At CES 2026, amid the usual flood of AI claims and futuristic demos, Anisoptera.io stood out by tackling a far more practical enterprise problem: why deploying vision AI is still too slow, too expensive, and too dependent on the cloud.
The company used the show to officially launch Dragonfly, a no-code Physical AI platform that converts live video and sensor data into actionable business intelligence—without relying on expensive cloud servers or specialized data science teams. The message resonated. Dragonfly earned a 2026 CES Picks Award, signaling early validation from industry experts who see edge AI as the next major battleground for enterprise adoption.
In short, Dragonfly promises something many enterprises want but struggle to achieve: real-time AI insights from physical operations, delivered quickly, securely, and at a predictable cost.
Edge AI Without the Usual Tradeoffs
For years, enterprises have faced an uncomfortable choice when deploying vision AI. Cloud-based systems offer powerful analytics but come with latency, privacy exposure, and escalating infrastructure costs. On the other end, advanced edge computing reduces those risks but typically requires niche expertise, custom models, and long implementation cycles.
Dragonfly aims to remove that tradeoff.
The platform processes video and sensor data locally at the edge, where the data is generated, while still delivering enterprise-grade analytics through a centralized, intuitive interface. Crucially, it does this through a no-code model, allowing deployment in two to four weeks, compared with the six to twelve months that traditional AI projects often require.
According to Sam Ares, CEO and Co-Founder of Anisoptera, the shift reflects a broader change in how enterprises think about AI.
“Companies are realizing that expensive cloud server infrastructure isn’t just costly—it’s unnecessary,” Ares said. “Dragonfly brings the intelligence to where the data lives, enabling real-time decisions while keeping sensitive information on-premise.”
That emphasis on where intelligence runs is increasingly important as AI moves out of dashboards and into physical environments—factories, warehouses, stores, and public spaces.
The Real Cost of Traditional AI Deployment
Anisoptera frames the problem in stark terms. The global computer vision AI market is projected to reach $83.6 billion by 2028, yet adoption remains concentrated among tech giants and well-funded early adopters.
For most enterprises, three barriers continue to slow progress:
- Runaway cloud costs: Bandwidth and compute fees can exceed $50,000 per year per deployment location
- Latency limitations: Cloud round trips introduce 200–500 milliseconds of delay, unacceptable for safety-critical or time-sensitive use cases
- Talent scarcity: AI deployment often requires data scientists, ML engineers, and DevOps specialists—roles that command six-figure salaries and are still hard to hire
Dragonfly is designed specifically to bypass those constraints. By running AI models locally and abstracting complexity behind a no-code interface, the platform shifts AI deployment from a specialist-driven IT project to an operational tool.
What Dragonfly Actually Does
At its core, Dragonfly ingests live video and sensor streams and translates them into structured, actionable insights. The platform is positioned for use cases across logistics, retail, sustainability, and media operations—anywhere physical activity generates data that’s currently underutilized.
Instead of training models from scratch or tuning cloud pipelines, users configure AI workflows visually. That makes it possible for operations managers, facility directors, and business analysts—not just data scientists—to deploy and manage AI-driven applications.
This approach reflects a broader industry trend toward democratized AI, where value comes not from model sophistication alone, but from how easily intelligence can be embedded into day-to-day operations.
Early Traction and Measurable ROI
Dragonfly isn’t launching cold. Since its beta phase, the platform has been deployed in more than 50 enterprise installations, with customers reporting an average 3:1 return on investment and payback periods typically under six months.
Anisoptera also reports a 97% customer satisfaction rate and recognition as a Preferred Edge AI Technology Partner at one of the Big Four global consulting firms—signals that the platform is gaining credibility beyond pilot projects.
Those results matter in a market where many AI initiatives stall after proof-of-concept, unable to justify scaling costs or operational complexity.
Why This Matters for Enterprise AI Adoption
The timing of Dragonfly’s launch is notable. Enterprises are under increasing pressure to improve efficiency, reduce operating costs, and meet sustainability commitments—often simultaneously.
Physical operations represent a massive, largely untapped source of insight. But until now, extracting that insight required either heavy cloud investment or deep technical expertise.
“Dragonfly’s launch arrives at a pivotal moment,” said Manuel Navarrete, Chief Growth Officer at Anisoptera. “The complexity and cost of cloud AI deployment have kept these capabilities out of reach for most organizations.”
By removing those barriers, Dragonfly reframes AI deployment as an operational decision, not a research project. That shift could significantly expand the addressable market for computer vision AI beyond early adopters.
A Different Model for AI Infrastructure
Another notable aspect of Dragonfly is its business model. The platform is offered as a subscription that bundles hardware, software, deployment support, and ongoing maintenance. For enterprises, that simplifies budgeting and reduces the risk of hidden infrastructure costs—one of the most common pain points in cloud-heavy AI deployments.
It also reinforces Anisoptera’s core thesis: AI value should come from outcomes, not infrastructure ownership.
The Bigger Picture: Physical AI Goes Mainstream
Dragonfly’s debut highlights a growing realization across the enterprise AI landscape: the next wave of value won’t come from larger models in bigger data centers, but from intelligence embedded directly into physical operations.
Edge AI, once seen as niche, is becoming central to how organizations think about privacy, cost control, and real-time decision-making. Platforms that make that intelligence accessible—without requiring armies of specialists—stand to benefit.
If Anisoptera can maintain its early momentum, Dragonfly may serve as a blueprint for how Physical AI moves from experimental deployments to everyday enterprise infrastructure.
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