Technology Overview

Postman is the world’s leading API platform, enabling organizations to develop, test, manage, and distribute APIs and services. Its continued evolution addresses a new challenge: APIs are now load-bearing infrastructure for AI agents, and enterprises need more than development and testing capabilities. They now require a single place to see all APIs and services, tighter integration with how they already write and ship code, and the ability to seamlessly enforce governance standards.

Postman combines API lifecycle management, institutional context, and autonomous execution in a single AI-native platform. With API Catalog, Agent Mode, and AI Engineer, teams can discover what APIs exist, understand ownership, automate work, and deploy agentic systems with the context required for reliable production.

What Sets Postman API Platform Apart

Postman re-architected its platform around a core belief: AI must be native to the API platform, not bolted onto it. In March 2026, Postman introduced AI-native workflows and API Catalog, a centralized system of record that provides real-time visibility into APIs, performance, ownership, and governance across the enterprise. By grounding AI in authoritative API context, organizations can scale agentic systems safely and reliably.

Building on this foundation, Postman launched Agent Mode, enabling developers to understand APIs, execute changes, diagnose issues, and apply updates directly within existing workflows. In June 2026, Postman expanded these capabilities with AI Engineer, powered by Postman’s Context Graph. By capturing the institutional memory of APIs over time, it enables autonomous execution across development, testing, documentation, and CI/CD workflows.

Technology Overview

Bedrock Data’s ArgusAI governs the enterprise AI risk surface end to end. It maps every AI model, agent and MCP-connected service to the underlying data it can access, including sensitivity classification, entitlement chains, lineage and regulatory context, and packages that mapping into a continuously updated Data Bill of Materials (DBOM) for every AI system in a customer’s environment.

Enterprises are deploying copilots, AI agents and retrieval-augmented generation systems against sensitive data faster than any governance framework was built to handle. Most security teams have no systematic way to see what their agents can access, through which services or with what entitlements. The DBOM gives security, compliance and governance teams a documented, queryable record of what every AI system can access and under what permissions.

What Sets ArgusAI Apart

ArgusAI's core capability is MCP Server Discovery, which maps the path from an AI agent through its MCP connections to the enterprise data it can reach, surfacing exposure paths that identity reviews miss. Agent DLP acts on that map at runtime, inline at the agent gateway, inspecting tool calls in both directions and blocking, redacting or logging sensitive data, with native hooks for AWS AgentCore, LiteLLM and Claude.

Before an agent runs, the DBOM links each AI model, agent and knowledge base to its underlying data with sensitivity classification, entitlement chains and regulatory context, producing the auditable inventory regulators require, across Amazon Bedrock, Azure AI Foundry, Gemini Agent Search, Snowflake Cortex and Databricks. It shows which identities can actually reach sensitive data, including inherited and unused access. All processing occurs inside customer environments, with no data copied outside.

Technology Overview

WEKApod 3 is the third generation of WEKA’s turnkey AI storage and memory appliance, including WEKApod Nitro, Prime, and Prime Max configurations. Purpose-built for AI inference and agentic workloads, WEKApod combines custom-engineered hardware with WEKA's NeuralMesh software to address infrastructure inefficiency in production AI environments, delivering powerful storage with the world's highest capacity and performance density in a single rack.

Organizations running AI today face constraints in power, rack space, GPU utilization, and datacenter capacity that limit scalability. Most AI architectures built for training cannot meet the data requirements of production inference, leading to underutilized compute and operational inefficiencies that make AI deployments difficult and costly to scale. WEKApod delivers exceptional capacity and performance density and reduces infrastructure overhead to unlock breakthrough AI inference economics.

What Sets WEKApod Apart

WEKApod is the world's densest AI storage and memory system and the first system to deliver over an exabyte of effective capacity in a single rack.

A single WEKApod system can deliver:

• 1.1 exabytes of effective capacity in a single rack, with NeuralMesh data reduction running on a hardware foundation of 441.5 PB raw capacity. Throughput reaches 10.2 TB/s per rack. IOPS reach 210 million per rack.
• 267% more capacity density and 114% more performance density than the nearest competitive solution available on the market today.
• 40-80% lower power consumption, cooling, and data center footprint,

Features:

• Hardware custom-designed and engineered by WEKA from the chassis up.
• Multiple patents pending for innovative chassis design, underlying drive-interconnect, thermal-management, and serviceability technologies.
• Built on PCIe Gen 6 Internal Fabric.

Technology Overview

ResolveAI, AI for prod, helps engineering teams run and operate software once code reaches production. ResolveAI connects to a company’s existing observability, code, infrastructure and knowledge systems, then uses its domain-specific multi-agent platform to triage alerts, investigate incidents, explain likely root causes and recommend or prepare next steps, and autonomously run daily operational tasks to keep the product healthy. As AI coding tools make it easier to ship software faster, the work of keeping that software reliable is becoming more complex: more changes, more alerts, more incidents, and more pressure on the teams on call. By building an evidence-based production timeline and coordinating investigations across tools, ResolveAI gives every engineer the context they need to reduce incident time, escalations and operational toil.

What Sets AI for Prod Apart

Purpose-built for production, rather than adapted from a coding assistant or generic chatbot, ResolveAI runs a multi-agent architecture that reasons causally over evidence instead of committing to the first plausible answer. Underneath, post-trained models are orchestrated alongside frontier models to optimize for the best accuracy, latency, and cost, and a knowledge graph gives agents the context, tool fluency, and engineering judgment a solo model lacks. Every investigation feeds a learning loop: explicit engineer feedback and implicit signals flow into a domain-specialized eval framework that absorbs each new model and harness release, so performance is continuously measured and improved. Enterprise-ready, the platform offers tenant isolation, data protection, predictable costs, 60+ integrations, and seamless access through coding agents, Slack/Teams, and the UI.

Technology Overview

Quiq is the agentic AI agent platform for customer experience that gives enterprises the visibility and control they need with Voice AI technology that is empathetic, life-like, and resolves customer questions quickly. AI agents work across every channel - voice, chat, SMS, email, and messaging - while giving businesses complete visibility into every AI decision.

The core problem Quiq solves: enterprise brands struggle to deliver controlled, consistent AI agents at scale. Voice AI agents and chatbots deflect rather than resolve. AI and human agent hand-offs require customers to repeat themselves. Black box AI makes it hard for enterprises to predict and understand AI actions.

Quiq addresses all of this in one platform. AI Agents resolve customer issues. AI Assistants support human agents in real time. Context flows across every channel, while Quiq protects enterprises with guardrails and verification.

What Sets Quiq Agentic AI Platform Apart

Three capabilities set Quiq apart from the competition.

First, Quiq’s Voice AI technology is turning the contact center world upside down. For decades, we’ve moved customers off voice because it was expensive and hard. But now the technology delivers an empathetic, life-like, solution-oriented experience. Listen to real-life calls on our website to see for yourself.

Second, Quiq maintains context across every channel and every handoff, AI to human, voice to messaging, text to email, without losing history or requiring customers to repeat themselves.

Third, Quiq protects brands with Verified Intelligence, verifying every AI response before it reaches customers, providing step-by-step visibility into how every AI decision was made and delivering simulations and testing tools to make sure the AI agent is ready for primetime.

Technology Overview

Gravity is the first agentic foundation built specifically for healthcare enterprise transformation. Unlike point solutions that sit in walled gardens with partial context, Gravity unifies clinical, financial, and operational data across EMRs, payers, and enterprise systems into a single shared context layer, then deploys AI agents that execute.

Built on 14 years of unified data infrastructure and $2B+ in documented outcomes across 125+ enterprise clients, Gravity solves the core reason 74% of healthcare organizations cannot scale AI past pilots: the foundation underneath their agents was never built for coordinated operations.

Customers see a 23% higher first-pass prior authorization approval rate, a 7% increase in revenue capture, $7M in savings from tech consolidation, and agents conceived and deployed in an average of 40 days.

What Sets Gravity™ by Innovaccer Apart

Gravity’s differentiation starts with what sits underneath it: 14 years of unified healthcare data infrastructure built before a single agent was written. That foundation creates four advantages competitors cannot replicate by layering AI onto fragmented data.

• A healthcare-native unified data model with 400+ prebuilt connectors, 100+ FHIR resources, and 6,000+ data quality checks spanning ICD-10, SNOMED, and RxNorm.
• A shared context layer where every agent operates on the same real-time data foundation, enabling cross-workflow learning.
• Cloud-agnostic architecture across AWS, Azure, Snowflake, and Databricks, with HIPAA and HITRUST compliance built in.
• Forward Deployed AI Engineers who take organizations from concept to production in an average of 40 days, with agents running in as little as 48 hours. The result: a platform that grows smarter over time.

Technology Overview

Voxel turns the security cameras industrial facilities already have into a 24/7 safety and operations partner. Using computer vision, the platform watches for the moments that lead to injuries - a forklift passing too close to a worker, an unsafe lift, a blocked emergency exit - and alerts supervisors in real time, before harm occurs. The problem it solves is visibility: a single supervisor can't watch hundreds of thousands of square feet at once, and by the time a safety report surfaces a risk, someone has often already been hurt. Voxel closes that gap. Customers using the platform have seen recordable injuries drop by as much as 91%, proving that the right intelligence, applied early, saves lives without slowing operations down.

What Sets Voxel’s Industrial Intelligence Platform Apart

Most safety technology asks companies to choose between security and safety, or to install new hardware before they see value. Voxel does neither. It connects directly to existing cameras and goes live in days with no new sensors or wiring required. Its computer vision models are purpose-built for industrial environments, trained on billions of frames of real-world warehouse, distribution, and manufacturing footage, so detection is tuned to the hazards of forklifts, conveyor lines, and high-rack storage, not generic security scenes. And unlike point solutions that flag a single risk type, Voxel functions as an end-to-end intelligence layer: it detects the risk, routes the right alert to the right person, and tracks whether the action was actually taken, closing the loop most safety tools leave open.

Technology Overview

Most enterprise AI stalls in pilot mode — delivering dashboards and demos, but not decisions. SymphonyAI solves this with three purpose-built Vertical AI platforms designed to run industry workflows.

CINDE transforms retail by embedding AI across merchandising, shelf management, and supply chain — driving measurable sales, inventory, and profit outcomes from day one.

Symphony Risk Intelligence stops financial crime at scale, using agentic AI to cut false positives, accelerate investigations, and expose hidden risk that rules-based systems miss.

IRIS Foundry powers industrial performance through predictive models, knowledge graphs, and agentic workflows — reducing downtime, improving asset utilization, and accelerating root-cause analysis.

All three run on the Eureka AI Platform — SymphonyAI's unified vertical AI architecture — pre-trained with industry-specific ontologies and workflows to deliver production results in weeks, not quarters.

What Sets SymphonyAI Apart

SymphonyAI's competitive moat is vertical intelligence at the architecture level — not a generic platform stretched across industries, but three purpose-built systems on a common AI foundation.

The underlying platform combines predictive, generative, and agentic AI into a unified vertical architecture, pre-loaded with industry-specific ontologies, knowledge graphs, and domain models — so the AI understands the context of a missed promotion, a false positive alert, or an equipment failure before a single workflow is configured.

Agentic AI is embedded throughout: CINDE agents act on shelf and demand signals autonomously; Symphony Risk Intelligence agents adjudicate alerts and draft SAR narratives with 98-plus percent accuracy; IRIS Foundry agents guide corrective action from root-cause to resolution.

With 30-plus patents, SymphonyAI delivers governed, explainable, and auditable AI — not a black box.

Technology Overview

Doppel is the Frontier AI Platform for Social Engineering Defense. Agentic attacks need an agentic defense. Unlike legacy tools, Doppel unifies Digital Risk Protection, Email Security, and Human Risk Management on one Superintelligence Layer, so a threat caught anywhere is stopped everywhere. Doppel uses AI to detect and connect threats across channels, giving security teams a more complete view of social engineering campaigns.

The platform protects enterprises against social engineering attacks, including phishing, impersonation, deepfakes, and fraud across email, domains, social media, messaging apps, paid ads, and voice channels. The Doppel Threat Graph correlates cross-channel signals to identify attacker infrastructure, prioritize threats, and automate takedowns across domains, social platforms, registrars, telecom providers, and more.

Doppel also strengthens organizational resilience through simulations and threat-informed training that mirror modern adversary tactics, helping organizations reduce analyst workload, accelerate response times, and prevent fraud, reputational damage, and compliance risk.

What Sets Doppel Platform Apart

Doppel pioneered Social Engineering Defense, applying frontier AI to detect and connect threats across the entire social engineering attack chain. Rather than treating phishing, impersonation, fraud, and brand abuse as isolated events, Doppel identifies relationships across channels and infrastructure to surface coordinated campaigns and emerging threats.

Digital Risk Protection detects threats across multiple channels, links alerts into a real-time Threat Graph, and drives AI-powered infrastructure disruption. Email Security uses a self-healing, agentic architecture to investigate inbox threats, close detection gaps, and use the Doppel Threat Graph to disrupt attacker infrastructure. Human Risk Management turns live threat intelligence into phishing simulations and next-generation security awareness training, helping organizations adapt their defenses to the tactics attackers are actually using. Doppel’s AI continuously learns from threat activity and analyst feedback, improving detection and response as attacker tactics evolve.

Technology Overview

ExtraHop RevealX is an agentless network detection and response (NDR) platform that helps enterprises stop sophisticated attacks across hybrid and cloud-native environments. By analyzing real-time network telemetry, the platform exposes active threats hiding in areas where traditional security tools are blind – unmanaged cloud workloads, ephemeral containers, encrypted traffic, and more.

To secure these environments, ExtraHop analyzes network traffic, out-of-band and in real-time, to expose stealthy threats instantly with zero impact on workload performance. By combining line-rate decryption, deep protocol fluency, and cloud-scale AI, the platform delivers continuous threat detection and active defense across the entire attack lifecycle - from intrusion to lateral movement to data exfiltration – and the forensic detail teams need to uncover cyberattacks before they disrupt the business.

What Sets ExtraHop RevealX Apart

ExtraHop is distinguished by its agentless, out-of-band deployment, analyzing live traffic with zero impact on performance. It is the only vendor to unify NDR, NPM, IDS, and packet forensics via a single sensor to deliver real-time visibility across the entire hybrid network. This consolidation ensures deep context, for example, providing packet evidence to validate threats before containers disappear.

ExtraHop combines decryption with deep protocol fluency, detecting threats in encrypted streams that metadata-only (ETA) solutions miss. This granular visibility enables the platform to baseline behavior patterns and alert when container workloads exhibit anomalous activity. Built for enterprise scale, ExtraHop maintains this visibility even as infrastructure and traffic volumes grow.

Operationally, this eliminates friction with networking and DevOps teams, unifying visibility across the entire network.

Technology Overview

Fortanix Confidential AI enables enterprises to securely deploy and run advanced AI models without exposing sensitive information or proprietary model IP, no matter where their data lives. Built on Confidential Computing, it uses hardware-isolated Trusted Execution Environments (TEEs) to keep data, model weights, and inference encrypted at rest, in transit, and in use.

Fortanix Confidential AI addresses the barrier to enterprise AI adoption caused by data exposure and intellectual property theft. It ensures sensitive data and models remain protected from cloud providers, infrastructure operators, and privileged insiders. This removes the tradeoff between security and innovation, making it safe to scale AI in high-risk environments.

What Sets Confidential AI Apart

Fortanix Confidential AI is differentiated by its end-to-end, hardware-rooted security architecture that protects data and models not just at rest or in transit, but while in use. It leverages Trusted Execution Environments (TEEs) with CPU and GPU memory encryption to isolate workloads from infrastructure, including hypervisors and privileged insiders.

Key innovations include composite attestation across CPU and GPU for a unified chain of trust, ensuring verifiable workload integrity, and attestation-gated key release so decryption only occurs inside verified environments.

Operationally, it supports bring-your-own models, multi-cloud and edge deployment, and integrates a FIPS 140-2 Level 3 HSM with post-quantum-ready cryptography (PQC). This combination enables a true zero-trust AI architecture with full lifecycle protection, which most alternatives do not provide.

Technology Overview

Enterprises are deploying AI agents faster than they can secure them. 91% already run AI agents, yet only 10% have an effective governance strategy. Shadow AI is expanding, agents accumulate excessive access, and organizations can’t contain rogue agents. Okta for AI Agents secures the full lifecycle of AI agents by:

• Registering AI agents in a centralized user directory, importing agents from supported platforms, assigning human owners and policies, and detecting shadow agents via the browser and the endpoint.
• Securing agent access with scoped, short-lived tokens across authorization servers, secrets, service accounts, applications, MCP servers, and other agents.
• Enforcing runtime authorization based on relationships, attributes, and policy, not static roles.
• Requiring human-in-the-loop approval for sensitive or high-risk agent actions before they execute.
• Providing governance through automated access reviews, audit logs, and a kill switch that revokes tokens if an agent behaves unexpectedly.

What Sets Okta for AI Agents Apart

• Broad, expanding platform coverage. Okta for AI Agents connects to the tools teams use to build agents, pulling them into a unified registry via pre-built integrations rather than custom connectors.
• Vendor-neutrality. Agents connect to any resource — SaaS apps, APIs, MCP servers, other agents — regardless of which cloud, platform, or framework they run on. While cloud-native tools only secure their own ecosystem, Okta helps secure agents wherever they run.
• Full lifecycle, not a feature add. Most solutions solve one problem. Okta handles discovery, onboarding, protection, and governance across the entire lifecycle.
• Works with the IdP already in place. Organizations can continue using their existing human identity provider (IdP). Okta works alongside major IdPs, keeping existing setups intact while layering agentic security on top.

Technology Overview

Splunk SOAR, a native capability within Splunk Enterprise Security, unifies threat detection, investigation and response in a single AI-powered security operations platform. It automates repetitive security tasks through orchestration, playbooks and embedded response workflows, enabling analysts to investigate, prioritize, and remediate threats faster and with greater consistency. By integrating SIEM, SOAR, threat intelligence, user and entity behavior analytics (UEBA) and AI-driven assistance into one seamless experience, Splunk eliminates fragmented workflows, tool sprawl and analyst fatigue. Organizations use Splunk SOAR to reduce alert volumes, accelerate investigations from hours to minutes, improve SOC efficiency, and strengthen cyber resilience by helping security teams focus on the highest-priority threats instead of manual processes.

What Sets Splunk SOAR, native capability within Splunk Enterprise Security Apart

As a native capability within Splunk Enterprise Security, Splunk SOAR delivers a unified, AI-powered SecOps platform that sets a high, competitive bar. Unlike point solutions, Splunk natively converges SIEM, SOAR, UEBA, threat intelligence, and detection engineering into a single, collaborative interface, which eliminates tool sprawl and analyst fatigue. The platform leverages machine learning to surface risk insights across users, devices, and applications, enabling detection of advanced, multi-stage attacks competitors routinely miss. AI-guided workflows autonomously triage alerts and recommend response actions, dramatically accelerating mean time to detect and respond. With 2,800+ automated actions across 300+ third-party integrations, Splunk's orchestration depth and breadth far outpaces alternatives by transforming reactive SOCs into proactive, resilient security operations.

Technology Overview

VMware Cloud Foundation (VCF) 9.1 is a private cloud platform for running enterprise applications, containers, and AI workloads. It unifies vSphere compute, vSAN storage, NSX networking, Kubernetes, automation, operations, security, and Private AI services into a single operating model, eliminating fragmented toolchains. The primary problem VCF 9.1 solves is making private cloud the most cost-effective and secure foundation for production AI. Enterprises can run inferencing and agentic AI with up to 40% lower server costs through intelligent NVMe memory tiering, up to 39% lower storage TCO through enhanced deduplication and compression, and zero-downtime live patching that secures up to 80% of updates without disrupting workloads. VCF delivers public cloud agility and developer self-service with the data sovereignty, compliance, and cost predictability that production AI demands.

What Sets VMware Cloud Foundation 9.1 Apart

VCF 9.1 is differentiated by its unified private cloud platform spanning vSphere compute, vSAN storage, NSX networking, VKS Kubernetes, VCF Automation, VCF Operations, and Private AI services. The 9.1 release delivers specific, measurable advances: NVMe Memory Tiering reduces server TCO by up to 40% for clusters running mixed AI and non-AI workloads; vSAN global deduplication and compression cuts storage TCO by up to 39%; VKS scales to 500 clusters per Supervisor with 38% lower Kubernetes operational costs; fleet operations now support 5,000 ESX hosts with 4x faster cluster upgrades; and zero-downtime Live Patching secures up to 80% of updates without downtime. Multi-accelerator Private AI support for Intel, AMD, and NVIDIA, plus EVPN/VXLAN interoperability with Arista, complete a platform no point solution matches.

Technology Overview

With the increasing complexity and sophistication of modern networks and infrastructure, pinpointing incidents and root causes has become even more challenging. Legacy monitoring tools, operating in isolation, provide fragmented insights, further adding to the challenge.

Selector is an AI-powered observability platform that provides enterprises with a unified view across network, infrastructure, cloud, and application environments. It ingests data from across the stack, correlates signals in real time, and applies AI to deliver clear, explainable insights, enabling teams to see exactly what went wrong and why within seconds. The result is faster resolution, reduced alert noise, improved operational efficiency, and a true single pane of glass for modern, full-stack environments.

What Sets Selector AIOps Platform Apart

The unified, AI-native architecture ingests and harmonizes data across network, cloud, infrastructure, and application domains without relying on domain-specific tooling. Selector preserves data fidelity while adding context through a programmable layer, enabling precise, cross-domain correlation and explainable AI outputs. The foundation is built on a transformation-first ETL philosophy. By prioritizing the addition of context without diluting the integrity of the original source data, information is refined, ready for high-level analysis from the moment it enters the pipeline.

Its patented real-time intelligence layer combines multiple AI/ML models, including statistical, causal, and language models for accurate root cause analysis and actionable insights. With capabilities such as topology-aware correlation, automated ticketing, and natural-language interaction via LLMs, Selector enables faster decision-making, reduces operational overhead, and replaces fragmented toolsets.

Technology Overview

Harness Cloud & AI Cost Management (CACM) gives engineering and FinOps teams one place to unify, attribute, and govern every dollar of cloud and AI spend. Traditional tools weren't built for either – cloud reports arrive to the CFO monthly, long after engineers could act, and AI spend is worse, invisible below the invoice and disconnected from what it actually produced.

CACM closes both gaps at once: AutoStopping and right-sizing cut idle cloud spend automatically, while per-agent, per-session unit economics tie every AI dollar to the work, team, and outcome behind it. The result: one system for proving cloud efficiency and AI ROI, instead of two separate stories.

What Sets Harness Cost Management Agent Apart

Harness CACM is the only FinOps platform natively integrated with the full software delivery lifecycle (CI/CD, IaC, feature flags), so cost data surfaces where engineers make infrastructure decisions.

Automation depth of the tools is unmatched too. AutoStopping cuts idle compute up to 70%, while Commitment Orchestrator continuously rebalances reserved capacity.

Most distinctly, especially today, Harness captures AI spend at the level of each individual request, tying every dollar to the agent, session, or workflow that triggered it. This is granularity most FinOps tools don't attempt, since they were built for monthly cloud invoices, not per-token AI usage. That lets a finance leader finally answer the question the business is actually asking: is this agent worth what it costs?

Technology Overview

Enterprise technology now spans cloud, on-premises, hybrid, air-gapped, and AI environments, making legacy monitoring tools insufficient to understand and protect today’s interconnected systems. Virtana is the first and only full-stack Agentic Observability platform, delivering unified visibility across applications, services, infrastructure, AI factories, and data pipelines. By continuously collecting and correlating system-wide telemetry, Virtana enables AI agents to observe, reason, predict, and act, accelerating root cause analysis, detecting risks before they become outages, and automating remediation. The result is faster incident resolution, fewer business disruptions, optimized performance and cost, and greater operational resilience. Trusted by Global 2000 enterprises and public sector organizations, Virtana helps assure the availability, performance, and resilience of the high-stakes systems that power modern business.

What Sets Virtana AI Observability Platform Apart

Virtana delivers the deepest and broadest Agentic Observability platform, built on a patented, system-aware architecture that continuously models how enterprise systems operate. Our real-time System Dependency Graph correlates more than 20,000 telemetry signals at sub-second granularity across applications, services, infrastructure, AI factories, data pipelines, networks, storage, and GPUs. Unlike legacy monitoring tools that generate disconnected alerts, Virtana identifies the exact constraints driving performance, cost, and failures, providing AI agents with the context to investigate, reason, and act autonomously. Open interfaces, including MCP, enable any AI agent or LLM to consume this intelligence, making Virtana the operational foundation for autonomous, AI-driven enterprise operations.

Technology Overview

HERE Enterprise Browser was built specifically for the enterprise, designed to tackle critical security & productivity challenges. Built on Google Chromium, HERE streamlines workflows by unifying access to disparate desktop applications & data in a single, intuitive interface – eliminating the productivity-draining “toggle tax” of switching between tabs and systems. From the world’s leading financial institutions, to contact centers, to government agencies, HERE empowers knowledge workers with contextual access to the tools & information they need, all in one place.

As organizations accelerate AI adoption, HERE clears the biggest barrier: full integration into enterprise workflows directly in the browser. Embedded, secure AI surfaces insights in context – directly where users work. No app switching or sacrificing data control. Faster, smarter and more secure digital workspaces boosting efficiency & improving UX across enterprises.

What Sets HERE Enterprise Browser Apart

HERE Enterprise Browser stands apart by delivering a secure, streamlined experience purpose-built for enterprise workflows – far beyond what traditional browsers offer.

• Supertabs: Group browser tabs into unified layouts, for a full view of relevant tools & data at a glance.
• Interoperability between applications: enabling dynamic updates & data sharing without manual copy-pasting.
• SuperSearch: surfaces results from across all enterprise apps in a single, actionable interface.
• Notification Center: simplifies task management by letting users triage & act on messages without switching tabs.
• Centralized admin controls: provides the ability to authorize apps, manage permissions & track usage with analytics that support compliance and vendor oversight.
• AI Center: securely embed proprietary AI models directly into daily workflows, making AI accessible & impactful.
• HERE Studio: Brings together natural-language "vibe coding" to regulated industries for the first time. Non-technical employees in financial services, healthcare, and government agencies now have the ability to build work applications that support their everyday tasks and make them more productive. HERE Studio is ‘compliant by construction’ as it is powered by a firm's own enterprise AI, using its own data and deployed into a governed workspace the organization already controls, rather than to the public cloud.

Collectively, these innovations dramatically reduce the “toggle tax” & unlock enterprise-wide efficiencies.

Technology Overview

VAST Data builds the AI-native data infrastructure platform that enables enterprises to run large-scale AI systems efficiently and continuously. The VAST AI Operating System unifies storage, databases, vector search, streaming data, KV cache, and AI pipelines into a single global platform optimized for agentic computing.

Traditional enterprise AI environments rely on fragmented infrastructure stacks that require constant data movement between disconnected systems, creating bottlenecks, higher latency, operational complexity, and poor GPU utilization. VAST eliminates these inefficiencies by replacing siloed architectures with a unified data layer capable of serving all enterprise and AI data types in real time.

This architecture enables organizations to build and manage millions of agents, maximize GPU efficiency, simplify operations, and support scalable AI and analytics workloads across cloud and on-premises environments.

What Sets VAST AI Operating System Apart

The VAST AI Operating System is built on a unique architecture called Disaggregated Shared-Everything (DASE), purpose-built for AI and large-scale accelerated computing. DASE separates compute from persistent memory, allowing thousands of stateless containers and GPU workloads to simultaneously access a unified global pool of flash storage.

DASE re-engineers the distributed file system concepts pioneered by Google in 2003 for modern AI and agentic computing environments. Unlike traditional multi-tier infrastructure, which creates bottlenecks through fragmented storage and duplicated data pipelines, DASE enables independent scaling of performance and capacity while maintaining a single shared data space.

This “embarrassingly parallel” architecture delivers high performance, resilience, operational simplicity, and cost efficiency simultaneously - eliminating many of the historical tradeoffs that constrained previous generations of enterprise infrastructure.

Technology Overview

OpenSearch is the trusted, vendor-neutral open source platform for search, analytics, observability and vector database workloads. Under the OpenSearch Software Foundation, OpenSearch enables organizations to ingest, analyze, and act on massive volumes of structured and unstructured data through a single, unified platform – powering enterprise search, real-time observability, security analytics, and generative AI applications without vendor lock-in or proprietary licensing costs.

Enterprises scaling AI typically run separate tools for logs, metrics, traces, search, and AI retrieval. This creates real operational and financial overheads – more systems maintained, opportunity to miss context, more vendor contracts to manage. OpenSearch addresses this by combining search, observability, vector retrieval, and agentic AI in one open source platform, under Apache 2.0 licensing, so enterprises aren't locked into a single vendor's roadmap.

What Sets OpenSearch Platform Apart

OpenSearch's primary architectural advantage is unification: enterprise search, observability, vector databases, security analytics, and agentic AI infrastructure within a single open source platform. Users don’t have to stitch separate tools or worry about lock-in. They have access to a formal Long-Term Support program, launched in 2026, which provides defined 18-month support lifecycles, SBOM-backed security compliance, and accredited vendor support – ensuring the kind of stability enterprises typically associate with proprietary vendors without sacrificing the freedom of open source. The platform provides OpenTelemetry-native observability, native Prometheus integration, high-performance hybrid search, and high-speed vector retrieval. Integrated AI functionality includes agent skills for easy, native deployment from popular IDE tools, and agent traces that enable observability for gen AI applications and LLM agents.

Technology Overview

EDB Postgres AI (EDB PG AI) is the sovereign data and AI platform for the agentic enterprise: one Postgres foundation unifying data, governance, and agent runtime—deployed entirely on infrastructure you own.

Enterprise AI stalls because agents lack grounded context and low-latency access to data fragmented across silos and separate vector and analytical stores, stitched together by brittle ETL pipelines. EDB PG AI eliminates that gap by bringing AI to where your data already lives.

EDB PG AI is where Postgres becomes AI-native: models and agents run inside the database, right next to your data. Vector search, inferencing, and native MCP integration connect agents to the tools your developers already use—no round trips, no separate database to manage.

What Sets EDB Postgres® AI Apart

• Built-in vector engine: Store, index, and search data natively within Postgres—up to 3x faster app development, 51% lower TCO than AWS (McKnight Consulting).
• Unified data layer: Postgres, ClickHouse, WarehousePG, and open lakehouse formats from a single Postgres front end, connected with zero ETL. Oracle compatible to modernize with 95% fewer rewrites. Distributed high availability to keep your business always on. Petabyte-scale analytics is built in.
• Governed agent runtime: Proactively enforces agent governance as actions execute at the data layer, where it cannot be bypassed. Zero layers between execution and enforcement.
• Sovereign by default: Deploy in your environment of choice—no external vector database, data movement, cloud dependencies, or added governance surface. Manage, automate, observe, and optimize every cluster you run at enterprise scale.
• Composable multi-agent systems: Every flow callable from MCP server so agents collaborate across teams without custom integration work.

Technology Overview

The SnapLogic Agentic Integration Platform enables enterprises to connect applications, data, APIs, and AI agents across complex hybrid environments through a unified, AI-powered integration and orchestration platform.

As organizations move from AI experimentation to operational deployment, many struggle with fragmented systems, disconnected data, legacy infrastructure, and limited governance over AI-driven workflows. SnapLogic addresses this challenge by serving as the execution layer for enterprise AI and digital transformation, removing the integration tax that stalls projects along the path from pilot to production.

The platform combines integration, automation, API management, data engineering, and AI orchestration in a single solution, enabling organizations to rapidly build, deploy, and govern intelligent workflows at enterprise scale. Supporting both low-code and pro-code development, SnapLogic accelerates time-to-value while reducing operational complexity.

What Sets SnapLogic Agentic Integration Platform Apart

SnapLogic differentiates through its unified approach to integration, orchestration and AI execution. Unlike traditional iPaaS vendors focused on connectivity, SnapLogic combines application integration, data engineering, API management, workflow automation and AI agent orchestration within a single platform.

The platform includes over 1,200 pre-built connectors and templates, AI-assisted development through SnapGPT, low-code and pro-code experiences, and support for modern standards, including MCP. Recent innovations, including AI Gateway and Trusted Agent Identity, enable governed AI execution with policy enforcement, auditability, and identity-aware agent interactions.

SnapLogic’s agentic architecture allows organizations to orchestrate AI agents securely across enterprise systems, maintaining governance and control. It’s metadata-driven design, reusable integration patterns, and hybrid deployment flexibility enable enterprises to scale integrations and AI workflows without vendor lock-in or extensive custom development.

Technology Overview

DDN EXAScaler is a high-performance parallel file system designed to power the most demanding AI and HPC workloads at scale. As organizations invest heavily in GPUs, data infrastructure has become a critical bottleneck, often limiting performance and increasing costs. Built on Lustre, EXAScaler delivers up to 10 TB/s of throughput and multi-petabyte scalability, ensuring data reaches GPUs fast enough to maximize utilization. By eliminating storage constraints across training, inference, and analytics workloads, EXAScaler helps organizations accelerate AI deployment, reduce infrastructure waste, improve operational efficiency, and achieve greater returns on their AI investments.

What Sets EXAScaler Apart

DDN EXAScaler is differentiated by its ability to keep large-scale AI and HPC environments running at maximum efficiency. While many storage platforms focus on capacity, EXAScaler is engineered to solve the industry's biggest challenge: feeding data to GPUs fast enough to prevent costly idle time. Built on a massively parallel Lustre architecture, EXAScaler delivers multi-petabyte scalability, extreme throughput, and parallel access for thousands of GPUs and compute nodes simultaneously.

Unlike conventional storage systems, EXAScaler is optimized for AI training, checkpointing, distributed inference, and large-scale simulation workloads where data movement determines performance. The result is higher GPU utilization, faster time to results, lower infrastructure costs, and the ability to scale AI projects from pilot to production with predictable performance and efficiency.

Technology Overview

Enterprise AI agents often fail due to fragmented data and lack of “AI-ready” context. They struggle to understand relationships within data, leading to inaccurate outputs. Building effective agents requires manual tool design, prompt engineering, and repeated iteration – demanding expertise and specialized infrastructure. Development is slow and difficult to scale.

Neo4j Aura Agent solves this. It’s an agent-creation platform designed to make agentic AI practical, scalable, and trustworthy. It enables organizations to build, test, and deploy AI agents grounded in enterprise data in minutes, not months. Using no- or low-code and auto-generation tools, plus automated orchestration and AIOps for graph-based retrieval, it eliminates the need for deep AI expertise. The result is faster development, lower barriers, and agents that deliver more accurate, context-aware outcomes at scale.

What Sets Neo4j Aura Agent Apart

Neo4j Aura Agent’s graph-native orchestration delivers superior accuracy and transparency:

• Graph-Driven Agent Creation: Auto-generate a ready-to-deploy draft agent in minutes with prompts and tools customized to your graph schema and use case.
• Accurate Agentic GraphRAG: Improve relevance with robust graph retrieval tools: vector search, query templates & text-to-query.
• Rapid Testing & Iteration: Test, refine, and evaluate agent behavior easily in a built-in low-code playground.
• Advanced Reasoning & Explainability: Transparent chain-of-thought and multi-hop graph reasoning exposed through Aura Agent’s reasoning tab.
• Single-Click Deployment: Secure, hosted, MCP & REST endpoints in the cloud.

Organizations use Neo4j to give their AI a structured, explainable foundation – combining graph queries with language models to reduce inconsistent outputs and enable natural language access to complex data. The result: AI that reasons, explains, and scales.