Former AWS specialists launch Qubitz AI, serving to organisations flip AI concepts into production-ready functions as much as 80% quicker and cheaper.
Cloud202 has launched Qubitz AI, a brand new agentic AI platform designed to assist organisations transfer from AI risk to deployment and governance via a single built-in workflow that can save companies as much as 80% in prices, and supply a production-ready AI answer inside weeks, in lots of circumstances.
The launch addresses the 2 greatest challenges dealing with enterprise AI adoption: realizing what to construct, and transferring past experimentation into manufacturing.
Whereas platforms akin to Lovable, Replit and Base44 have made it simpler than ever to construct AI-powered functions, many organisations proceed to battle to rework prototypes into safe, scalable manufacturing techniques.
One organisation already benefiting from the platform is Halved.io, an all the time out there studying help system that brings knowledgeable steerage to each pupil on the precise second they want it.
Founder Andy James had beforehand spent 1000’s creating an software with one other provider, solely to search out the answer failed to fulfill the enterprise’s necessities. Utilizing Qubitz AI, Cloud202 refreshed the platform, incorporating accountable AI controls, safety measures and GDPR compliance, for about 20% of the price.
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The mission was delivered in simply 4 weeks, in contrast with an estimated six months utilizing conventional improvement and compliance processes.
“Halved.io wanted to maneuver shortly to start trials with faculties earlier than the summer time break,” says Naman Gupta, founder and CEO of Cloud202. “Utilizing Qubitz AI, we have been capable of speed up improvement, implement accountable AI safeguards, obtain GDPR compliance and put together the platform for deployment in a fraction of the time and value usually related to these tasks.”
Constructed by former Amazon Internet Companies specialists Fortunate Sharma and Naman Gupta, Qubitz AI makes use of a working backwards methodology impressed by Amazon’s product improvement ideas. Quite than starting with a expertise stack or mannequin choice, the platform begins by figuring out and prioritising the enterprise issues most certainly to ship a return on funding, producing necessities paperwork earlier than creating the multi-agent structure, workflows and deployment mannequin required to resolve them.
The founders developed Qubitz AI after repeatedly seeing organisations make investments closely in AI consulting and proof-of-concept tasks that failed to succeed in manufacturing or make a significant impression.
Throughout his profession at Accenture and AWS, Sharma labored with world manufacturers and witnessed them spending between £350,000 and £500,000 on consultancy earlier than a single software had been deployed.
“An excessive amount of of the AI market stays targeted on experimentation,” mentioned Sharma. “Many organisations are producing prototypes, consuming tokens and constructing MVPs with out ever reaching manufacturing. We needed to create a platform targeted on significant AI options that clear up actual enterprise issues and may be deployed securely at scale.
“And when your subsequent enterprise buyer asks about your AI governance, ‘we use ChatGPT’ is just not a solution. Governance, observability and compliance should be the working layer of an agentic system, not a retrofit.”
As soon as a enterprise downside has been chosen, Qubitz AI routinely generates multi-agent architectures, enterprise instruments, implementation plans and a whole production-ready, full-stack software. A built-in Check Mattress evaluates system outputs in opposition to the organisation’s personal enterprise expectations earlier than launch, so techniques ship after they align with the enterprise, not when the demo seems to be good.
Not like many AI improvement instruments that cease at prototype era, Qubitz AI contains testing, governance and deployment performance designed to help steady enchancment and real-world enterprise operation. As soon as dwell, functions are managed via a complete management layer that covers GDPR compliance, penetration testing, AWS Properly-Architected Framework evaluations, FinOps, observability, and Accountable AI.
There is no such thing as a black field. Each software is constructed with the shopper’s code held in their very own non-public GitHub with the required safety plugins and checks operating contained in the pipeline. Prospects can modify their software at any time utilizing the Qubitz app.
One other organisation utilizing the platform is SphereTrax, a music discovery firm that has labored on tasks akin to Harry Potter and Frozen and with artists together with Michael Bublé and Bruno Mars.
Working with Qubitz AI, SphereTrax undertook a structured AI alternative evaluation that recognized a number of potential use circumstances throughout the enterprise. One concept stood out: Search With Feeling, an AI-powered discovery engine that enables customers to look music and sound results by feelings.
Qubitz AI helped establish, prioritise and architect the idea earlier than creating an AI tagging engine able to analysing 1000’s of soundtracks and categorising them in keeping with emotional traits, style and similarity.
A key differentiator is Qubitz AI’s Carry Your Personal Cloud functionality, which permits organisations to deploy functions instantly into their very own AWS environments in a few hours whereas sustaining full management over information, safety, governance and compliance necessities. Organisations also can begin on Qubitz’s AWS surroundings and migrate later as procurement and compliance wants evolve.
The platform additionally features a rising library of {industry} blueprints designed to speed up improvement and cut back what Sharma calls ‘token consuming.’
Obtainable blueprints embody AI-powered recruitment screening, clever doc processing, buyer help automation, hyper-personalisation and suggestion engines.
Organisations can use these blueprints to scale back improvement effort by combining pre-built architectures, deployment patterns and industry-specific finest practices.
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