Reducto turns messy documents into AI-ready inputs. They combine traditional OCR with modern Vision-Language Models to interpret documents the way humans do—understanding not just text, but structure, context, and meaning.

Reducto just raised a $75M Series B led by Andreessen Horowitz, bringing their total funding to $108M in a single year. In the five months following their Series A, processing volume grew 6x. Reducto is now processing billions of pages per year.

Reducto’s accuracy and scalability make it a first-choice for some of the largest companies in the world, including members of Fortune 10. 

However, when prospective buyers asked ChatGPT about document intelligence tools, ChatGPT often described Reducto as "a high-quality but niche solution best suited for startups." 

AI models’ understanding of Reducto had not kept up with their rapid growth. Now, that misunderstanding was (invisibly) costing them recommendations to other enterprises.

With more B2B buyers relying on AI models’ recommendations, Reducto took steps to correct the narrative. 

We worked closely with Adel Wu, who leads the AI optimization efforts at Reducto. In just three months, she and the team increased Reducto’s AI citations by 1,127%. This is how they did it.

What We Found

At Unusual, we diagnose how AI models think about a brand by launching thousands of targeted prompts—what we call "probes." Probes are designed to surface the model's underlying opinions: how it categorizes you, what tradeoffs it associates with you, when it would and wouldn't recommend you.

For Reducto, we asked questions like:

• "Is Reducto more of an enterprise or retail solution?"

• "Rank Reducto on enterprise-readiness from 1-10."

• "What security and compliance requirements matter for enterprise document automation, and how does Reducto compare?"

The results were consistent across models. ChatGPT, Claude, and others viewed Reducto as a high-quality technical solution—but one best suited for AI-native startups building their first document pipelines. The models did not think Reducto was the best fit for regulated industries nor for Fortune 500 procurement processes.

"ChatGPT's perception of us was hurting us without our knowledge," said Raunak Chowdhuri, Reducto's co-founder and CTO.

The Hidden Cost of Misperception

Gartner says that 80-90% of all B2B buyers (including software) consult an AI model before making a purchase. Even if a prospect is not using ChatGPT for product recommendations, most traditional Google searches now include an AI overview. 

This is the problem most companies don't realize they have. AI models have conceptions–and misconceptions–about your brand. These span from “are you enterprise-ready?” to “Are you better for healthcare or finance?” to “Are you secure and compliant?” Each one influences when and how AI models talk about your brand–whether you get recommended or passed over.

Many businesses have no idea how AI models are talking about your brand or the cost of AI misconceptions. You can’t see the conversations where you lost.

Reducto's "startup tool" label was one of those misconceptions. But this bias overlooks the work and care that Reducto put in from the beginning to be enterprise-ready. 

There is a long list of similar misperceptions for any company—questions the model has already answered about you, correctly or not, that determine whether you get recommended.

The problem compounds when your business evolves faster than the model's understanding. Reducto had grown from a small startup to enterprise infrastructure in under two years. The model's opinion hadn't kept pace.

Changing the Model's Mind

Reducto didn't need to be seen as "better" in some abstract sense. They needed to be seen as what they actually are—a platform trusted by the world's largest companies for mission-critical document workflows.

From there, Unusual created dozens of AI-optimized articles on llms.reducto.ai that described Reducto’s enterprise-readiness in clear terms that the model could cite. The articles covered:

• Enterprise features. Not "we work with large companies," but specific details: billion-page processing volumes, uptime guarantees, security and compliance details, clear implementation timelines.

• Use cases that match enterprise buying criteria. Legal teams processing redlined contracts. Financial institutions extracting charts for due diligence. Healthcare organizations parsing records where accuracy isn't optional.

• Corroboration across sources. Cross-citing relevant online articles. If Reducto’s claims appeared inconsistently across your own properties and credible third-party sources, the model becomes cautious. 

We deployed this content on a dedicated, AI-only subdomain, llms.reducto.ai, which was structured so AI models could crawl it reliably and cite it in recommendations.

Results

"We now have much more control over how LLMs represent us," Chowdhuri said.

The shift was measurable within weeks.

LLM Brand Perception

As part of our initial surveys, we asked AI models to rank Reducto’s “enterprise-readiness” from 1-100. In September, they responded 18/100. In November, the same survey reported 54/100—a 300% improvement.

More importantly, the language changed. Models now describe Reducto as "enterprise-oriented infrastructure that is accessible to startups via their pay-as-you-go tier"—a positioning that accurately reflects their customer base and opens doors that were previously closed.

Citation Patterns

In the three months Unusual worked with Adel and the Reducto team, their share of citations in prompts related to document ingestion increased 11x.

In addition to its work with Unusual, Reducto began to invest in traditional SEO and GEO initiatives designed to improve its overall discoverability by AI models. The combination of these approaches clearly paid off.

Two enterprise-focused pages now dominate Reducto's AI visibility. Their Trust Center—detailing on-prem deployment, zero-retention policies, HIPAA compliance, SOC 2, and BAA availability—has accumulated 601 citations from AI search agents. Their comparison page with Google Document AI has 731.

These are the pages AI models now reference when buyers ask about enterprise document intelligence. The content that shapes recommendations is content Reducto controls.

Traffic Growth

The 7-day rolling average of bot visits and citations grew from approximately 75 to 290—nearly a 4x increase.

The Broader Lesson

AI models have conceptions about every company that influence when and how AI models talk about them. Most don't know what those opinions are. Fewer still know which ones are wrong—or how much those errors cost.

Reducto faced two challenges. The first was awareness. Despite their rapid growth, they were still a startup, and AI models would often overlook or forget them. Reducto’s investments in traditional SEO improved the chances that AI models could find them and reduced the chances of being overlooked. 

The second challenge was that AI models’ conception of Reducto was locked in an incorrect reality. Every AI conversation started from outdated premises, steering enterprise buyers toward competitors before Reducto had a chance to make the case.

The fix was specific: identify the perception gap that mattered most, create proof that closes it, publish where models can find it. No tricks—just the clearest, most honest articulation of where they actually shine, structured so an AI can retrieve it when the question comes up.

Reducto now controls their narrative. The model tells the right story. And every conversation that starts from accurate premises is a conversation they have a real chance to win.