Archive for September 30, 2026

Ascerta raises $18M to help enterprises maximize AI ROI

Posted in Commentary with tags on September 30, 2026 by itnerd

Enterprise AI has moved from experimentation to a material line item. Companies can count tokens, licenses, agent runs and lines of AI-generated code, yet many still cannot answer the question that determines what happens next: which AI initiatives are actually worth scaling.

Ascerta was built to give them that answer. Today, the company formerly known as Pay-i announced its new name alongside an $18 million Series A led by Dell Technologies Capital, with participation from Hitachi Ventures, BGV, Wipro Ventures and earlier investors. The round brings total funding to $22.9 million and will help Ascerta scale what it calls Enterprise AI Management, giving companies a single view of AI cost, adoption and business value across the organization. 

From managing AI cost to AI value

Ascerta was founded in 2024 by Microsoft veterans David Tepper, Doron Holan and Erik Winters, who saw firsthand how quickly AI economics break at enterprise scale. Tepper spent 19 years at Microsoft and led GenAI strategy for internal use across Azure, while Holan spent 27 years there and architected hyperscale throttling infrastructure handling hundreds of billions of requests a day.

The company emerged from stealth as Pay-i in May 2025 with a $4.9 million seed round focused on AI cost management. As adoption accelerated, the problem widened: enterprises needed to know how AI was being used, what agents and models were doing, and whether that activity was creating enough value to justify more investment. That shift drove the rebrand to Ascerta and an expansion into the full lifecycle of enterprise AI value.

“The market is full of meaningless vanity metrics,” said David Tepper, CEO and Co-Founder of Ascerta. “Companies are counting tokens, lines of generated code, and agent runs, struggling to derive the impact AI has on their business. We built Ascerta to cut through the noise and give organizations the means to win in the AI-era. That means insights specific to their business, people, and use cases. That means purpose-built tools to prevent waste and aggressively optimize for value. Ascerta is a guide through one of the most pivotal eras of transformation in history.”

How Ascerta works 

Ascerta gives leaders one system to see how their organization uses AI, what that AI is doing and whether it pays off. It connects to the AI already running across the enterprise and deploys alongside existing systems. That includes homegrown applications and most common enterprise AI tools such as Microsoft’s Copilot suite, Amazon Bedrock AgentCore, Salesforce Agentforce and major coding agents including GitHub Copilot, Claude Code, and Codex.

From there, Ascerta follows AI from how people use it, through the work it performs, to the outcomes it drives. Its proprietary research ties each use case to the business KPIs it was meant to move, showing which initiatives create value, which need fixing and which should be cut. It tracks adoption by person, team and tool, so organizations can see who is getting real results and help everyone else build AI fluency. Underneath it all, Ascerta measures the true cost of AI with the most granular accuracy on the market. That goes down to tying individual model calls to specific use cases, including sub-token costs, hidden fees and enterprise discounts that other tools miss.

Three products put this to work across the AI estate. Atlas measures AI value, adoption and ROI, from a single workflow to the full portfolio. Forge shows how engineering teams use coding agents and turns that adoption into real productivity. Convoy helps organizations that provision their own capacity get full value from it and add new use cases without disrupting production.

Customer traction 

Today, Ascerta works with customers including Atos, Wipro, and global insurance carriers and alongside partners such as Microsoft, AWS, IBM, Slalom, and Trace3. Across customers, the company says its platform has improved ROI on AI initiatives by 47%, reduced agent launch times by 24% and cut wasted AI spend by 86%.

Customers use Ascerta to put hard dollar values on AI-powered features, recover spend lost to failed agent runs, duplicate projects and Shadow AI. Engineering leaders use it to guide teams toward more effective use of coding agents. Organizations running their own AI capacity use it to consolidate workloads and scale new use cases without disrupting production. 

Why this matters today

Enterprise AI is scaling faster than companies can account for it. It now spans models, copilots, coding agents, internal applications and GPU capacity. Traditional FinOps tools can show what it costs, but not what it’s doing for the business, and that gap widens as agents take on more work. Ascerta closes it by connecting how people use AI and what it costs to the outcomes it drives. That shows leaders what’s working, what needs fixing and where to invest next.

Looking ahead

Ascerta will use the Series A to scale its platform and go-to-market team. It also plans to extend its integrations to every major enterprise AI tool, building on coverage that already includes nearly all of them.

As enterprises run thousands of models, agents and workflows, they need more than a view of what AI costs. Ascerta is turning its research in AI value optimization into new products, moving from measuring what AI is worth to actively improving it.

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Park Place Technologies’ New AI-Powered Platform Provides Full View of IT Infrastructure Health

Posted in Commentary with tags on September 30, 2026 by itnerd

ParkView Asset Intelligence, an AI-powered platform that shows IT managers the health of their infrastructure and insights to act on it, launched today from Park Place Technologies.

Called ParkView Asset IntelligenceTM, the new proprietary platform provides a detailed view into the condition of hardware devices, including servers, storage, network, security appliances and hyperconverged infrastructure, at a level previously only accessible to original equipment manufacturers (OEMs).

Each lifecycle health score, expressed as a 0 to 100 rating, is built from three streams of data – Park Place’s own hardware maintenance records, spanning more than 35 years and a million serviced assets across every major manufacturer; regional spare-parts availability; and customers’ own asset performance and service history.

A score of 66 means something very different once a team can see that comparable devices in the same model family average 92, Adams gave as an example. The result is designed to help experienced IT teams decide whether to refresh, repair or retire a specific piece of hardware.

What ParkView Asset Intelligence Does

The platform combines hardware telemetry, Park Place’s service history and live supply-chain data into a single view of fleet health:

  • Scores every asset, 0 to 100 – Hardware-level data is collected out-of-band from enrolled assets, independent of the operating system, so scoring continues even when the OS is unreachable. Each score is paired with a confidence indicator.
  • Reads failure patterns no single customer can see – Park Place service records across nearly one million of hardware assets reveal how specific models fail, and at what age. No individual enterprise runs a fleet large enough to detect those patterns on its own.
  • Factors in whether parts actually exist – data on spare-part stock by geography feeds directly into the score, so an asset’s supportability in its own region is part of the reading rather than a separate calculation.
  • Surfaces risk before it becomes a failure – Configuration problems, firmware gaps and deteriorating component trends move the score before they generate a support ticket.
  • Benchmarks against the peer group – Every score is set against aggregated data from comparable devices across Park Place’s global customer base, showing whether an asset is performing above or below the expected range for its model.
  • Rolls up to the whole fleet – A portfolio dashboard breaks health scores out by OEM, product family and location, making it possible to spot trends across thousands of assets and concentrate attention where it matters most.

For organizations running hardware past OEM support on a third-party maintenance contract, the scores supply the documented evidence needed to justify that decision internally — or the early warning needed to act before a failure forces the issue.

Availability

The beta version of ParkView Asset Intelligence is available now to all existing Park Place customers and will close on October 31. New customers of Park Place can receive the full version of ParkView Asset Intelligence immediately.

For more information about ParkView Asset Intelligence, visit https://www.parkplacetechnologies.com/parkview-asset-intelligence/.

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VDURA Data Platform V12 Now Generally Available, the Hyperscaler Storage Playbook to AI Factories and Neoclouds on Supermicro

Posted in Commentary with tags on September 30, 2026 by itnerd

VDURA today announced the general availability of the VDURA® Data Platform V12, the release that turns the platform into a multi-tenant, API-driven storage service for GPU clouds and AI factories. V12 ships as a qualified solution on Supermicro Building Block Solutions®, scaling from 8 to 100,000 GPUs on one software stack. VDURA will showcase V12 at Ai Everything Abu Dhabi, 6–7 October at ADNEC Centre, Booth H3-D45.

Neoclouds and AI factories exist to rent and run GPUs, and storage determines how much of that GPU capacity turns into revenue. Every accelerator waiting on a checkpoint, a model load, or a cold read is margin lost. Every tenant that cannot be isolated is a contract that cannot be signed. Every kilowatt spent on storage is a kilowatt not spent on compute. V12 was engineered for that operating model: keep every GPU fed, keep every tenant isolated, keep every cluster online, and do it on hardware operators already standardize on.

What is new in V12

V12 extends the HYDRA architecture, VDURA’s High-performance, Yield-optimized, Distributed, Resilient Architecture, with the capabilities multi-tenant GPU infrastructure requires:

  • Multi-tenant by design. Per-tenant quality of service, namespaces, encryption keys and VLAN isolation on one shared fleet, so a provider can carve a single storage pool into hard-walled tenant services with capacity and performance guarantees.
  • API-first automation. REST APIs, Kubernetes CSI and infrastructure-as-code tenant provisioning, so storage is deployed, provisioned and billed through the same pipelines as the rest of the GPU cloud.
  • Context-Aware Tiering™. Data lands on the right media automatically as access patterns shift between training and inference. Roughly 90% of files stay on flash while roughly 90% of capacity settles on HDD, in one platform with no stub files, no rehydration steps and no manual tuning.
  • Persistent context for inference. A KV cache that outlives the pod. Sessions resume instead of prefilling again, delivering faster first tokens and more concurrent users on the same GPUs, at flash cost rather than recompute cost. 
  • RDMA data paths. Direct GPU-to-storage transfers with the CPU out of the path. The DirectFlow™ parallel client takes roughly 191 MB of DRAM and zero cores from the GPU node.
  • Elastic Metadata Engine. VeLO™ metadata acceleration of up to 20x improvement, 225,000 creates and deletes per second per Director, and billions of metadata operations per second in aggregate.
  • File and S3 in one platform. An S3 object is a file in the volume, not a copy of one. No staging copies between ingest, training, inference and archive.
  • Snapshots and SMR HDD optimization. Instantaneous, space-efficient snapshots for checkpoints and operational recovery, and SMR HDD unlocking 25 to 30% more capacity per rack.
  • End-to-end encryption. AES-256 at rest and in flight, with KMIP key management per tenant.
  • Self-healing resiliency and VDURA Sentinel™. Failure domains as small as a single VPOD™, no manual rebuilds and no downtime windows, backed by VDURA SentinelTM proactive support that opens the service request, with the diagnosis attached, before the customer sees a fault.

Qualified on Supermicro

V12 is qualified on 100% Supermicro Building Block Solutions. The qualified configuration uses the Supermicro AS-1116CS-TN, a 1U system with a single AMD EPYC™ 9005 series processor and 12 NVMe bays, as both the VeLO Director node and the all-flash F-Node, alongside the AS-2015HS-TNR hybrid storage node and the CSE-947HE2C 4U 90-bay JBOD for the mixed-fleet data plane. Every node connects with RDMA straight to the GPU nodes and no dedicated back-end storage fabric.

Built for GPU economics

Clusters grow online from three nodes to thousands, and flash share is a dial rather than a fork. A single platform at roughly 20 PB usable spans a 35x performance range, from a capacity-optimized mixed fleet at 2% flash and 18 kW to an all-flash configuration at 1,000 MB/s per TB, so operators size storage to the workload instead of standing up a second system when the workload changes. The result is 2x+ performance per watt and more than 60% lower total cost of ownership than competitive architectures at the same feed rate, which returns power, rack space and capital to the operator for more GPUs.

Proven at scale

V12 builds on 25 years of parallel file system engineering, from PanFS to VDURA, with more than 1,000 production deployments in over 50 countries and namespaces of more than 1,500 nodes. VDURA Data Platform V12 was named AI Data Management Solution of the Year in the 2026 AI Breakthrough Awards.

Meet VDURA and Thor at Ai Everything Abu Dhabi

VDURA will demonstrate V12 on Supermicro at Ai Everything Abu Dhabi, 6–7 October 2026, ADNEC Centre, Booth H3-D45. VDURA partner Hafþór “Thor” Björnsson (The Mountain from Game of Thrones and Strongman), whose record-setting data lifts have powered VDURA, will be at the booth alongside Team VDURA. Operators building or expanding GPU capacity in the region can book time with the team at AI Everything Abu Dhabi.

Availability

VDURA Data Platform V12 is generally available today for all V5000 class systems and as an upgrade for V11 customers. VDURA SentinelTM is included with V12; proactive service requests and parts dispatch are delivered through VDURACare Premier, VDURA’s 10-year support offering covering hardware, software and 24×7 expert response under a single contract. Learn more at vdura.com/data-platform.

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Binalyze Research: Overloaded Enterprise SOCs “Ignore” 300 Potential Threats a Day Due To Lack of Resources

Posted in Commentary with tags on September 30, 2026 by itnerd

Enterprise Security Operations Centers (SOCs) are succumbing to an overwhelming flood of data, research from Binalyze has found. The leader in automated investigation and response’s report shows SOCs receive on average 2,047 alerts a day. But 300, or 15%, of these are ignored due to a lack of resources, despite being potential threats.  

Beyond alerts, SOCs face more information than ever before. Even AI tools designed to help, for instance by identifying threats, can end up providing more and more data instead of real insight:

  • 91% of CISOs say their organizations suffer from information overload, with 77% missing warnings and vulnerabilities as a result.
  • Overall, 37% (172 of 462) of the vulnerabilities enterprises face go undiscovered, costing an average of $5.1 million a year in damages and rectification.
  • SOCs are suffering: 56% are paranoid they’ll miss an alert signifying a major breach. And despite all the tools at their disposal, 60% don’t have confidence in their decision making.

SOC leaders know a change is needed. 81% say the only way to prevent alert fatigue is hunting down and dealing with threats earlier. But doing so demands SOCs break out of current constraints.

The simple fact is that traditional threat hunting, performed correctly, costs. In time, resources, and skills. Threat hunting and intelligence take 27% of security budgets, but almost two thirds of SOCs still focus almost entirely on alerts and triage – wasting that investment. 

Security budgets are stretched too far. 76% of CISOs want to invest more in threat intelligence but have too many other demands. Only 34% of SOCs have all the resources and skilled people they need to operate effectively. And a higher budget is no guarantee. 42% of CISOs have the resources to hire more skilled analysts but can’t because skills are in such high demand.

To overcome this, SOCs need a way to reduce the data burden. And turn too much information into truthful, contextual insights.

A word on regulation:

Binalyze’s report also investigated SOC leaders’ attitudes to regulation, and whether it helps or hinders operations. It found:

  • It doesn’t protect the right things: 72% say “regulation is more focused on protecting organizational reputations than on protecting people.”
  • It doesn’t guarantee information sharing: 57% of organizations have suffered a breach thanks to a vulnerability another organization had already suffered and reported.
  • It can waste time: 63%: SOCs are under pressure to be always-prepared for audits that wouldn’t help improve or future-proof security, and leaders “waste” 10% of their time, or at least 4 hours a week, proving compliance.
  • It can incentivize poor decisions. 74% of CISOs have had security projects refused because they “weren’t deemed necessary” to meet compliance.

To read the full report visit https://binalyze.ai/2026-state-of-soc-report/

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Sauce Labs Launches the Industry’s First ARM-Native Android Testing Cloud for the Enterprise at Scale

Posted in Commentary with tags on September 30, 2026 by itnerd

Sauce Labs, the test automation leader created by the founders of Selenium and Appium, today announced ARM-native virtual devices for Android in Virtual Device Cloud, the industry’s first enterprise-grade, ARM-native virtual Android testing environment, part of AURA, the company’s AI-Unified Release Assurance platform. The product runs on Google Cloud’s C4A metal instances, bare-metal ARM infrastructure running on Google Axion processors, a next-generation foundation that makes this level of ARM-native performance possible at enterprise scale. The launch marks a new chapter in the strategic collaboration between Sauce Labs and Google Cloud.

The collaboration closes a longstanding performance gap in cloud-based virtual Android testing. While x86-based cloud emulators have served as the standard for mobile web and app functional testing on Android, teams running native ARM64 libraries have had limited options in the cloud, and all teams have had to accept some translation overhead between the test environment and the ARM-based devices users actually hold. That translation process requires every test to be converted at runtime from ARM to x86, resulting in slower session start times and higher error rates than ARM-native environments. For engineering teams where speed, fidelity, and ARM64 compatibility are critical, that gap has been a real constraint.

Google Cloud’s C4A metal removes the root cause. Running virtual Android devices directly on bare-metal ARM hardware eliminates translation overhead entirely. Test results reflect what actually happens on the physical devices your users hold.

ARM-native virtual devices deliver faster test cycles, more reliable results that more closely reflect real-device behaviors, and CI/CD-scale parallelism across Jenkins, GitHub Actions, and modern pipelines. Teams previously blocked by ARM64 library incompatibilities now have a first-class virtual testing option.

Also announced: Sauce Labs is now available on Google Cloud Marketplace with private offer support. Enterprise teams with existing Google Cloud commitments can consolidate procurement, lock in custom terms, and deploy the platform without the friction of traditional software acquisition.

As part of AURA, Sauce Labs’ AI-Unified Release Assurance platform, Virtual Device Cloud extends that same production confidence to native ARM Android testing.

Sauce Labs is trusted by global enterprises including Walmart, Bank of America, and Indeed, with more than 300,000 users and 8.7 billion tests executed on the platform. ARM-native Android testing in Virtual Device Cloud is now generally available. To learn more, visit www.saucelabs.com.

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