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.
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Bitmo Lab is testing an ultra-thin, bendable tracker built to fit inside items traditional trackers can’t
Updated
February 12, 2026 4:43 PM
Bitmo Lab's MeetSticker tracker. PHOTO: BITMO LAB
Location trackers have become everyday accessories for keys, bags and luggage. But as personal items grow slimmer and more design-focused — from minimalist wallets to passport sleeves and specialised gear — tracking them has become less straightforward. Most trackers are built as small, rigid discs that assume the presence of space, loops or compartments. That assumption has created a growing mismatch between modern product design and the technology meant to secure it.
Hong Kong–based startup Bitmo Lab is attempting to address that gap with a device called MeetSticker. Instead of the solid plastic casing typical of most trackers, MeetSticker is engineered to be flexible and ultra-thin, measuring just 0.8 millimetres thick. The bendable design allows it to sit within narrow compartments or along curved surfaces without altering the shape of the object. Rather than attaching to an item externally, it is intended to integrate discreetly inside it.
That structural shift is the core of the product’s proposition. By removing the rigid shell that defines conventional tracking hardware, MeetSticker can be placed in items that previously had no practical way to accommodate a tracker. Bitmo Lab states that the device connects through a proprietary network and a companion application compatible with both iOS and Android, positioning it as a cross-platform solution rather than one tied to a single ecosystem.
The implications extend beyond form factor. Objects without obvious attachment points — such as compact travel accessories or specialised tools — could potentially be monitored without visible add-ons. In doing so, the device broadens the scope of tracking technology into categories where aesthetics, aerodynamics or compact design matter as much as functionality.
Before moving toward retail distribution, however, the company is focusing on validation. Bitmo Lab has launched a five-week global alpha testing programme beginning February 9. Sixty participants will receive a prototype unit and early access to the app. According to the company, the programme is designed to assess durability, usability and real-world performance before a wider commercial release. Participants who provide feedback will receive a retail unit upon launch.
Such testing is particularly relevant for flexible electronics. Unlike rigid devices, bendable hardware must withstand repeated flexing, daily handling and environmental exposure. Early user data can help refine manufacturing processes and software optimisation before scaling production.
As with other connected tracking devices, privacy considerations remain part of the equation. Bitmo Lab has stated that data collected during the alpha programme will be used strictly for testing purposes and deleted once the programme concludes.
Whether flexible trackers will redefine the category will depend on how they perform outside controlled testing environments. Still, the introduction of a near-invisible, bendable tracking device reflects a broader shift in consumer technology. As everyday products become thinner and more design-conscious, the tools built to protect them may need to adapt just as seamlessly.