Customer journeys no longer start on your website—and Contentsquare is redesigning analytics to keep up.

As AI assistants like ChatGPT become a primary entry point for discovery, the company has rolled out a major platform expansion to help brands track, analyze, and act on journeys that now span humans, LLMs, and AI agents.

The update introduces a unified system that connects signals from websites, mobile apps, AI assistants, and customer conversations—effectively creating a 360-degree view of what Contentsquare calls the “agentic” customer journey.

Analytics for a Post-Website World

For years, digital analytics revolved around websites and apps. That model is breaking.

Today, users increasingly discover products through AI prompts, interact with brands inside chat interfaces, and only later (if at all) visit traditional digital properties.

That fragmentation creates a visibility gap. Brands can see what happens on their sites—but not what happens before or alongside those interactions.

Contentsquare’s latest release aims to close that gap by bringing AI-driven touchpoints into the analytics fold.

Meet Sense Analyst: AI That Tells You What to Do

At the center of the update is Sense Analyst, the company’s configurable AI agent.

Unlike traditional dashboards that surface metrics, Sense Analyst is designed to interpret them—proactively identifying issues, surfacing opportunities, and prioritizing actions based on business impact.

Key capabilities include:

• Personalized insights aligned to KPIs and industry context

• A customizable “Newsroom” where AI agents continuously analyze experience data

• Automated insight delivery via email to reduce dashboard fatigue

This reflects a broader shift across analytics: from reporting what happened to recommending what to do next.

ChatGPT Becomes a Measurable Channel

One of the more notable additions is visibility into interactions happening inside ChatGPT apps.

Brands building experiences within LLM ecosystems can now track:

• How users discover them via prompts

• Engagement within AI-driven interfaces

• Movement between AI assistants and websites

That opens the door to entirely new questions:
Which prompts drive conversions? Are AI-native experiences worth investing in? Do users return via these channels?

For early adopters like Accor, this kind of visibility is critical as they experiment with AI-first customer experiences.

LLM Traffic Is Now a First-Class Signal

It’s not just about discovery—AI is also reshaping how traffic reaches websites.

Contentsquare now provides analytics for LLM- and agent-driven traffic, helping teams distinguish between human and AI interactions and understand how each behaves.

That includes insights into:

• Traffic originating from AI chatbots

• Navigation patterns of AI-referred visitors

• Conversion performance of these new segments

As AI agents increasingly act on behalf of users, this level of visibility could become essential for optimizing content and conversion strategies.

Conversation Intelligence Meets Behavioral Data

The platform is also doubling down on conversation intelligence, integrating insights from support tickets, chats, reviews, and social media.

Powered in part by its Loris acquisition, this layer connects what customers say with what they do—and what it means for revenue.

That unified view helps teams:

• Identify friction points and sentiment trends

• Track movement between conversations and digital interactions

• Prioritize fixes based on business impact

In a landscape where journeys often begin with a question or complaint, this connection between voice and behavior is increasingly valuable.

Analytics, Now Embedded in AI Workflows

In a nod to how teams actually work today, Contentsquare is pushing insights beyond its own platform.

The company is integrating with tools like Microsoft Copilot and other AI assistants using the Model Context Protocol (MCP), allowing users to query experience data directly within their workflows.

Instead of opening dashboards, teams can ask questions like “Where is friction highest this week?” and get immediate answers.

It’s a small UX shift—but one that reflects a larger trend toward ambient, embedded analytics.

Industry Context: The Rise of “Experience Intelligence”

Contentsquare’s move comes as competitors like Adobe, Salesforce, and Google race to unify customer data across channels.

What’s new here is the explicit focus on AI-native touchpoints—something most legacy analytics platforms weren’t built to handle.

As LLMs become intermediaries between brands and customers, understanding those interactions may become as important as tracking website clicks.

Why This Matters

The shift to AI-mediated journeys isn’t theoretical—it’s already happening.

Brands that fail to measure these interactions risk losing visibility into the earliest—and often most influential—stages of the customer journey.

Contentsquare is betting that the next generation of analytics won’t just track users—it will track conversations, agents, and intent across an increasingly complex ecosystem.

Bottom Line

Digital analytics is being redefined in real time.

By bringing AI assistants, conversations, and behavioral data into a single system, Contentsquare is positioning itself for a future where customer journeys are no longer linear—or even fully human.

For marketers and product teams, the message is clear: if you can’t see AI-driven interactions, you can’t optimize them.

Get in touch with our MarTech Experts.

AI is coming for one of the most manual corners of enterprise operations: insurance underwriting.

Convr has introduced a generative AI assistant embedded directly into the underwriting workbench, aiming to streamline how insurers analyze risk, process submissions, and make decisions.

The pitch is straightforward: bring conversational AI—think ChatGPT—into the heart of underwriting workflows, but with domain-specific intelligence built for commercial insurance.

From Static Forms to Interactive Underwriting

Traditional underwriting is document-heavy and time-consuming. Teams sift through submissions, cross-check external data, and manually piece together a risk profile before making decisions.

Convr’s approach turns that process into a conversation.

Underwriters can query a submission in natural language, ask for summaries, uncover hidden risks, and even trigger actions—all within the same interface. The AI assistant doesn’t just surface insights; it helps complete tasks like updating submissions or finalizing reviews.

That shift—from passive review to interactive analysis—could significantly reduce cycle times.

Built on a Domain-Specific AI Engine

What differentiates Convr’s offering is its underlying architecture.

The assistant is powered by the Convr Context Engine, which combines a commercial insurance ontology, knowledge graph, and semantic layer. This allows the system to interpret industry-specific data and relationships more accurately than general-purpose AI models.

The result:

• Context-aware risk analysis

• More reliable summaries and recommendations

• Reduced dependence on large external models

In a regulated industry where accuracy and explainability matter, that domain focus is critical.

AI That Doesn’t Just Answer—It Acts

The assistant goes beyond Q&A.

After analyzing both submission data and relevant external information, it generates key observations and can take next steps—creating tasks, updating data, or moving the submission toward completion.

This “action-oriented” AI mirrors a broader trend toward agentic systems that don’t just assist users but actively participate in workflows.

For underwriting teams, that could mean fewer handoffs, less manual input, and faster turnaround times.

Efficiency Gains Meet Auditability

Another notable feature: every interaction with the AI is recorded within the underwriting file.

That creates a transparent audit trail—something essential in insurance, where decisions must be documented and defensible.

It also allows teams to review and refine how the AI is used over time, improving both performance and compliance.

Industry Context: Insurance’s Slow but Steady AI Shift

The insurance sector has historically lagged in digital transformation, but that’s changing.

Carriers are increasingly adopting AI for claims processing, fraud detection, and risk modeling. Vendors like Guidewire and Duck Creek Technologies are also embedding AI into core systems.

Convr’s focus on underwriting—arguably the most complex and judgment-driven function—signals where the next wave of innovation is headed.

Why This Matters

Underwriting sits at the core of insurance profitability. Faster, more accurate decisions can directly impact loss ratios, customer experience, and operational efficiency.

By embedding AI directly into the workflow, Convr is targeting a key friction point: the time and effort required to move from submission to decision.

If successful, this could help insurers scale operations without proportionally increasing headcount—a major advantage in a competitive market.

Bottom Line

AI in insurance is moving beyond automation into augmentation.

Convr’s generative AI assistant brings conversational, context-aware intelligence into underwriting—turning a traditionally manual process into a more dynamic, interactive system.

For insurers, the question isn’t whether to adopt AI—it’s how quickly they can integrate it into the decisions that matter most.

Get in touch with our MarTech Experts.

AI in marketing isn’t lacking tools—it’s lacking structure.

That’s the bet behind Candid Platform’s new “Live Marketing” environment, an end-to-end AI infrastructure designed to unify strategy, execution, and media operations under one system.

The pitch is ambitious: replace today’s fragmented stack of AI tools with a centralized platform where campaigns, research, and production happen faster—and with measurable business impact.

From Tool Chaos to AI Infrastructure

Most marketing teams today operate in what can only be described as AI sprawl.

They use tools like ChatGPT alongside dozens of niche solutions for content, analytics, media buying, and automation. The result is disconnected workflows, duplicated effort, and limited ROI visibility.

Candid’s Live Marketing platform aims to solve that by acting as a unified backbone—bringing multiple AI models, agents, and workflows into a single environment.

According to the company, the system is built to handle the entire marketing value chain, from strategy and research to execution and production.

The Big Claim: 90% of Marketing Tasks Will Be Automated

Candid makes a bold prediction: AI will handle up to 90% of operational marketing tasks in the near term.

That aligns with broader industry signals—but also highlights a growing gap. While AI adoption is high, measurable results are not. Candid cites research showing that while most organizations use AI, only a small fraction see real financial impact.

The implication: adoption isn’t the problem—execution is.

Inside the Platform: Gateway, Cortex, Studio

Live Marketing is structured around three core components:

• Gateway: Provides simultaneous access to multiple LLMs and proprietary AI tools

• Cortex: An automation layer where AI agents orchestrate workflows across campaigns

• Studio: A production engine for visuals, video, audio, and creative assets

Together, these modules aim to compress timelines dramatically—turning processes that once took months into days.

It’s a familiar promise in AI marketing, but Candid’s differentiation lies in integration: rather than adding another tool, it’s trying to replace the stack.

Built for Enterprise, Not Experimentation

A key selling point is security and compliance.

Unlike standalone AI tools that may require data sharing with third parties, Candid emphasizes an ISO-certified, GDPR-compliant environment designed for enterprise use. That positions the platform for organizations that want to operationalize AI—not just experiment with it.

This is increasingly important as data governance becomes a barrier to AI adoption, particularly in regulated markets.

Agencies as a Distribution Channel

Candid isn’t just selling software—it’s bundling it with services.

With over 300 specialists across agencies like Brand Potential and STROOM, the company can offer Live Marketing as a managed service.

That hybrid model—platform plus expertise—mirrors strategies from larger players in consulting and advertising, where technology alone isn’t enough to drive transformation.

Industry Context: The Race to Build “AI Operating Systems” for Marketing

Candid’s move reflects a broader shift in MarTech.

Companies like Adobe, Salesforce, and HubSpot are all evolving their platforms into AI-powered ecosystems that unify data, workflows, and execution.

What sets Candid apart—at least in positioning—is its focus on infrastructure over applications. Instead of offering AI features within tools, it’s building a system where tools themselves become interchangeable components.

Why This Matters Now

The timing is critical.

According to recent CMO data, AI has rapidly jumped to the top of the priority list, yet most teams still rely on disconnected tools. That mismatch is creating inefficiencies—and limiting ROI.

Platforms that can unify these capabilities while maintaining security and compliance could become the next layer of competitive advantage.

Bottom Line

Marketing doesn’t need more AI tools—it needs systems that make them work together.

Candid’s Live Marketing platform is an attempt to build that system: a centralized, secure environment where AI moves from experimentation to execution.

If it delivers on its promise, it could help marketers finally close the gap between AI adoption and real business results.

Get in touch with our MarTech Experts.

Online fashion has a chronic problem: shoppers don’t trust the fit.

Now CATCHES thinks it can fix that—with physics, not guesswork.

At NVIDIA GTC, the company unveiled “RealFit,” a generative AI-powered virtual try-on system that promises something most fashion tech has struggled to deliver: accurate sizing, realistic fabric behavior, and a true-to-life preview of how clothes will actually look on your body.

Virtual Try-On, But With Physics

Unlike earlier virtual try-on tools that rely heavily on visual approximation, RealFit leans into simulation.

Built on NVIDIA’s CUDA and Omniverse platforms, the system combines generative AI with physics-based modeling to simulate how garments drape, stretch, and move. The result, CATCHES claims, is a “mirror-like” experience where shoppers can see how a piece fits—not just how it looks.

Here’s how it works:

• Users upload a photo and input body measurements

• The system generates a personalized digital twin

• Shoppers can try on garments virtually and toggle between sizes

• Fabric behavior is simulated based on real-world material properties

The first live deployment is already running on the AMIRI website, with more brand rollouts expected in the coming months.

Why Fit Is the Billion-Dollar Problem

Sizing uncertainty isn’t just a UX issue—it’s a revenue killer.

In some fashion categories, return rates exceed 50%, largely driven by poor fit. That creates a cascade of costs: reverse logistics, lost margins, and environmental impact.

RealFit is designed to tackle that head-on by giving shoppers confidence before they click “buy.” If it works as advertised, the upside is straightforward: higher conversion rates and fewer returns.

That’s a compelling pitch in a market where brands are under pressure to improve both profitability and sustainability.

The Tech Stack: GPUs, Digital Twins, and AI Models

Under the hood, RealFit is doing more than standard generative AI.

CATCHES spent two years building a GPU-accelerated simulation framework that models real fabrics—capturing weight, structure, and movement with high precision. The platform combines:

• Physics engines for fabric simulation

• Diffusion models for visual generation

• Vision-language and large language models for interaction

• NVIDIA’s accelerated computing stack for performance

The system runs on high-end infrastructure, including NVIDIA RTX and Blackwell GPUs, enabling millimeter-level fit accuracy and photoreal rendering.

In short: this isn’t just AI-generated imagery—it’s a hybrid of simulation and generation, which could mark a shift in how digital fashion experiences are built.

Industry Context: From Guesswork to “Physical AI”

Most virtual try-on solutions today—from startups to features embedded in platforms like Shopify—focus on visual overlays or size recommendations.

CATCHES is taking a different route: anchoring AI to physical laws.

That approach aligns with a broader trend in AI development, where companies are moving beyond probabilistic outputs toward systems grounded in real-world constraints—especially in areas like robotics, simulation, and design.

If successful, it could push the entire category forward from approximation to accuracy.

Early Backing and Luxury Interest

CATCHES has already raised $10 million from a mix of tech and luxury industry investors, including figures tied to LVMH and former executives from brands like Tommy Hilfiger.

That backing signals growing interest from high-end fashion, where fit, craftsmanship, and customer experience are central to brand value.

Luxury brands, in particular, may see RealFit as a way to replicate the in-store experience online—without sacrificing personalization.

What This Means for Retail and MarTech

RealFit sits at the intersection of retail, AI, and customer experience—squarely in MarTech territory.

For marketers and e-commerce teams, the implications go beyond sizing:

• Better conversion data: Understanding which sizes and styles resonate

• Personalized journeys: Tailoring recommendations based on body profiles

• Reduced churn: Fewer returns mean happier customers

• New engagement channels: Virtual try-on as a discovery experience

It also hints at a future where digital twins become a standard part of online shopping—especially as AI-driven personalization evolves.

Bottom Line

Virtual try-on has been around for years, but accuracy has always been the missing piece.

CATCHES’ RealFit is betting that combining generative AI with physics simulation can finally close that gap—turning a flashy feature into a functional tool.

If it delivers, it won’t just improve online shopping. It could fundamentally change how fashion is sold in the AI era.

Get in touch with our MarTech Experts.

When CEOs talk about AI, the ambition is rarely the problem—execution is. A new joint venture between Teneo and Thoughtworks aims to close that gap, promising to turn boardroom strategy into production-ready AI systems in a matter of months.

Announced today, the partnership blends Teneo’s high-level advisory reach with Thoughtworks’ deep engineering bench—more than 10,000 technologists across design, product engineering, and AI. The pitch is straightforward: help enterprises move from AI ambition to measurable business outcomes at a pace that matches today’s market volatility.

It’s a bold claim in a space crowded with transformation consultancies, but the firms are betting that tighter integration between strategy and execution—rather than treating them as separate phases—will resonate with CEOs under pressure to deliver results.

From AI Vision to Production in 90 Days

The standout hook here is speed. According to the companies, the venture is structured around aggressive timelines:

• Align on new product concepts in three days

• Build a working prototype in three weeks

• Deploy production systems in three months

That’s a sharp contrast to traditional enterprise transformation cycles, which often stretch into multi-year roadmaps with unclear ROI.

Thoughtworks CEO Mike Sutcliff framed the issue bluntly: AI initiatives fail when strategy, culture, and execution move at different speeds. This venture attempts to synchronize all three from day one—pairing executive advisors with engineers and data scientists in unified teams.

In practical terms, that means fewer slide decks and more shipped software.

Why This Matters Now

The timing isn’t accidental. Enterprises are pouring billions into AI infrastructure—often via hyperscalers like Amazon Web Services, Google, Microsoft, and hardware players like NVIDIA—but many are struggling to show tangible returns.

This has created a widening “AI execution gap”:

• Plenty of pilots, few scaled deployments

• Heavy investment, unclear ROI

• Fragmented ownership across business and IT

That’s the gap Teneo and Thoughtworks are targeting. By working directly with CEOs and executive teams, the venture positions itself above typical IT consulting engagements—closer to strategic decision-making, but with the ability to actually build and deploy systems.

It’s also a signal of how the consulting market is evolving. Firms are increasingly moving toward hybrid models that combine advisory, product development, and AI delivery in one offering—something competitors like Accenture and McKinsey have been pushing aggressively.

What the Venture Will Actually Do

Rather than offering generic AI consulting, the joint venture is structured around specific CEO-level priorities. Its services span:

• Scaling enterprise AI programs, including generative AI and advanced analytics

• Modernizing operating models and core systems

• Improving productivity and financial resilience through digital tools

• Enhancing stakeholder engagement with AI-driven insights

• Managing geopolitical and market risk via real-time monitoring

• Transforming customer and employee experiences through modern platforms

Notably, the focus isn’t just on technology—it’s on aligning strategy, operations, and execution simultaneously. That’s a subtle but important shift from traditional consulting models, where strategy often precedes (and disconnects from) implementation.

A CEO-Centric Play

Teneo CEO Paul Keary’s comments highlight another trend: the CEO is increasingly becoming the de facto “AI leader” inside large organizations.

That reflects a broader shift in enterprise tech. AI is no longer confined to IT departments—it’s reshaping business models, risk strategies, and even corporate reputation. As a result, decisions about AI deployment are moving into the C-suite.

By positioning itself as a CEO advisory-led venture, Teneo and Thoughtworks are effectively targeting the highest level of enterprise decision-making—where budgets, priorities, and timelines are set.

Built for Global Scale

The venture will be headquartered in New York, with hubs across the Americas, Europe, the Middle East, and Asia-Pacific. It will also tap into Thoughtworks’ partner ecosystem, including players like Databricks and Mechanical Orchard, to accelerate delivery.

That global footprint matters. AI transformation isn’t just a technical challenge—it’s shaped by regional regulations, geopolitical risks, and market dynamics. The ability to operate across jurisdictions could be a differentiator, especially for multinational clients.

The Bigger Picture

This launch underscores a broader reality: AI transformation is entering a new phase. The hype cycle is giving way to execution pressure, and enterprises are being forced to prove that their investments can deliver real business value.

In that environment, firms that can bridge the gap between strategy and shipping code have an edge.

Whether Teneo and Thoughtworks can deliver on their ambitious timelines remains to be seen. But the premise—AI transformation measured in weeks, not years—is exactly what many enterprises are now demanding.

And if they’re right, the consulting playbook may be due for a rewrite.

Get in touch with our MarTech Experts.

The UK’s well-known Festival of Marketing is going global—and it’s starting with Asia.

Haymarket Media Asia, publisher of Campaign, has announced the launch of Festival of Marketing Asia (FoM Asia), set for September 3, 2026, at PARKROYAL COLLECTION Kuala Lumpur. The move marks the first regional expansion of the franchise since Haymarket acquired it from Centaur Media in 2025.

If the UK edition is anything to go by—drawing over 1,000 marketers annually—FoM Asia is aiming to become a cornerstone event for the region’s marketing leadership. But instead of scaling up, the organizers are deliberately keeping things tight, focused, and senior.

A Smaller, Sharper Marketing Event

Unlike sprawling multi-day conferences, FoM Asia is launching as a single-day event—a design choice that feels less like a constraint and more like a response to executive fatigue.

The target audience: mid- to senior-level marketers, including CMOs, strategy leaders, and MarTech decision-makers. The goal isn’t volume; it’s relevance.

That positioning reflects a broader shift in the events space. As marketing leaders juggle AI transformation, data privacy pressures, and ROI scrutiny, there’s growing demand for high-signal, low-noise gatherings—events that deliver actionable insights rather than keynote overload.

Built Around Asia’s Marketing Reality

What differentiates FoM Asia from simply exporting a UK format is its localized agenda.

An advisory board of senior marketers from brands like Mastercard, McDonald's Malaysia, Unilever, Schneider Electric, and Standard Chartered is shaping the program. That mix spans B2C and B2B, reflecting the increasingly blurred lines between the two disciplines.

It’s a notable move. Many global marketing events struggle with regional nuance; FoM Asia is attempting to bake it in from day one.

What’s on the Agenda

The programming leans into both big-picture strategy and hands-on execution—arguably the most in-demand combination in marketing right now.

Main Stage: “The Big Picture”
Expect macro-level discussions on how AI, data, and shifting consumer behavior are reshaping marketing across Asia.

Focused Tracks:

• Creating Customer Value: Data-driven engagement, personalization, and campaign performance

• Excellence in B2B: Rethinking B2B marketing strategies in a digital-first world

Knowledge Lounge
Smaller, informal sessions focused on practical tools and real-world case studies—less theory, more application.

C-Suite Boardrooms
Closed-door discussions for CMOs and strategy leaders, designed for candid peer exchange rather than polished presentations.

This mix mirrors a broader industry trend: marketers are no longer just storytellers—they’re operators, expected to connect brand, data, and revenue in measurable ways.

Why Kuala Lumpur—and Why Now

Choosing Kuala Lumpur as the launch city is a strategic play. The city has positioned itself as a regional hub with strong connectivity across Southeast Asia, making it accessible for marketers from Singapore, Indonesia, Thailand, and beyond.

Timing is just as important. Asia’s marketing landscape is evolving rapidly, driven by:

• Accelerated digital adoption

• Rising investment in MarTech and AI

• Increasing demand for measurable ROI

• A growing emphasis on first-party data strategies

In short, the region is primed for a platform that brings together brand marketers, tech leaders, and agencies in one room.

A Signal for the Events Industry

FoM Asia’s launch also says something about where marketing events are headed.

Instead of bigger expos and broader agendas, the emphasis is shifting toward:

• Curated audiences over mass attendance

• Actionable insights over inspiration alone

• Peer exchange over passive listening

It’s a model that competes less with trade shows and more with executive forums—where the value lies in who’s in the room as much as what’s on stage.

The Bottom Line

With FoM Asia, Haymarket is betting that Asia’s marketing leaders don’t need another conference—they need a focused, high-impact environment that respects their time and delivers tangible value.

If it works, expect more global event brands to follow suit, trading scale for substance in a region that’s quickly becoming central to the future of marketing.

Get in touch with our MarTech Experts.

Dropshipping has long promised low-barrier ecommerce—but running a profitable store still requires a surprising amount of manual work. Doba thinks AI can fix that.

The company has launched Doba Pilot in beta, positioning it as the industry’s first AI-powered “Dropshipping Agent”—a conversational tool that can build, manage, and scale an online store using natural language prompts. Instead of juggling dashboards, integrations, and spreadsheets, users can simply describe what they want, and the system handles the execution.

It’s a familiar pitch in the age of AI copilots—but applying it end-to-end across ecommerce operations is a notable step forward.

From Idea to Storefront—By Typing a Sentence

At its core, Doba Pilot acts like an ecommerce operator you can talk to.

A user might type: “Build a store, pick trending outdoor products, and list them with a 20% margin.” From there, the platform kicks off a multi-step workflow:

• Store setup (typically on Shopify)

• Product sourcing based on demand and pricing signals

• AI-generated product listings with descriptions and pricing

• Inventory syncing across suppliers in real time

The key difference isn’t any single feature—it’s that the system handles the entire workflow, not just isolated tasks.

That’s where Doba is trying to stand apart. Most dropshipping tools focus on one layer—supplier access, listing automation, or fulfillment. Doba Pilot bundles all of it into a single AI-driven flow.

The Rise of the “AI Agent” in Ecommerce

Doba Pilot lands squarely in one of 2026’s biggest tech trends: AI agents.

Unlike traditional automation tools, agents are designed to interpret intent and execute multi-step processes autonomously. In ecommerce, that means moving beyond “assistive” features (like copy generation) toward systems that can actually run parts of the business.

We’re already seeing similar moves across SaaS:

• AI copilots embedded in CRM and marketing platforms

• Autonomous workflows in customer support and analytics tools

• Generative AI layered into product and merchandising systems

Doba’s bet is that dropshipping—often used by solo founders and small teams—is especially suited for this model. The fewer people involved, the more valuable end-to-end automation becomes.

What Doba Pilot Actually Does

The beta version focuses on four core capabilities:

1. AI Product Discovery
Analyzes demand trends, pricing potential, and supplier data to surface “winning” products.

2. Automated Store Setup
Helps users quickly configure a storefront, with a strong emphasis on Shopify integration.

3. AI-Generated Listings
Creates product descriptions, pricing suggestions, and structured item details optimized for search and conversion.

4. Real-Time Inventory Sync
Keeps product availability aligned across suppliers and storefronts—one of the more error-prone aspects of dropshipping.

All of this is powered by Doba’s existing supplier marketplace, which includes a network of primarily U.S.-based fulfillment partners. That infrastructure is critical: without reliable sourcing and logistics, even the best AI layer falls apart.

Why This Matters for Ecommerce

Doba Pilot is less about adding new features and more about collapsing complexity.

Launching a dropshipping store typically involves:

• Choosing products

• Evaluating suppliers

• Setting up a storefront

• Writing listings

• Managing inventory and pricing

Each step has tools—but stitching them together is where most beginners struggle. By turning that process into a conversational workflow, Doba is effectively lowering the operational barrier.

For experienced sellers, the value shifts from access to speed and scale. Automating repetitive tasks could free up time for higher-impact work like branding, customer acquisition, and retention.

The Catch: Execution Still Matters

As with most AI-driven platforms, the promise is compelling—but the outcome will depend on execution.

Key questions remain:

• How accurate is the product selection logic?

• Can AI-generated listings actually convert?

• How well does the system handle edge cases like supplier delays or pricing volatility?

Dropshipping margins are notoriously thin, so even small inefficiencies can erode profitability. An AI agent that accelerates setup but misses on product-market fit won’t move the needle.

What’s Next

Doba says the beta phase will focus on user feedback, with plans to expand the platform into a more comprehensive AI assistant.

Future updates are expected to include:

• Deeper product intelligence and trend forecasting

• Enhanced automation for day-to-day operations

• Tools for compliance, IP risk detection, and inventory monitoring

In other words, the company is aiming to evolve Doba Pilot from a setup tool into a full lifecycle ecommerce agent.

The Bottom Line

Doba Pilot reflects a broader shift in ecommerce tooling—from dashboards to dialogue.

If the platform delivers on its promise, it could make dropshipping more accessible to newcomers while giving experienced sellers a faster path from idea to execution. But like any AI agent, its real value will depend on how well it performs in the messy, real-world dynamics of online retail.

For now, it’s an ambitious step toward a future where running an ecommerce business might be as simple as having a conversation.

Get in touch with our MarTech Experts.

Enterprise AI has a scaling problem. Plenty of pilots, not enough production—and too many disconnected systems in between. Cisco and NVIDIA want to change that.

The two companies have announced a major expansion of their Secure AI Factory initiative, positioning it as a full-stack framework for deploying AI across core data centers and edge environments—with security baked in from silicon to software.

The goal: help enterprises move from experimentation to real-world deployment in weeks, not months, while avoiding the integration headaches that often stall AI projects.

AI Moves to the Edge—And Infrastructure Has to Follow

One of the biggest shifts driving this update is where AI actually runs.

Inference—where models generate predictions or decisions—is increasingly happening outside centralized data centers, closer to where data is created. Think hospital floors, retail stores, or factory lines where latency matters and decisions can’t wait.

Cisco and NVIDIA are leaning into that reality with expanded edge capabilities:

• Support for NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs across Cisco UCS and edge platforms

• New reference architectures for service providers via the Cisco AI Grid

• Integration with Cisco’s Mobility Services Platform for carrier-grade AI services

The pitch is simple: bring AI to the data, not the other way around.

That’s especially relevant as industries push toward real-time analytics, autonomous systems, and AI-driven operations—all of which depend on low-latency processing at the edge.

Faster Builds, Bigger Pipes

Beyond edge computing, Cisco is also targeting one of the biggest bottlenecks in AI infrastructure: network performance and deployment complexity.

The expansion introduces new high-performance networking hardware, including:

• Cisco N9100 switches delivering up to 102.4 Tbps throughput, powered by NVIDIA Spectrum-6 silicon

• 800G switching support for high-bandwidth AI workloads

• Integration into Cisco Nexus One and Nexus Hyperfabric for simplified deployment

If that sounds like overkill, it’s not. Large-scale AI workloads—especially those involving distributed training or real-time inference—are incredibly network-intensive. Bottlenecks at the network layer can cripple performance.

Cisco’s approach is to treat networking as a first-class component of AI infrastructure, not an afterthought.

Two Paths to Building AI “Factories”

For organizations building large-scale AI environments—what NVIDIA often calls “AI factories”—Cisco is offering two validated deployment models:

1. A reference architecture aligned with NVIDIA’s Cloud Partner program

2. A Cisco-native cloud architecture built on its Silicon One platform

Both aim to reduce the need for custom integration, a common pain point for enterprises stitching together multi-vendor stacks.

This reflects a broader industry trend: pre-validated, modular architectures are becoming the default for AI deployments, replacing bespoke builds that are costly and slow to scale.

Security Moves Front and Center

If there’s one theme running through this announcement, it’s security.

As AI systems become more autonomous—particularly with the rise of AI agents—attack surfaces expand. Models, data pipelines, and even agent-to-agent interactions introduce new risks.

Cisco is embedding security across multiple layers:

Infrastructure Security

• Cisco Hybrid Mesh Firewall enforces policies across networks and workloads

• Extended to NVIDIA BlueField DPUs for server-level threat blocking

• Designed to stop threats before they reach sensitive data

AI Model and Agent Security

• Cisco AI Defense adds vulnerability testing and model protection

• Integration with NVIDIA NeMo Guardrails to manage AI behavior

• New controls for securing agent interactions, especially at the edge

Agent Runtime Protection

• Support for NVIDIA OpenShell runtimes

• Continuous monitoring of agent actions to prevent misuse or unintended behavior

The message is clear: in the “agentic AI” era, security can’t be bolted on later—it has to be embedded from the start.

Why This Matters Now

The timing aligns with a broader shift in enterprise AI.

According to analysts like IDC, companies are moving past the “what can AI do?” phase and into “how do we operationalize it?” That shift brings new challenges:

• Scaling infrastructure efficiently

• Managing distributed workloads

• Securing increasingly autonomous systems

• Avoiding vendor fragmentation

Cisco and NVIDIA are positioning Secure AI Factory as a solution to all four—essentially offering a blueprint for enterprise AI at scale.

The Bigger Competitive Landscape

Cisco isn’t alone in this push. Hyperscalers and infrastructure players—from AWS to Microsoft Azure—are also racing to provide end-to-end AI stacks.

What differentiates Cisco’s approach is its focus on networking, edge infrastructure, and security integration—areas where it already has deep enterprise penetration.

By partnering closely with NVIDIA, the dominant force in AI hardware, Cisco is strengthening its position in a market increasingly defined by full-stack ecosystems rather than standalone products.

The Bottom Line

Cisco and NVIDIA’s expanded Secure AI Factory is less about launching new hardware and more about reducing friction in enterprise AI adoption.

By combining high-performance networking, edge-ready infrastructure, and embedded security, the companies are trying to solve a persistent problem: turning AI from a promising pilot into a scalable, secure, production system.

For enterprises under pressure to show ROI on AI investments, that shift—from experimentation to execution—may be the most important upgrade of all.

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