WRITER started as a machine learning translation company and has since blossomed into one of the most sought-after enterprise AI platforms—with hundreds of customers and a valuation approaching $2 billion.
The company’s story began in 2013, when founders May Habib and Waseem Alshikh—both raised in non-English-speaking households—connected over a shared belief that language barriers should never limit opportunity. They launched a translation platform capable of localizing complex materials like legal documents, mobile apps, and websites, leveling the playing field for non-native speakers. But as generative AI emerged, they saw a broader opportunity: transforming not just language, but brand voice and business context across every part of the enterprise.
That idea became WRITER, a startup offering an end-to-end enterprise generative AI platform. Today, companies across healthcare, finance, retail, consumer goods, and technology use WRITER to build and scale secure AI agents that automate workflows, improve decision making and help drive business outcomes.
One Platform, Infinite Use Cases
While some enterprises can spend a lot of time experimenting with how best to adopt AI into everyday workflows, WRITER helps its customers scale agents quickly. Its platform is designed with enterprise needs in mind—from proprietary LLMs and secure data integrations to brand and compliance enforcement. A drag-and-drop interface makes it easy for non-technical teams to build and deploy AI agents.
There are real-world applications in a number of industries that can help with processes including:
- HR: automating recruiting and onboarding tasks.
- Legal: enforcing trademark and brand guidelines in real time.
- Customer support: resolving simple inquiries with automatic responses.
- R&D: analyzing trial data and generating reports for expert and general audiences.
Thanks to its flexibility, companies can build hundreds—or even thousands—of specialized agents, each solving distinct problems.
Security, Data Privacy, and Control—Standard on WRITER’s Platform
WRITER offers enterprise-grade security:
- Customer data is never used to train its models.
- Data stays fully contained within the customer’s environment.
- Deployments can be hosted in secure private clouds.
To further protect customer data, WRITER has developed its own pipeline of synthetic data—artificial datasets that mimic real-world scenarios without exposing sensitive information. It enables the platform to train high-performing, domain-specific and foundation LLMs while maintaining strict privacy standards. WRITER’s model strategy is all about delivering models that enterprises can trust to reliably power not just one or two agents but thousands of agentic workflows for core business processes.
The Next Frontier Could Be Self-Evolving Models
WRITER’s long-term vision centers on self-evolving models—AI systems that learn from usage patterns and adapt in real time, without the need for manual retraining. For example, for fund reporting, the model can adapt to user preferences for pulling in data from various sources based on the fund on which the report is being run. If a data connector in an agent breaks, the model can diagnose and fix the error, and if there’s a new tool that’s better suited for creating fund reports, the model will detect and recommend it. “It’s learning behavior based on what you’re doing,” said Chung.
While still in early development, this concept reflects WRITER’s broader strategy: pushing beyond generative AI models toward adaptive systems that deliver continuous value without the need for constant manual training.
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