Helix 2.0 slashes enterprise AI deployment to eight weeks
Dramatically reducing deployment times
According to HelixML, Helix 2.0 is already in use at several Fortune 500 financial services firms. The platform is designed to reduce deployment times from the typical six to 12 months down to just eight weeks. HelixML also states that its approach provides predictable, fixed licensing and infrastructure costs, which it says can result in cost reductions of up to 75% when compared with public AI platforms.
Among the technical features highlighted are enterprise-grade testing, version control, and rollback capabilities, which the company claims reduce operational risk by 90%. The platform also features integrated Vision RAG (Retrieval-Augmented Generation) technology which, according to HelixML, enhances document processing accuracy by 85%, particularly for financial, regulatory, and technical documents.
Helix 2.0 supports deployment on private infrastructure to enable compliance with regulations such as GDPR and HIPAA, and includes features for agentic AI and enterprise CI/CD. It integrates with DevOps platforms via Git-based workflows, purportedly allowing teams to use existing processes and systems, reducing the learning curve and accelerating initial value realisation.
Key features
The platform offers a Kubernetes-native architecture designed for scalability to 1,000 or more concurrent users and provides OpenAI-compatible APIs to ease migration of existing projects. Additional features include enterprise authentication integration with Okta, Auth0, and Active Directory, as well as support for automated software engineering workflows and rollback capabilities.
On technology integration, the Vision RAG system is built to process complex documents, including financial statements, regulatory filings, and technical diagrams. According to HelixML, this feature delivers an 85% improvement in accuracy using ColPali-powered visual document understanding.
Describing the current enterprise landscape and positioning for the platform, Paul Nashawaty, Principal Analyst at theCUBE Research, stated: "HelixML's launch of Helix 2.0 represents a pivotal shift in enterprise AI adoption, moving from experimentation to secure, production-ready deployment in record time. According to theCUBE Research, 68% of enterprises are piloting or deploying AI agents, but many face 6-12 month ramp-up timelines and spiraling infrastructure costs. Helix 2.0 condenses that timeline to just eight weeks, offering up to 75% cost savings and an 85% improvement in document processing accuracy. For highly regulated sectors like financial services, this level of integration, data sovereignty, and CI/CD compatibility is no longer a competitive advantage; it's a business imperative."
HelixML describes the deployment process as involving secure, private infrastructure provision, pre-configured open-source models, and agent templates for new projects. The orchestration engine within Helix 2.0 is said to dynamically allocate resources and optimise model selection based on workload requirements. Modular YAML configurations define AI agents, which are versioned and managed through Git for traceability and rollback. The system natively integrates with enterprise DevOps pipelines and supports automated testing, in addition to CI/CD workflows and GitOps practices.
Luke Marsden, CEO at HelixML, commented: "As someone who's spent years building and deploying AI in the real world, I know that speed, control, and trust aren't just nice-to-haves, they're mission-critical. With Helix 2.0, we're not just solving today's enterprise AI challenges, we're charting a new course for developers and enterprises, one where organizations can move fast, stay secure, and truly own their AI future."
Pricing model
Helix 2.0 is offered with a pricing model intended to provide transparency and flexibility for enterprise deployments. Hosted platform access is available at USD $75 per user per month, while private deployments operate on fixed infrastructure costs plus a licence fee, with proof-of-concept engagements starting at USD $125,000.
A white label programme is also available for partners, managed service providers, and system integrators wishing to launch their own branded AI solutions. HelixML states that this programme offers multi-tenant management, usage-based billing, and full rebranding capabilities, targeting the USD $2 billion market with 30% potential margins.
Partnership with Civo
To support rapid private AI deployments, HelixML has partnered with Civo, a cloud platform focused on Kubernetes and GPU-powered infrastructure. The partnership enables users to deploy Helix 2.0 on Civo's GPU infrastructure in a single step via the Helix deployment portal, designed to reduce infrastructure complexity for customers.
Mark Boost, CEO of Civo, said: "We're excited to partner with HelixML as their preferred GPU provider. Both our companies share a passion for empowering businesses to take control of their AI deployments, and our synergy is clear. Civo's fast and scalable GPU infrastructure perfectly complements HelixML's innovative private AI platform, enabling enterprises to deploy AI at scale with speed, security, and sovereignty."
Helix 2.0 is available for enterprise and partner evaluation, with options aimed at organisations experiencing issues related to escalating AI costs, security challenges, or compliance demands.
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Helix 2.0 slashes enterprise AI deployment to eight weeks
HelixML has announced the release of Helix 2.0, a private AI platform aimed at enabling enterprises and developers to deploy production-ready AI agents on their own infrastructure within an eight-week timeframe. Dramatically reducing deployment times According to HelixML, Helix 2.0 is already in use at several Fortune 500 financial services firms. The platform is designed to reduce deployment times from the typical six to 12 months down to just eight weeks. HelixML also states that its approach provides predictable, fixed licensing and infrastructure costs, which it says can result in cost reductions of up to 75% when compared with public AI platforms. Among the technical features highlighted are enterprise-grade testing, version control, and rollback capabilities, which the company claims reduce operational risk by 90%. 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