Synthflow AI, the enterprise platform known for powering conversational AI at scale, has introduced the BELL Framework, an OpenAI-driven lifecycle system designed to solve the biggest risks enterprises encounter when deploying voice AI in real-world environments. While the broader industry focuses on building larger models, Synthflow AI emphasizes that the core issue lies in the surrounding ecosystem areas like routing, testing, compliance, latency, and customer handoffs. Therefore, the company created BELL (Build, Evaluate, Launch, Learn) to unify the entire agent lifecycle into a single repeatable and measurable approach.

This new framework integrates directly into Synthflow AI’s platform, which already supports more than 65 million calls across over 1,000 enterprise deployments spanning healthcare, insurance, and large-scale contact-center operations. As organizations increasingly turn to AI to automate call volumes and reduce operational strain, Synthflow aims to provide a reliable and structured pathway to deploy high-performing voice agents.

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“Enterprises do not need more guesswork, they need guarantees,” said Hakob Astabatsyan, Co-founder and CEO of Synthflow AI. “BELL ensures your voice agents behave as expected before customers ever hear them. It is the difference between hoping your AI works and knowing it will perform on every call.”

Breaking Down the Four Pillars of BELL

To eliminate common failure points in voice AI, Synthflow engineered each stage of BELL with targeted capabilities:

Build: Teams can design voice agent logic through a visual flow interface, manage variables, and coordinate multi-agent systems to ensure predictable and consistent results.

Evaluate: A dedicated testing center simulates hundreds of complete phone calls. It measures accuracy, CSAT, and task completion to validate performance before agents go live.

Launch: Instead of relying on external vendors, Synthflow operates on its own global telephony infrastructure. This enables sub-100 ms latency and a 99.9% uptime guarantee, ensuring real-time responsiveness.

Learn: With detailed logs, analytics dashboards, and built-in Auto-QA, enterprises can evaluate every call, webhook, and event to refine performance continuously.

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This structured lifecycle addresses a major industry challenge. Large enterprise call centers handle millions of repetitive interactions from billing queries to account verifications that overwhelm human agents and increase customer wait times. Although voice AI appears to be the ideal solution, many deployments ultimately fail due to brittle integrations, routing errors, latency issues, and unpredictable agent decisions.

“Most voice AI dies in production because teams cannot control the layers that define performance,” said Sassun Mirzakhan-Saky, Co-Founder and CTO of Synthflow AI. “By owning the telephony, testing, and orchestration layers, we have removed that uncertainty. BELL makes success repeatable.”

Additionally, Synthflow leverages OpenAI’s GPT-4.1, GPT-5, and GPT-5.1 model family across its system. The platform intelligently selects models with optimal reasoning capabilities to maintain real-time accuracy and high-quality responses.

Eva Spannagl, Head of Startups, DACH, at OpenAI, highlighted this collaboration, adding: “Our frontier AI models and developer tools help customers like Synthflow create enterprise-grade, real-time systems. Synthflow is a great example of how to build on our models to create reliable, production-ready outcomes from day one.”

Ultimately, by building the BELL Framework into its no-code platform, Synthflow AI empowers enterprises to develop, test, and deploy robust voice agents within weeks not months while maintaining full compliance with SOC 2, HIPAA, and GDPR requirements.

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