Ollang scales media localization with 76% less manual processing after integrating Voice AI

The media localization landscape faces unprecedented demand as streaming platforms, broadcasters, and content creators require sophisticated multilingual content processing at scale. Founded in 2019, Ollang provides AI-enabled closed-captioning, subtitling, and dubbing services in more than 100 languages, working with streaming platforms like Netflix and YouTube, TV broadcasters, e-learning platforms, and social media content creators.

Ollang operates as a comprehensive AI-driven platform, offering multilingual, multimodal, and multi-agent capabilities engineered for localization, multilingual support, and data analysis—optimized for speed, quality and security. Led by founder and CEO Ebru Yildirim, the company addresses the critical challenge of achieving cultural accuracy in localization through agentic AI that dynamically selects the best models and continuously self-corrects for enhanced performance across languages.

Ollang built its competitive advantage around a sophisticated multi-agent AI architecture designed to deliver production-ready media localization at scale. However, the platform's success depended entirely on having exceptionally accurate transcription as the foundational first step for video processing workflows.

Transcription accuracy bottleneck

Existing cloud providers delivered insufficient accuracy for non-English audio, with poor punctuation and capitalization handling that cascaded errors throughout Ollang's entire multi-agent workflow.

Production quality standards

Media clients working with extensive video libraries—long interviews, industry events, and hundreds of series episodes—demanded production-ready results without compromise. Any transcription errors would undermine Ollang's value proposition to streaming platforms and broadcasters.

Scaling challenge

To serve major clients and handle complex localization projects at scale, Ollang needed transcription technology that could consistently deliver near-perfect accuracy across multiple languages and challenging audio environments.

Recognizing that transcription accuracy would make or break their platform strategy, Ollang's engineering team prioritized finding AI speech-to-text technology that could serve as a reliable foundation for their multi-agent AI system.

Strategic integration approach

Rather than building transcription capabilities in-house, Ollang focused its development resources on its core differentiator—the multi-agent orchestration platform that dynamically selects optimal models and continuously self-corrects for enhanced performance.

Technical requirements

The team needed transcription technology that could handle non-English audio with exceptional accuracy, provide automatic speaker identification with word-level labeling, and integrate seamlessly into the platform's existing multi-agent architecture.

Production-ready output

Most critically, Ollang required consistent quality that would enable the platform's downstream AI workflows to deliver results that meet professional media production standards.

The company selected AssemblyAI's Universal Speech-to-Text API to serve as itstranscription foundation, enabling the product team to focus on building the platform'sproprietary multi-agent orchestration capabilities.

The state-of-the-art Universal Speech-to-Text model boasts more than 93.3% accuracy, even on noisy audio, providing the industry's lowest word error rate (WER). The model also supports multilingual transcription across numerous languages, with more languages being continuously added.

Ollang's engineering team executed a remarkably efficient integration that immediately enhanced their platform capabilities:

One-week deployment

The technical integration took approximately one week—including testing and deployment— enabled by a straightforward API architecture that fit seamlessly into Ollang's existing multi-agent system.

Multi-agent enhancement

The improved transcription accuracy immediately enhanced every component of Ollang's platform—from captioning and subtitle translation to dubbing workflows—enabling the system to make faster, more confident automated decisions.

Scalable architecture

The integration positioned Ollang to handle diverse content types at scale, from social media clips to extensive series libraries and educational content across 100+ languages, all while maintaining production-ready quality standards.

The enhanced transcription foundation delivered transformative results across Ollang's media localization operations:

1. 76% reduction in human-in-the-loop effort: By improving the accuracy of its foundational transcription layer, Ollang dramatically reduced manual intervention requirements across its entire production workflow, enabling true scale in media localization services.
2. 30-40% improvement in overall platform accuracy: Enhanced transcription quality cascaded through Ollang's multi-agent system, reducing error rates significantly across all content types—particularly crucial for the non-English audio that represents the majority of global media localization demand.
3. 97%+ production-ready results: For most content types, Ollang's enhanced multi-agent system now consistently achieves near-production-ready results without human intervention—a breakthrough that fundamentally changes the economics of media localization.
4. 25% increase in autonomous service orders: More clients now place AI dubbing orders without requesting human review, reflecting the improved reliability of Ollang's end-to-end platform and enabling higher-margin, fully-automated service offerings.

Ollang now delivers production-ready transcripts directly from its multi-agent system for many video types, fundamentally changing its service delivery model from human-intensive to AI-first operations while maintaining professional quality standards.

The enhanced platform capabilities position Ollang for continued growth in the rapidly expanding media localization market:

Advanced technology integration

Ollang continues expanding its multi-agent platform capabilities, currently testing next-generation models for enterprise use cases with plans for production integration based on promising early results.

Market expansion strategy

The combination of improved accuracy and reduced operational overhead enables Ollang to pursue larger streaming platforms and handle more complex media localization projects at scale, supporting the company's $1.5M seed funding growth trajectory.

Competitive differentiation

Ollang's ability to deliver production-ready results with minimal human intervention creates significant competitive advantages in the media localization market, particularly when competing for contracts with streaming platforms and broadcast clients who demand both quality and scale.

Service portfolio expansionThe reliable AI foundation enables Ollang to expand beyond traditional localization services, developing new offerings around its core multi-agent platform capabilities.

Ollang transformed its multi-agent AI platform from requiring extensive human oversight to delivering production-ready results with 76% less manual intervention, enabling its product team to serve streaming platforms and media clients at scale while building a sustainable competitive moat in the professional localization services market.