Ollang Orchestrates 100+ AI Models to Turn Localization Into Growth Infrastructure

San Francisco-based Ollang runs more than 100 AI models through a multi-agent platform that localizes video, audio, text, documents and websites into over 240 languages — and its customers include Fortune 500 companies. Co-founder and CEO Ebru Yıldırım laid out the company's model, results and ambitions in an interview with Pulse 2.0.

One customer, the mobile video streaming platform Shorten, localized more than 10,000 short videos into 11 languages in three months. The company cut translation errors by 60% compared with other AI plus human-in-the-loop methods, saved 83% on English-to-Japanese localization costs, and reduced human effort by an estimated 76%, as measured by post-localization quality assessments. Another unnamed enterprise customer saved $600,000 in its first year after moving localization to Ollang.

Who is behind Ollang?

Yıldırım started as a translator and interpreter. She studied translation and interpreting at Marmara University and completed graduate work in France at the University of Orléans and the Sorbonne. She then worked across the language industry with global organizations including Accenture, IKEA and Eurotunnel.

"I've been the linguist producing the work, the operator managing it, the buyer responsible for getting it delivered, and eventually the entrepreneur running a language services business," she said. Before Ollang, she founded and ran a translation agency serving Fortune 500 clients. She co-founded Ollang with CTO Aziz Ulak, pairing her language and operations background with his engineering expertise.

How did the idea come together?

The problem, Yıldırım said, was not translation itself. It was execution. As a translator she moved constantly between documents, spreadsheets, videos, terminology databases and stakeholder feedback. As an agency owner she watched companies spend heavily coordinating vendors, managing files and shuttling content between disconnected systems. "Companies didn't have a translation problem alone. They had an execution problem," she said.

When AI changed what was technically possible, she chose to rebuild the whole process rather than speed up translation. Ollang unifies AI models, human expertise, quality control and delivery in one system that plugs into a customer's existing technology stack.

What does the platform actually do?

Ollang describes itself as an "AI execution layer." Localization costs compound because a single video requires script, subtitles, voiceover, on-screen graphics and accessibility features — each multiplied by every language. Going from five languages to fifty turns one video into hundreds of deliverables, which is why most companies expand slowly.

Ollang's agents act, in Yıldırım's words, as "specialized digital knowledge workers." They pick models based on quality, speed and cost, and run workflows end to end. Humans stay in the loop where production-ready output demands more than translation: a healthcare specialist reviews medical content, a technical linguist validates product documentation, a cultural expert checks a campaign's fit for a market.

The platform has supported more than 40,000 projects across over 25 content formats. Integrations include TikTok One, Google Drive and Figma, alongside APIs, SDKs and MCP capabilities.

Why does Ollang claim an edge over rivals?

Competitors sell translation. Ollang sells the infrastructure that removes the coordination burden around it, Yıldırım argued. "No company wakes up in the morning thinking, 'We need more translations.' They think about how to reach more customers, enter new markets and grow their business," she said.

The company's answer to enterprise AI's "last-mile problem" is a model-agnostic, multi-agent system that learns each customer's terminology, brand guidelines and quality requirements. A 60-minute television episode or a 50-slide training course demands consistency across hundreds of assets — terminology, brand voice, cultural nuance, fonts, layouts, brand colors. "The future of enterprise AI won't be determined by who can create the most impressive demo. It will be determined by who can turn AI capabilities into reliable, repeatable, production-ready outcomes at scale," she said.

What milestones has the company hit?

Ollang started in Türkiye, where the founders raised pre-seed funding and built a working product with early customers. Moving to San Francisco, Yıldırım said, "fundamentally changed our perspective on what we could build and how quickly we could build it." U.S. investors include Michael Montgomery, Zeal Capital Partners and JFF Ventures. Enterprises in automotive, social media, media and entertainment, and healthcare now use the technology.

What comes next?

Yıldırım's long-term vision: make language infrastructure as fundamental to global business as cloud computing or payments. Three priorities drive the roadmap:

• Advance the multi-agent AI infrastructure with deeper context management, automated quality evaluation and workflows requiring less manual intervention.
• Expand integrations so language execution becomes a native capability inside enterprise content systems.
• Target media and entertainment, where she argues AI-powered language infrastructure can make global distribution economically viable for far more content — for major studios and independent creators alike.

She also pushed back on the assumption that AI translation will make language professionals irrelevant. Their role, she said, is shifting from managing translation tasks to defining quality standards, governing intelligent workflows and advising on global market decisions. Localization leaders, in her framing, can move from cost managers to strategic partners in international growth — closing with her own question for customers: "How much more could we grow if language were no longer a barrier?"