What happened
Braiin Limited has launched Force AI, a new orchestration layer integrated into its RADIUS contact center-as-a-service (CCaaS) platform. This update allows enterprise clients to deploy autonomous AI agents, human-agent co-pilots, and analytics tools across various cloud-based and open-source large language models (LLMs) without being restricted to a single provider. The platform is designed to support operations across voice, chat, email, social media, and video channels.
Braiin Limited announced the launch of Force AI, an orchestration layer that serves as the core of its RADIUS CCaaS platform. The system is designed to facilitate the use of multiple LLMs, enabling enterprises to integrate models from various providers into their customer service operations.
The platform powers three distinct modules: AI Engage, which handles autonomous customer interactions; AI Assist, a real-time co-pilot for human agents; and AI Insight, which provides automated analysis and quality scoring for customer conversations.
According to the company, the platform is currently available for global deployment across cloud, on-premises, and hybrid environments. Braiin reports that the underlying RADIUS platform has previously processed over 4.5 million customer interactions with a 99.9% uptime rate as of March 31, 2026.
The company claims that the orchestration layer allows for model switching based on cost, performance, or regulatory needs, effectively decoupling the contact center application from the underlying AI model provider.
Source details: markets.businessinsider.com
Why it matters
The launch of Force AI represents a strategic move by Braiin to capture a portion of the projected $56.9 billion contact center software market. By enabling model-agnostic orchestration, the company aims to address enterprise concerns regarding vendor lock-in, compliance, and cost management. This allows organizations to switch between different AI models—such as those from OpenAI, Anthropic, or Google—based on evolving performance or regulatory requirements, while maintaining a centralized command center for customer interactions.
The contact center software market is a high-value sector, with estimates placing its 2026 valuation at $56.9 billion. Braiin's expansion is a direct effort to increase its market share by offering a flexible, multi-model approach to AI deployment.
Vendor lock-in is a significant concern for large enterprises, particularly in regulated industries like finance and healthcare. By providing a platform that supports 'any leading LLM,' Braiin aims to mitigate the risks associated with relying on a single AI vendor's roadmap or pricing structure.
The integration of agentic AI—systems capable of performing tasks autonomously—into standard contact center workflows marks a shift toward more proactive customer service, moving beyond simple conversational bots to outcome-driven automation.
Interactive Mechanism: How It Actually Works
Explore the underlying technology behind this development interactively.
crm_get_transaction(id='4092').An agent must create a draft calendar event for Tuesday at 2 p.m. Which evidence would establish the requested result?
What to watch next
Market adoption of the Force AI layer among Braiin's existing client base in sectors like healthcare, finance, and government will be a key indicator of the platform's efficacy. Observers should monitor whether the promised flexibility in model switching translates into measurable cost savings or performance improvements for enterprise users, as well as how the company manages the technical complexities of integrating diverse LLMs into a unified, compliant contact center environment.
The practical performance of Force AI in high-volume, regulated environments remains to be seen. While the company cites indicative results from selected deployments, the actual efficacy of switching between disparate LLMs in a production environment is a significant technical challenge.
The company's ability to maintain compliance and security standards while orchestrating multiple third-party models will be critical for its target enterprise and public-sector clients.
Future updates regarding the specific LLMs supported and the ease of integration for proprietary or custom-trained models will be important for assessing the platform's long-term viability.