Kiteworks Quadrupled Non-Brand Traffic with Quattr.
Kiteworks Quadrupled Non-Brand Traffic with Quattr.
A leading legal intelligence platform is dedicated to making state and federal court data accessible and actionable. The platform connects legal professionals and the public with critical information across millions of programmatically generated pages by transforming complex legal documents into a powerful and easily searchable resource.
The company's growth-focused SEO and engineering teams wanted to unlock new avenues for organic traffic across their vast content ecosystem. Recognizing that traditional internal linking methods were insufficient for their scale, they initiated a strategic collaboration with Quattr.
Together, the teams designed and launched a rigorous, controlled experiment to statistically prove the impact of an automated, data-driven linking strategy. This partnership turned a complex SEO challenge into a validated, high-impact growth opportunity.
THE CHALLENGE
The company built technology to collect millions of legal records and transform them into valuable data products for the legal community, using that content as a search engine acquisition channel. However, the team faced a paradox that many large-scale content platforms eventually encounter: the more content they published, the more difficult it became for users and search engines to find what they needed.
Manual linking was mathematically impossible. With millions of pages indexed in Google and thousands of new cases published daily, even a large editorial team couldn't keep up.
Rule-based systems created relevance failures. The engineering team had built what seemed like a logical solution: a database-driven internal linking system based on fixed attributes, such as jurisdiction, case type, or filing date. In theory, it made sense. In practice, it wasn't enough to sustain consistent growth as Google's algorithms evolved and search behavior shifted with the rise of LLM ecosystems.
Search engines have limited bandwidth to understand site structures. Without strong semantic connections between pages, Google's crawlers struggled to understand which pages were authoritative for which topics. Rankings stagnated. The platform's expertise wasn't being surfaced for the long-tail legal queries that drive qualified traffic.
The team needed a solution that could:
1. Scale automatically across millions of pages
2. Understand semantic relevance, not just keyword or URL patterns
3. Adapt continuously as new content is published daily
4. Work without consuming engineering resources on an ongoing basis
Most importantly, they needed proof that it would work before committing to a platform-wide rollout.
THE SOLUTION
Rather than gambling on a full platform deployment, the team took a scientific approach: design a controlled experiment that would prove causality and quantify the opportunity.
We've all seen vanity metrics that claim success based on correlation rather than causation. The team was determined to avoid that trap.
The experimental design:
1. Treatment group: ~50,000 New York case pages would receive 10 AI-powered internal links each from the same basket of treatment URLs
2. Control groups: Massachusetts and Florida pages would receive no changes, allowing the team to isolate the internal linking impact from external factors like algorithm updates or seasonal patterns
3. Data sources: Google Search Console Bulk Export (URL-level daily clicks and impressions) and Googlebot crawl logs (to understand search engine behavior)
4. Timeline: Pre-period from August 6 to September 2, 2025; treatment deployed September 3; post-period through September 30
The rigor of this approach meant that when results appeared, the team could confidently attribute them to internal linking, not luck. Florida and Massachusetts results predicted New York clicks with accuracy in the pre-period, establishing a solid baseline for comparison.
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Quattr's Autonomous Linking API solves a problem that seems simple but is actually quite complex: determining which pages should link to each other when you have millions of options.
The system uses two intelligent data sources working in tandem:
Instead of matching pages by keywords or metadata, the system analyzes the actual content to understand semantic relationships. It can recognize that a breach of contract case involving commercial real estate shares meaningful context with another case about lease disputes—even if they use different legal terminology and occurred in different jurisdictions.
This mirrors how an expert legal researcher thinks, not how a database filter operates.
Here's where it gets interesting. Rather than guessing what anchor text might be relevant, the system pulls actual search queries that users are typing into Google, queries that are already driving traffic to these pages.
If real users are searching "statute of limitations contract dispute New York" and landing on a particular case page, that query becomes a potential anchor text candidate for internal links. The anchor text isn't based on assumptions; it's based on proven search demand. Quattr offers an entire workflow to construct and prompt anchor text generation with these demand signals.
Why this matters for executives:
1. Zero ongoing manual work: The API continuously generates recommendations as new content is published, sourced from XML and RSS feeds. No editorial bottleneck.
2. Fast implementation: Quattr's team integrated the API with minimal engineering time required from the client.
3. Self-optimizing: As search behavior evolves and new cases are added, the recommendations adapt automatically.
This is where many case studies resort to hand-waving. The team took the opposite approach, employing multiple statistical methods to ensure the results were genuine.
Within 7 days: The New York pages showed a statistically significant 12% increase in organic clicks compared to the pre-period, while control markets (Massachusetts and Florida) remained flat. This early signal suggested the treatment was working.
At 28 days, the full picture emerged:
Using regression counterfactuals (a method that predicts what would have happened without the intervention), the analysis revealed:
1. +15.1% daily click uplift when comparing treated NY pages to untreated NY pages (+580 clicks/day; +12,772 cumulative clicks)
2. +19.4% daily click uplift when comparing treated NY pages to the FL+MA control states (+748 clicks/day; +16,472 cumulative clicks)
The CTR impact:
It wasn't just more traffic from the same impressions. The CTR increased by 0.47 percentage points (roughly +5.5%), while impressions remained essentially flat (-0.7%). This meant the platform was converting more of its existing visibility into clicks and users were finding the links more relevant & engaging from their newfound visibility in Google.
The consistency advantage:
One of the most interesting findings emerged when analyzing which pages were appearing in the top rankings by overall clicks. Traffic often experiences bizarre spikes and drops, but the treated URLs involved with the internal linking proved to be more resilient. New York case pages showed significantly more "days with clicks" compared to Florida and Massachusetts pages, indicating sustained performance rather than temporary volatility.
THE RESULT
1. Week 1: +12% organic clicks (statistically significant vs. controls)
2. 28 days: +15-19% daily click uplift (control-adjusted)
3. Full pilot period: +13,400 additional clicks captured that wouldn't have existed otherwise
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The control markets remained stable throughout, confirming causality.
The pilot's validated performance revealed the true opportunity:
1. Current platform baseline: 29.5 million annual organic clicks
2. Conservative 12% uplift applied platform-wide: +3.5 million additional annual clicks
3. Monthly incremental gain: Nearly 300,000 additional organic clicks
But here's what makes this especially compelling for executives: this isn't theoretical. The 12% figure is based on week 1 results; the 28-day performance showed a 15-19% uplift. The projection uses the conservative estimate.
For a B2B legal intelligence platform, organic clicks translate directly to qualified leads, as search is their largest acquisition channel.
An additional 300,000 monthly organic clicks, assuming even modest conversion rates at each funnel stage, represents significant enterprise value, especially when the ongoing cost is near zero, as the API operates automatically when new content is published.
If your organization operates at scale, whether that's e-commerce, publishing, SaaS, or professional services, you likely face a version of this same challenge: how do you maintain relevance and discoverability when your content library grows faster than editorial teams can curate?
The traditional answer has been "you can't." This case study proves otherwise.
The combination of semantic AI and real demand signals can make intelligent decisions across millions of pages, continuously, without human bottlenecks. The result isn't just more traffic, it's more relevant traffic that converts better, driven by an automated growth engine that compounds over time.
For executives evaluating similar opportunities, the question isn't whether AI-powered internal linking can work at scale. This pilot proved it can, with statistical rigor. The question is: how much growth are you leaving on the table by relying on manual processes or rule-based systems that can't keep pace with your content?
Quattr's AI-first platform evaluates like search engines to find opportunities across content, experience, and discoverability. A team of growth concierge analyze your data and recommends the top improvements to make for faster organic traffic growth. Growth-driven brands trust Quattr and are seeing sustained traffic growth.
And we’re happy to share the wisdom. Here are some resources curated just for you:
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