Circles is using AI to turn telecom support from a cost centre into a faster, more personalised growth engine
Updated
May 1, 2026 2:04 PM
A woman holding a phone while using a laptop. PHOTO: ADOBE STOCK
Circles, a Singapore startup that builds software for digital telecom operators, has launched an AI concierge as part of its partnership with OpenAI. The release marks a new step in the company’s effort to modernise how telecom providers serve and retain customers. The move reflects a wider shift in the telecom sector. Many operators still rely on older support systems that can be slow, fragmented and costly to run. AI is now being tested as a way to improve service while creating new revenue opportunities.
Circles said the concierge is built on OpenAI’s API platform and sits within what it calls an AI-native telecom stack. In practical terms, the system is designed to handle customer support, account changes and personalised offers through automated interactions.
One part of the platform is called CareX. According to the company, it can deal with billing issues, service requests and network-related problems. Circles said CareX currently resolves 85% of customer queries globally without human intervention and reaches a 95% resolution rate on end-to-end tasks. That matters because customer support remains one of the larger operating costs for telecom providers. Faster automated handling could lower pressure on service teams while reducing wait times for users.
The second part of the platform is Xplore IQ, which focuses on revenue growth. The tool is designed to predict what a customer may need, recommend a suitable plan or offer and complete upgrades or downgrades automatically. Circles said the early rollout has led to a 22% rise in average revenue per user for Circles.Life Singapore. It also said personalised offers helped reduce customer churn by 9%.
"AI should empower users - not force-fit into outdated journeys. OpenAI's role has been critical in enabling Circles to scale this vision globally. With the AI concierge, we are moving beyond providing simple answers to delivering real-world outcomes, along with balancing cost and latency to maximize value for operators and customers alike", said Awais Malik, Global Chief Growth Officer at Circles.
"Circles is demonstrating how advanced AI can modernize essential industries like telecommunications at scale. By combining frontier models with multi-agent systems, they are enabling telecom operators globally to deliver faster, smarter and more personalized customer experiences. This milestone is a strong example of how AI can deliver tangible value for businesses and customers they serve", Oliver Jay, Managing Director, International for OpenAI, added.
Together, the tools are intended to connect customer service, operations and sales into one system. Rather than treating support and monetisation as separate functions, the company is combining them into a single digital layer.
Circles said the partnership will continue over the next two years as both companies work toward a more autonomous telecom model. Whether that vision is achieved remains to be seen, but the direction is clear: telecom operators are increasingly treating AI as core infrastructure rather than an optional add-on.
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Examining the shift from fast answers to verified intelligence in enterprise AI.
Updated
January 8, 2026 6:33 PM
Startup employee reviewing business metrics on an AI-powered dashboard. PHOTO: FREEPIK
Neuron7.ai, a company that builds AI systems to help service teams resolve technical issues faster, has launched Neuro. It is a new kind of AI agent built for environments where accuracy matters more than speed. From manufacturing floors to hospital equipment rooms, Neuro is designed for situations where a wrong answer can halt operations.
What sets Neuro apart is its focus on reliability. Instead of relying solely on large language models that often produce confident but inaccurate responses, Neuro combines deterministic AI — which draws on verified, trusted data — with autonomous reasoning for more complex cases. This hybrid design helps the system provide context-aware resolutions without inventing answers or “hallucinating”, a common issue that has made many enterprises cautious about adopting agentic AI.
“Enterprise adoption of agentic AI has stalled despite massive vendor investment. Gartner predicts 40% of projects will be canceled by 2027 due to reliability concerns”, said Niken Patel, CEO and Co-Founder of Neuron7. “The root cause is hallucinations. In service operations, outcomes are binary. An issue is either resolved or it is not. Probabilistic AI that is right only 70% of the time fails 30% of your customers and that failure rate is unacceptable for mission-critical service”.
That concern shaped how Neuro was built. “We use deterministic guided fixes for known issues. No guessing, no hallucinations — and reserve autonomous AI reasoning for complex scenarios. What sets Neuro apart is knowing which mode to use. While competitors race to make agents more autonomous, we're focused on making service resolution more accurate and trusted”, Patel explained.
At the heart of Neuro is the Smart Resolution Hub, Neuron7’s central intelligence layer that consolidates service data, knowledge bases and troubleshooting workflows into one conversational experience. This means a technician can describe a problem — say, a diagnostic error in an MRI scanner — and Neuro can instantly generate a verified, step-by-step solution. If the problem hasn’t been encountered before, it can autonomously scan through thousands of internal and external data points to identify the most likely fix, all while maintaining traceability and compliance.
Neuro’s architecture also makes it practical for real-world use. It integrates seamlessly with enterprise systems such as Salesforce, Microsoft, ServiceNow and SAP, allowing companies to embed it within their existing support operations. Early users of Neuron7’s platform have reported measurable improvements — faster resolutions, higher customer satisfaction and reduced downtime — thanks to guided intelligence that scales expert-level problem solving across teams.
The timing of Neuro’s debut feels deliberate. As organizations look to move past the hype of generative AI, trust and accountability have become the new benchmarks. AI systems that can explain their reasoning and stay within verifiable boundaries are emerging as the next phase of enterprise adoption.
“The market has figured out how to build autonomous agents”, Patel said. “The unsolved problem is building accurate agents for contexts where errors have consequences. Neuro fills that gap”.
Neuron7 is building a system that knows its limits — one that reasons carefully, acts responsibly and earns trust where it matters most. In a space dominated by speculation, that discipline may well redefine what “intelligent” really means in enterprise AI.