How Hal9 Grew AI-Referred Organic Traffic 16x in 60 Days

"Seeing the data laid out like this moves the conversation from subjective opinions to objective facts. We know exactly where to focus our efforts."

— Javier Luraschi, CEO of Hal9

At a Glance

The Challenge: The "Sci-Fi" Hallucination

Hal9 built a world-class low-code AI platform. But to Answer Engines (ChatGPT, Gemini, Claude), they didn't exist.

Because of Semantic Drift, the models defaulted to their strongest training data: the fictional "HAL 9000" from 2001: A Space Odyssey.

The Revenue Leak: High-intent buyers asking "What is Hal9?" were told it was a movie character. This wasn't just a branding error; it was an instant killer for the purchase journey.

The Monitoring Gap: Traditional SEO dashboards showed Hal9 ranking for keywords, but failed to detect that the narrative was completely broken.

The "Before" State: Manual Verification

When we queried the world's most powerful models, we found total Identity Erasure:

• Gemini: "It appears you might be asking about HAL 9000, a fictional artificial intelligence..."
• Claude: "HAL9 is a fictional artificial intelligence character... iconic representation of AI in fiction."
• Llama: "I don't have information on 'hal9'."

Figure 1: Before optimization, Perplexity surfaced outdated or incorrect narratives about Hal9, including legacy positioning and references to unrelated sources.

The Optimly Solution: Agents + Humans (No Slop)

Instead of spamming the web with low-quality content ("Slop"), Optimly deployed a targeted Agentic Remediation strategy.

Step 1: Autonomous Signal Detection (The Agents)

Optimly's monitoring agents traced the Messaging Misalignment back to specific Legacy Anchors—outdated GitHub READMEs and legacy press releases—that were cluttering the model's vector space.

Step 2: Human-in-the-Loop Engineering (The Strategists)

We didn't just write more content; we executed Precision Source Updates:

• Entity Disambiguation: We rewrote the Source Truth (Homepage H1s, Technical Docs) to explicitly differentiate the software from the cinematic character using LLM-native tokens.
• Semantic Alignment: We aligned Hal9's description with the specific latent vectors models use to define "B2B Data Platforms."

"This is a very easy 'yes.' We're just going to go through the list. It's all about making it super consistent, and this is the path to do that."

— Javier Luraschi, CEO of Hal9

The Results: 16x Growth in 60 Days

Traffic remained flat until Optimly's Source Truth fixes were deployed, followed by an immediate vertical lift.

Metric 1: +1,600% AI-Referred Traffic

Cumulative increase in high-intent visitors coming directly from ChatGPT and Perplexity citations.

Metric 2: 300% Accuracy Lift

Perplexity "Brand Accuracy Score" jumped from 2.0 (Fictional Villain) to 8.1 (B2B Platform).

Metric 3: Zero Messaging Misalignment

Identity-level errors were eliminated across all major models within 60 days.

The "After" State: Strategic Clarity

Figure 2: After optimization, Perplexity adopted Hal9's updated positioning and cited the refreshed GitHub page, accurately reflecting Hal9's current capabilities.

(Note: This figure is likely an underestimate, as it relies on last-click attribution and does not capture the full influence of AI answers on buyer behavior.)

The Flywheel of AI Influence

With their baseline reputation secured, Hal9 and Optimly are now expanding into category-level authority.

Using Optimly's proprietary Source URL Graph, the team is targeting "Competitive White Space" — the high-value queries where Hal9 can become the default answer across LLMs.

This creates a repeatable flywheel: