B launch.
Deckard owns the category in AI responses.
Across the US LTR compliance category, Deckard ended the sprint with more brand mentions than its next four competitors combined and more than half of all category citations - leader-level presence built from scratch for a brand-new product, in 8 weeks.
Deckard.com’s citations more than doubled, from 130 to 290, and the breadth of cited URLs grew from 19 to 34 - AI models are now finding Deckard’s content across the full LTR buyer journey, not just one or two anchor pages.
Buyers comparing platforms now see Deckard.
In the high-intent queries where someone is actively asking AI to compare vendors before deciding, Deckard’s recommendation rate moved from being recommended in ~1 in 14 responses to ~1 in 4.
ChatGPT now consistently surfaces Deckard.
On the largest AI engine by usage, mention rate nearly tripled, citation rate more than doubled, and recommendations of Deckard grew 11×.
Deckard’s citation rate (purple) on ChatGPT climbs from baseline through the sprint, ending the period as the leader.
Forensic AI is now Deckard’s term in AI answers.
At baseline, a query asking ChatGPT about US providers of Forensic AI returned:
ChatGPT, baseline report, 8 April 2026
“I can’t determine any specific U.S. provider that offers a product literally described as ‘Forensic AI’… based on available public search results I found.”
By sprint end, the same query opened with:
ChatGPT, final report, 19 May 2026
“The vendor you’re referring to is Deckard Technologies. The company explicitly states it leverages ‘patented processes’ and AI/data analysis to identify and validate rental properties. It has been granted multiple U.S. patents related to property data collection and detection of rental activity.”
And it’s not only ChatGPT. By the final report, Claude was citing deckard.com directly in problem-recognition queries - earlier-stage questions like “we suspect many properties are operating as undeclared traditional rentals in our community,” where buyers describe the problem before naming a category or vendor:
Every platform moved; ChatGPT moved the most.
wooo!!!
What changed for Deckard?
The sprint produced more than visibility numbers. It gave Chloe and her team clarity on a space that had been opaque, an education on how AI search works, and a working playbook they now run themselves.
When asked at the wrap-up what was most valuable, Chloe led with the learning:
Overall the most valuable thing was just learning about the space. I probably appreciate the platform more. Then the second piece is just how you guys approach content.
Chloe Sasson, from the sprint wrap-up call, 20 May 2026That clarity came from how the work was structured:
Before, everything was a black box - everyone goes out with their different platforms and vendors, but you guys made it so simple because you’ve tied it back to the platform. It’s really easy to see what the next steps are. I now feel a lot more confident using the platform and working to keep this up.
Chloe Sasson, from the sprint wrap-up call, 20 May 2026And the commitment to keep going:
We’ve now been to the gym, we’ve got our program, we’ve built up some muscles, and I can’t just go back on sitting on the couch and expect my muscles to stay there.
Chloe Sasson, from the sprint wrap-up call, 20 May 2026On what producing this content volume would have required in-house:
Two years ago I would have hired a junior marketing manager to do this.
Working with BrandViz at a lighter monthly cadence, Chloe is rolling the same approach to Deckard’s other blogs, state-specific LTR pages, and other Rentalscape features. The Claude mention rate kept climbing past sprint-end:
Claude mention rate (green) reaches 48.7% by 26 May, a week after the official sprint closed - showing the compounding effect of the foundation work.
Is AI visibility your blind spot?
94% of B2B buyers use AI during their purchase journey. In any given category, just five brands capture roughly 80% of all AI-generated responses. In US LTR compliance, one of those five spots now belongs to Deckard - by a wide margin. Most GTM leaders haven’t yet realised that AI visibility is its own discipline. It moves on different timescales from SEO, and the buyers they’re trying to reach are forming opinions through AI before any sales call happens.
For B2B teams running a new product launch, the real question is when to do this work: during the launch window when AI visibility is cheapest to build, or after competitors have already claimed the shelf space.
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Written by
Rushana Maksudova
Co-Founder & CEO
Co-founder and CEO of BrandViz.AI. Former Site Reliability Engineer at Google, where she also designed and led technical training across ten global offices. She now leads GTM, client delivery, and content at BrandViz.AI — helping brands understand and act on the shift from traditional search to AI-driven discovery.
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