From firmographics to market signals, AI promises deeper account intelligence than traditional data providers ever delivered. But pushing untested enrichment directly into Salesforce risks polluting core systems, breaking reporting, and eroding confidence across sales, marketing, and ops.

Traction Complete’s new product, Complete Discover, is designed to close that gap.

The company has introduced Complete Discover as a way to turn Google Sheets into an experimentation layer for AI-driven account enrichment—a place where teams can test prompts, validate outputs on real accounts, and uncover go-to-market insights without touching production data in Salesforce.

In short, it’s a playground for AI curiosity—with guardrails.

From AI Ambition to Operational Reality

The launch addresses a growing tension inside RevOps teams. Leaders want to explore AI enrichment that goes far beyond static firmographics—think sub-industry detail, market-level context, growth signals, and competitive insights. But operations teams are tasked with keeping Salesforce clean, consistent, and auditable.

According to Traction Complete CEO David Nelson, too many organizations are forced to choose between those two priorities.

“What we’re seeing in the market is a growing disconnect between AI ambition and operational reality,” Nelson said. “Too many teams are forced to choose between innovation and data integrity.”

Complete Discover is positioned as the missing middle layer—where AI enrichment can be explored, pressure-tested, and refined before it ever becomes operational.

Why Google Sheets Is the Right Testing Ground

Choosing Google Sheets isn’t accidental. It’s where revenue teams already explore ideas, test hypotheses, and share early insights before committing them to systems of record.

Complete Discover effectively turns Sheets into an account data lab, allowing teams to:

• Experiment with AI enrichment prompts

• Compare AI-generated insights against known data

• Identify what’s useful, what’s noisy, and what’s wrong

• Iterate quickly without governance risk

This approach mirrors how analytics teams validate models before deployment—but applied to AI-driven GTM data, where mistakes can directly impact pipeline, targeting, and sales execution.

Beyond Basic Firmographics

One of the key themes behind Complete Discover is that enrichment has outgrown traditional data categories.

Basic firmographics—company size, location, industry—are now table stakes. AI makes it possible to surface richer, harder-to-find insights, but only if teams can trust the outputs.

Complete Discover enables revenue teams to explore and validate enrichment such as:

• Hard-to-find firmographics, including private SMB data and companies outside North America

• Validation and supplementation of location, headcount, and industry fields

• Automatic industry normalization across records

• Revenue estimates and year-over-year growth rates derived from company name or domain

• Real-world sales intelligence, including M&A activity, technology usage, and competitor relationships

This shift toward sub-industry and market-level context reflects a broader MarTech trend: precision targeting over volume-based enrichment.

From Experiment to Execution With Complete AI

Crucially, Complete Discover isn’t a dead-end sandbox.

Once teams identify prompts and enrichment logic that consistently deliver value, they can deploy those workflows directly into Salesforce using Complete AI, Traction Complete’s no-code automation layer.

That handoff is where governance comes back into play. Complete AI allows RevOps teams to scale validated insights with:

• Consistent application across accounts

• Clear rules and controls

• No engineering dependency

• Protection of Salesforce as a trusted system of record

The result is a structured pipeline from experimentation to execution—something that’s been largely missing as AI tools flood the RevOps stack.

Why This Matters for RevOps Teams

As AI moves from novelty to necessity, revenue operations teams are increasingly responsible for deciding how AI gets used—not just if it does.

The risk isn’t underusing AI. It’s deploying it too quickly, without validation, and undermining trust in core data systems.

Complete Discover reframes AI enrichment as a RevOps-led discipline, not a vendor-driven black box. It gives teams a way to answer critical questions before scaling:

• Does this enrichment actually improve segmentation or targeting?

• Is the data consistent enough to automate?

• Where does AI outperform traditional providers—and where does it fall short?

Stephen Daniels, VP of GTM & Strategic Operations at Cresta, highlighted the appeal of that nuance.

“The product delivers nuanced, sub-industry insights that go far beyond what typical data platforms provide,” Daniels said. “It puts the information I’ve always wanted right at my fingertips—precise, comprehensive, and effortless to capture.”

The Bigger Picture: AI Needs a Staging Environment

Complete Discover reflects a larger shift happening across MarTech and RevOps: AI needs staging environments, not just production endpoints.

Just as modern data teams rely on dev, test, and prod environments, AI-driven enrichment demands a similar lifecycle. Tools that jump straight into Salesforce risk backlash when data quality slips or insights fail to translate into results.

By positioning Google Sheets as the “AI test kitchen” and Salesforce as the execution layer, Traction Complete is aligning AI enrichment with how operations teams already think about risk, governance, and scale.

As AI continues to expand what’s possible in go-to-market strategy, platforms that respect operational reality—not just innovation hype—may be the ones that actually stick.

Get in touch with our MarTech Experts.

The corrugated and folding carton industry isn’t known for speed. Quotes can take days. Estimating depends on internal handoffs. And sales teams often lose deals before pricing ever lands in a customer’s inbox.

Pakked believes that’s no longer acceptable.

The packaging tech startup has launched Maverick AI, positioning it as the industry’s first AI-powered estimating chatbot built specifically for corrugated and folding carton manufacturers. The product is designed to overhaul front-end sales workflows—cutting quote times from days to minutes while improving accuracy, consistency, and customer experience.

It’s a focused application of AI to a problem that’s plagued packaging manufacturers for decades: estimating friction.

Built by Insiders Who Know the Pain

Pakked’s credibility starts with its origin story.

Founded in 2023 by brothers Philip and Wesley Webb, the company is led by third-generation packaging operators who grew up inside box plants. Their family previously owned and operated Fleetwood-Fibre Packaging & Graphics in Southern California, giving the founders firsthand exposure to the inefficiencies that define traditional estimating processes.

Those experiences shaped Maverick’s development over the past 18+ months. Rather than retrofitting generic sales software, Pakked built a tool tailored to how packaging estimates actually work—materials, colors, quantities, CAD files, and constant revisions.

“The corrugated industry is ready for a change,” said Philip Webb, co-founder of Pakked. “Pakked is modernizing the front-end sales process for manufacturers while improving the customer experience through faster response times and technology built for today’s expectations—not yesterday’s systems.”

Maverick AI: A Digital Estimating Teammate

Maverick functions less like a form and more like a conversational coworker.

Sales and estimating teams can interact with the chatbot in natural language—running internal estimates, adjusting quantities, changing colors, creating multiple pricing scenarios, and refining details in real time. Instead of restarting the estimating process with every revision, teams iterate instantly.

Once finalized, Maverick generates fully branded quotes that can be downloaded or shared with customers within minutes. The experience mirrors how sales teams already think and communicate, but without the delays imposed by legacy systems.

This conversational approach reflects a broader MarTech trend: AI agents replacing rigid workflows with adaptive, context-aware interactions—especially in industries that have historically relied on manual processes.

Fixing the Quote-to-Hit Rate Problem

Speed is only part of the story.

In corrugated packaging, quote-to-hit rates typically fall below 20%, a figure that underscores how many opportunities die due to slow responses and internal bottlenecks. By the time a quote arrives, customers have often moved on.

Maverick directly targets that failure point. Unlike traditional estimating systems that depend on departmental availability and sequential handoffs, Maverick works 24/7, generating estimates instantly without waiting for internal responses.

By eliminating operational noise and hidden costs tied to manual workflows, Pakked argues Maverick can help manufacturers respond faster, reduce friction between teams, and capture opportunities that would otherwise be lost.

Trained on Each Plant’s Own Data

One of Maverick’s most important design choices is customization.

Each deployment is trained on a manufacturer’s own internal data and configured specifically for that box plant. That approach avoids the “one-size-fits-all” problem common in horizontal AI tools and helps ensure accuracy in highly variable production environments.

In testing, Pakked reports Maverick achieved up to 99.3% accuracy when benchmarked against existing legacy estimating systems currently in use across the industry.

For manufacturers wary of AI-driven pricing errors, that figure matters. Accuracy isn’t just a technical metric—it’s a trust requirement.

More Than Estimating: A Workflow Hub

Maverick AI also serves as the central hub of the broader Pakked platform.

The system brings together estimate requests, CAD files, artwork, and approvals into a single workflow, reducing the need to juggle email threads, shared drives, and disconnected tools.

Pakked is working toward enabling customers to place orders directly from approved quotes—a move that would extend automation beyond estimating and into the full order lifecycle.

If executed well, that could shift packaging sales from a fragmented, back-and-forth process to a more continuous, digital experience.

Why This Matters Now

Packaging manufacturers face increasing pressure from faster-moving competitors, rising customer expectations, and tighter margins. In that environment, front-end efficiency isn’t just an operational issue—it’s a growth lever.

Maverick AI reflects a growing pattern across industrial and B2B sectors: AI isn’t being used to replace craftsmanship, but to remove friction around it. By modernizing estimating—a historically slow and opaque process—Pakked is betting that speed, clarity, and responsiveness will become competitive advantages in a traditionally conservative industry.

For corrugated and folding carton manufacturers, that could mark the beginning of a long-overdue shift from manual bottlenecks to AI-assisted sales execution. 

Get in touch with our MarTech Experts.

For years, B2B marketers and sales leaders have complained about the same thing: too many leads, not enough buyers. Bebop, an AI-native prospecting platform, thinks the problem isn’t volume—it’s definition. This week, the company unveiled Ready-to-Sell Leads, a new product designed to reset expectations around what a “qualified lead” should actually look like in modern B2B go-to-market teams.

Rather than feeding sales teams long lists of contacts that still need hours of research, Bebop says its new capability delivers opportunities that are already in-market, verified for intent, and paired with clear guidance on how to close. It’s a bold promise in a space crowded with intent data vendors, enrichment tools, and AI-powered sales assistants—all claiming to make pipeline creation easier.

From “Marketing Qualified” to “Ready to Close”

At the core of Ready-to-Sell Leads is Bebop’s proprietary prospecting engine, which combines large language models, specialized data sources, and custom algorithms to identify accounts that aren’t just researching—but actively buying. According to the company, every opportunity is quadruple-vetted for accuracy and intent before it reaches a customer’s CRM or inbox.

What makes the product stand out isn’t just the filtering process. Each lead arrives with a customized sales playbook that outlines recommended messaging angles, likely objections, and contextual cues sales reps can use immediately. In other words, Bebop isn’t just handing over a name and a title—it’s packaging the rationale for why that buyer should care, right now.

“Sales teams tell us they spend too much time qualifying and researching leads instead of selling,” said Bebop CEO Gianpiero Policicchio. “Ready-to-Sell Leads raises the bar for what sellers should expect from lead generation and removes the guesswork when it comes to closing.”

That framing taps into a growing frustration across B2B organizations. As buying cycles get longer and buying groups get larger, traditional MQL models are breaking down. Sales teams want fewer leads—but ones that already reflect buying intent and internal alignment.

AI as the Filter, Not the Flood

The timing of Bebop’s launch is notable. The B2B tech market has been flooded with AI tools promising to automate prospecting, write emails, and summarize accounts. Many of them optimize for speed and scale. Bebop is positioning itself differently—using AI to narrow the funnel rather than widen it.

By blending LLM-driven analysis with specialized B2B data sources, the platform aims to identify signals that suggest real purchase readiness, not just content consumption or keyword interest. That distinction matters as intent data becomes more commoditized and easier to game.

In practice, this approach could appeal most to growth-focused teams under pressure to do more with smaller sales orgs. If the leads are truly as qualified as Bebop claims, sales productivity gains could be significant—especially for mid-market and enterprise sellers juggling complex deal cycles.

How Ready-to-Sell Fits Into Bebop’s Broader Platform

Ready-to-Sell Leads isn’t a standalone experiment. It joins Bebop’s expanding suite of AI-driven sales tools designed to support the entire revenue motion.

The Bebop App provides instant prospect research and generates personalized playbooks on demand. Ready-to-Sell Leads pushes fully vetted opportunities directly into CRM systems or email, minimizing friction between marketing and sales. Meanwhile, Bebop Insights gives teams large-scale access to real-time B2B data for market analysis and strategic planning.

Together, the portfolio reflects a shift away from point solutions and toward an integrated, AI-first sales intelligence stack. Instead of bolting AI onto legacy workflows, Bebop is building around the assumption that sellers should start conversations already informed—and already relevant.

Implications for B2B Marketing and Sales Teams

If Bebop’s model gains traction, it could put pressure on both marketers and sales leaders to rethink how success is measured. Volume-based KPIs—like lead count or cost per lead—become less meaningful when the focus shifts to close-ready opportunities.

It also raises questions for competing platforms. Many vendors promise “high-intent” leads, but few operationalize intent with the level of prescriptive guidance Bebop is emphasizing. The inclusion of playbooks alongside leads blurs the line between data provider and sales enablement platform.

Of course, the real test will be performance. In a skeptical market, Bebop will need to prove that its quadruple-vetted opportunities consistently convert at higher rates—and justify whatever premium pricing comes with that promise.

Still, the launch signals a broader trend in MarTech and SalesTech: AI is moving from automation to judgment. And in a world where attention is scarce and sales cycles are unforgiving, that may be exactly what modern revenue teams are willing to pay for.

Get in touch with our MarTech Experts.

For years, digital transformation in food and beverage has meant adding tools, not removing complexity. One vendor for mobile ordering, another for payments, a third for POS, and yet another for loyalty—each solving a narrow problem while quietly creating operational sprawl. Fushi Tech believes that era is ending.

This week, the global AI and digital solutions provider announced a strategic partnership with Commonwealth Concepts, one of Singapore’s best-known food and lifestyle groups, to consolidate its entire digital stack onto a single, integrated platform. Under the agreement, Fushi Tech’s Fynix suite will replace multiple vendor systems with one unified environment covering mobile ordering, payments, point-of-sale, and customer relationship management.

For Commonwealth Concepts—home to 16 established brands including PastaMania, The Marmalade Pantry, and Bedrock Bar & Grill—the move represents a full-scale rethink of how technology should support modern F&B operations.

Why Fragmentation Has Become a Liability

Most F&B operators today juggle between three and five core technology vendors just to keep the business running. Mobile apps don’t talk cleanly to POS systems. Payment data sits in a different silo from customer profiles. Loyalty insights arrive late—or not at all.

Those gaps don’t just create IT headaches; they limit how brands understand customers and respond in real time. As consumer expectations rise around personalization, seamless checkout, and consistent experiences across channels, fragmented stacks have become a competitive disadvantage.

Fushi Tech’s pitch to Commonwealth Concepts was straightforward: replace that patchwork with a platform designed from day one to work as a single system.

Inside the Fynix One-Stop Platform

At the center of the partnership is Fynix, Fushi Tech’s branded F&B solution suite. Rather than integrating loosely connected third-party tools, Fynix bundles four core systems into one coordinated platform:

• FYNIX Mobile App: Android and iOS ordering paired with a built-in digital wallet

• MAKQR POS: Front-end point-of-sale management designed to sync natively with ordering and payments

• Yeahpay: Integrated payment processing and settlement

• Ascentis CRM: An AI-powered customer data and loyalty platform

Because each component is built to operate together, data flows continuously across ordering, payment, and customer engagement touchpoints. That enables real-time visibility into sales, behavior, and campaign performance—without the manual reconciliation that plagues multi-vendor setups.

“The one-stop advantage isn’t just convenience,” said Johnson Tan, Vice President of Fushi Tech. “When all your systems work together from the start, you can understand your customers better, see your full business picture in real time, and create seamless experiences whether customers are using your app or ordering in-store.”

A Full Digital Upgrade for Commonwealth Concepts

For Commonwealth Concepts, the partnership goes beyond backend efficiency. The rollout includes a comprehensive upgrade of its customer engagement strategy, anchored by a redesigned TriplePlus rewards program.

Key initiatives under the partnership include:

• Rebuilding the rewards program for greater scalability

• Expanding across customer, corporate, and staff membership tiers

• Personalizing offers and promotions using customer preference data

• Managing campaigns with exclusive discounts, rebates, and special gifts

The goal is to turn loyalty from a static points system into a dynamic, data-driven engagement engine—one that adapts in real time as customers move between mobile, in-store, and promotional touchpoints.

Johnson Tan framed the collaboration as a mindset shift rather than a technology refresh. “Digital transformation isn’t about buying more technology,” he said. “It’s about making everything work together. You shouldn’t have to choose between powerful features and seamless integration—you can have both.”

What This Signals for the F&B Tech Market

The Fushi Tech–Commonwealth Concepts deal reflects a broader evolution in F&B technology strategy. Early digital adopters focused on bolt-on capabilities: first online ordering, then payments, then loyalty. That approach delivered quick wins but left operators managing increasingly complex ecosystems.

Now, leading brands are prioritizing integration over accumulation. They want fewer platforms, deeper insights, and systems that scale without multiplying vendors.

In that sense, Fushi Tech’s approach mirrors a wider MarTech and retail trend: platforms are being judged less on individual features and more on how well they unify data, workflows, and customer experiences. AI plays a role—but mainly as an enabler of intelligence across the stack, not a standalone add-on.

A Bet on Cohesion as Competitive Advantage

For Fushi Tech, the partnership strengthens its position as an end-to-end platform provider rather than another point solution in an already crowded market. For Commonwealth Concepts, it’s a bet that operational cohesion and customer intelligence will matter more than incremental feature upgrades.

As F&B operators across Asia face rising costs, tighter margins, and more demanding customers, that bet may prove well-timed. The future of restaurant technology, increasingly, looks less like a toolbox—and more like a single system designed to work as one.

Get in touch with our MarTech Experts.

WhatsApp is no longer just where customers ask questions—it’s where discovery, engagement, and transactions increasingly converge. That was the clear message from Omnichat’s “Social CRM and AI” conference, a large-scale industry event that brought together leaders from Meta, Maxim’s Group Hong Kong, and MEDILASE to unpack how conversational commerce is reshaping customer experience across Asia.

At the center of the discussion was Omnichat’s latest product announcement: Omni AI Agent Studio, a new platform designed to make agentic AI a practical, deployable reality for businesses using WhatsApp as a core customer channel. The launch reflects a broader shift in MarTech and CX platforms—away from fragmented engagement tools and toward unified, data-driven social CRM systems built directly into messaging environments.

From Messaging App to Social CRM Engine

Omnichat, a Meta WhatsApp Business Solution Provider, used the event to position WhatsApp as the connective tissue of modern customer journeys. What once functioned primarily as a support inbox has evolved into a channel where brands can acquire, convert, and retain customers in a single conversational flow.

That evolution is being driven by both consumer behavior and platform capability. According to Kantar research cited at the event, 71% of Hong Kong consumers message a business on WhatsApp at least once a week—a signal that messaging is no longer peripheral to the buying journey.

“Customers no longer just expect WhatsApp to be a customer service channel,” said Silvie Lam, Business Director, Greater China at Meta. “They now expect to reach a brand immediately after discovering it through social ads.”

Meta’s roadmap reflects that expectation. Tools like Ads that Click to WhatsApp, Website-to-WhatsApp ads, WhatsApp Catalog, and WhatsApp Flows are designed to help businesses manage the full funnel—from discovery to transaction—without forcing customers to jump between platforms. In effect, WhatsApp is becoming a commerce-enabled front door rather than a post-sale support line.

Omni AI Agent Studio: Making Agentic AI Practical

Omnichat’s response to this shift is Omni AI Agent Studio, a new environment that allows businesses to deploy and orchestrate multiple AI agents across conversational workflows. The studio builds on Omnichat’s existing native AI agents, including:

• AI Customer Service Agent

• AI Marketing Campaign Agent

• AI Shopping Agent

What’s new is the ability to integrate these agents seamlessly into any customer interaction—without requiring businesses to rebuild workflows from scratch.

Founder and CEO Alan Chan described the launch as a strategic inflection point. “This signifies a pivotal moment in our commitment to democratising Agentic AI for businesses,” he said. “When combined with our advanced WhatsApp functionalities—our Social Data Customer Platform and WhatsApp loyalty program—we’re enabling brands to build a truly powerful social CRM.”

In practice, that means consolidating customer profiles, analyzing conversational data across CRM and social channels, and delivering experiences aligned with real customer behavior throughout the lifecycle—not just at isolated touchpoints.

Maxim’s Group: Turning WhatsApp Into a Revenue Channel

One of the most compelling case studies came from Maxim’s Group, one of Hong Kong’s largest F&B operators. Rather than treating WhatsApp as a support add-on, Maxim’s has positioned it as a high-performing e-commerce channel that complements its Eatizen membership app.

Using Omnichat’s platform, enhanced with Omni AI, Maxim’s engages both existing Eatizen members and new customers through WhatsApp. AI-powered chatbots streamline day-to-day operations, while segmented broadcast campaigns drive targeted promotions based on customer behavior.

The results, according to Eileen Tang, Head of Digital Business at Maxim’s Caterers Limited, have been striking. During a recent Mid-Autumn Festival campaign, Maxim’s built a fully end-to-end WhatsApp buying journey—from discovery to payment—inside the messaging app.

“The outcome was immediate,” Tang said. “We achieved a 2–3x higher Average Transaction Value compared to our traditional online marketplace channels.”

She attributed the uplift to two key factors: a frictionless customer experience that allowed users to order and pay directly within WhatsApp, and significantly lower costs compared to building new features inside a proprietary app. Maxim’s is now scaling WhatsApp commerce as a core growth pillar rather than a seasonal experiment.

MEDILASE: Automating Care Without Losing the Human Touch

While Maxim’s showcased commerce scale, MEDILASE demonstrated how conversational platforms can improve service quality and operational efficiency in high-consideration industries like aesthetic medicine.

By integrating Omnichat’s WhatsApp API solutions, MEDILASE automated key touchpoints across the customer journey—from initial inquiries to appointment confirmations and treatment reminders. The centralized platform unified marketing, customer service, and sales workflows, improving collaboration across teams.

“By leveraging the WhatsApp API solution, we’ve deployed 24/7 chatbots and achieved seamless cross-team collaboration,” said KC Ng, CEO of MEDILASE. “This has significantly elevated our service standards and operational transparency.”

Beyond automation, MEDILASE is using conversational data to anticipate customer needs and improve consultation quality. In one recent campaign, roughly 40% of customers participated in an interactive WhatsApp game, resulting in a 5% conversion rate—a notable outcome in a category where trust and personalization are critical.

Why This Matters for MarTech and CX Leaders

Taken together, the announcements and case studies at Omnichat’s event highlight a clear industry inflection point. Social messaging is no longer an edge channel—it’s becoming the backbone of customer engagement, especially in mobile-first markets across Asia.

What’s changing isn’t just where conversations happen, but how intelligently they’re managed. Platforms like Omnichat are moving beyond routing messages to using AI agents, conversational data, and unified customer profiles to guide decisions in real time.

For MarTech leaders, the implication is clear: CRM strategies that ignore messaging platforms risk becoming blind to where customers actually engage. For CX and commerce teams, WhatsApp is emerging as a place where convenience, personalization, and conversion can coexist—without forcing customers through fragmented digital experiences.

Conversation and Commerce, Fully Aligned

The collaboration between Meta, Omnichat, Maxim’s Group, and MEDILASE sends a unified signal to the market. The convergence of conversation and commerce is no longer a future-state vision—it’s already delivering measurable business outcomes today.

As brands look for sustainable competitive advantage in crowded digital markets, the winners may be those that stop treating messaging as a support tool and start building it into the core of their customer strategy. Omnichat’s bet on agentic AI and social CRM suggests that the next phase of customer experience will be conversational by default—and intelligently orchestrated behind the scenes.

Get in touch with our MarTech Experts.

The race to monetize AI is entering a more structural phase. While much of the industry’s attention has been fixed on OpenAI’s experiments with consumer ad testing and Google’s recent rollout of the Universal Commerce Protocol (UCP), a deeper shift is underway—one that redefines how commerce itself is discovered, evaluated, and executed.

That shift took a concrete step forward today as Deeplumen, an AI infrastructure company focused on next-generation commerce, announced the release of its UCP SDK for Java. The open-source launch follows Google’s introduction of UCP as an open standard meant to give AI agents a shared “commercial language” for discovering products and completing transactions directly within AI-powered experiences.

For enterprises running large-scale commerce systems, Deeplumen’s move addresses a practical bottleneck: how to participate in agentic commerce without rebuilding existing technology stacks from scratch.

UCP and the Rise of Agentic Commerce

Google’s Universal Commerce Protocol is designed to support native discovery and direct checkout across its AI surfaces, enabling brands to capture intent at the moment it emerges. Rather than routing users through traditional funnels, UCP allows AI agents to understand product data, validate availability, and execute transactions programmatically.

That model reflects a growing reality in commerce: decision-making is increasingly delegated to AI agents. Shopping workflows are no longer limited to humans scrolling, comparing, and clicking. Instead, autonomous agents evaluate options based on structured inputs—price, availability, fulfillment terms, and reliability—often faster and more objectively than human buyers.

Deeplumen’s Java SDK brings that future into reach for enterprise brands. By implementing UCP in Java, the company is enabling organizations with deeply embedded commerce infrastructure to connect directly to agent-driven ecosystems without abandoning proven systems.

From Persuading Humans to Informing Machines

The implications extend beyond technology integration. The shift to agentic commerce challenges decades of marketing orthodoxy.

“Traditional marketing is about optimizing for perception,” said Joy Wu, COO of Deeplumen. “AI agents optimize for parameters.”

In what Deeplumen describes as the M2AI (Marketing to AI) era, emotional storytelling and brand symbolism matter less to the point of transaction. What matters instead is high-fidelity, structured, and verifiable data—information that AI agents can trust, compare, and act upon with minimal ambiguity.

Rather than “selling” in the conventional sense, brands must now inform AI systems with precise product definitions, consistent availability signals, and reliable transaction logic. In this model, clarity becomes a competitive advantage.

Why Java Matters to the Enterprise

While much of modern AI development is Python-centric, global commerce infrastructure tells a different story. Core systems powering ERP platforms, large retailers, and order management engines are overwhelmingly built on Java.

Deeplumen’s decision to prioritize Java is a direct response to that reality. The UCP SDK for Java is designed to slot into existing enterprise environments, reducing friction at a time when many organizations are already stretched thin by digital transformation demands.

Key capabilities of the SDK include:

• Structured Identity: Tools that help brands define products and offers in ways AI agents can accurately interpret

• Seamless Integration: A plug-and-play library for Java environments, minimizing architectural disruption

• Transaction Readiness: Support for full-loop commerce, moving beyond discovery into fulfillment within AI interfaces

The result is a practical on-ramp to agentic commerce for enterprises that can’t afford wholesale rewrites of mission-critical systems.

Competing in the M2AI Era

Deeplumen positions the Java SDK as part of a broader infrastructure strategy aimed at helping brands compete on clarity, availability, and reliability rather than pure brand awareness. As AI agents become more influential buyers, these attributes increasingly determine which products surface—and which are ignored.

In this environment, the quality of a brand’s structured commerce data may matter more than its creative campaigns. Products that are easy for AI agents to discover, verify, and transact against will have a structural advantage, regardless of traditional marketing spend.

The company sees UCP for Java as an early building block in a longer-term roadmap focused on decentralized protocols and AI-to-AI commerce infrastructure. The goal is to enable transactions that happen autonomously between systems, with minimal human intervention, across interoperable standards.

A Signal of Where Commerce Is Headed

Deeplumen’s announcement lands at a moment when the industry is actively redefining what “commerce readiness” means. As Google, OpenAI, and others experiment with AI-native buying experiences, standards like UCP are emerging as the connective tissue between intent and execution.

By open-sourcing its Java SDK, Deeplumen is betting that adoption—not control—will determine who shapes this next layer of the commerce stack. For enterprise brands, the message is equally clear: preparing for AI buyers is no longer theoretical.

The transition from persuading humans to informing machines is already underway. And as agentic commerce accelerates, the brands that adapt early may find themselves becoming the default choice—not for people, but for the AI agents making decisions on their behalf.

Get in touch with our MarTech Experts.

As factories return home and reshoring gathers pace, a hard truth is resurfacing alongside production lines: machines can be shipped, but know-how cannot. For decades, American manufacturing quietly outsourced not just labor, but institutional memory. Now, with geopolitical pressure mounting and experienced workers retiring, that loss is becoming painfully visible on shop floors.

Dirac, a manufacturing software company, believes it has found a way to rebuild that missing layer. Earlier this year, the company announced the general availability of BuildOS, which it calls the first automated platform for creating and managing model-based work instructions. The launch coincides with a surge of momentum for Dirac, including $10.7 million in funding and a strategic partnership with Siemens aimed at accelerating digital transformation across global manufacturing.

“Dirac has built the first and only automated work instruction platform,” said Tomás Klausing, Director for Technology Partnerships at Siemens, underscoring how central the technology could become to modern production environments.

Manufacturing’s Quiet Crisis: Lost Context

Across aerospace, defense, shipbuilding, and heavy industry, frontline operations still depend heavily on static PDFs, outdated SOPs, and informal shadowing to train workers and execute tasks. The most critical details—how to torque a joint, how to adjust a setup mid-process, what sequence to follow when something goes wrong—often live only in the minds of senior technicians.

As those workers retire and production demands increase, manufacturers face a bottleneck that can’t be solved by adding capacity alone. Engineering files may exist, but without context, they are unusable on the floor without weeks or months of onboarding.

Dirac CEO and founder Filip Aronshtein frames this as a structural problem, not a staffing one. “We don’t have a labor crisis; we have a context crisis,” he said. “You can’t reshore production if nobody remembers how to build.”

The stakes extend well beyond efficiency. While the U.S. produces roughly 1.5 submarines per year, China produces more than 10 per month. In that context, modernizing manufacturing processes is not just an economic concern—it’s a national security issue.

From Engineering Intent to Repeatable Execution

BuildOS is designed to replace static documentation with automated, animated, physics-aware, and interactive work instructions. Instead of manually creating step-by-step guides, the platform automatically translates CAD files and bills of materials into visual, shareable instructions complete with 3D models, tools, and embedded tribal knowledge.

In effect, Dirac is trying to do for manufacturing engineers what CAD once did for mechanical engineers: give them a way to encode processes once and scale them reliably across teams, facilities, and generations of workers.

“BuildOS turns engineering intent into repeatable execution and tribal knowledge into institutional knowledge—automatically,” Aronshtein said.

The platform’s impact is already visible among early adopters. Ancra Aircraft, which supplies cargo loading systems to more than 60% of the global freighter fleet, reported that work instruction creation time dropped from three days to two hours, a 95% reduction. The company also saw fewer quality errors thanks to tighter process standardization, while capturing tacit knowledge from senior technicians in a single session.

Why AI Alone Isn’t Enough

Dirac is careful not to position BuildOS as just another AI tool layered onto existing workflows. Instead, the company argues that intelligence must be embedded directly into how work is defined and executed.

Artificial intelligence helps automate translation from design to instruction, but the real value lies in context preservation—making sure every worker understands not just what to do, but why and how it fits into the broader process. That context becomes increasingly critical as factories move toward low-volume, high-mix production with rising compliance requirements.

“America is investing billions in industrial capacity,” said Trae Stephens, Partner at Founders Fund, “but without platforms like Dirac’s BuildOS, we’re just recreating the same bottlenecks that made offshoring attractive in the first place.”

Stephens added that Dirac’s advantage lies in helping manufacturers compete on intelligence, not incentives. “They’re re-architecting American manufacturing from the ground up.”

Built for the Shop Floor, Not the Slide Deck

Part of Dirac’s credibility comes from its team. The company’s founders and engineers come from hardware-heavy industries—planes, cars, submarines—where the cost of miscommunication is measured in delays, defects, and safety risks. BuildOS wasn’t designed for a theoretical factory of the future, but for the realities of today’s shop floors.

That practical focus has helped drive adoption across aerospace, defense, automotive, agriculture, and heavy equipment manufacturing, particularly among companies managing complex products with small batch sizes.

Dirac and Siemens: Closing the Digital Loop

The partnership with Siemens adds another layer to Dirac’s ambitions. Together, the companies aim to close the gap between product design and physical production, creating a tighter feedback loop between PLM and CAD systems on one side and ERP and MES platforms on the other.

“The partnership with Dirac enables us to provide additional efficiency to our customers,” Klausing said. Early integrations have already improved work instruction generation, manufacturability, and production planning. Siemens plans to deepen the integration as part of its broader digital manufacturing portfolio.

For Siemens, the collaboration supports a strategic goal: bringing emerging technologies to market faster through flexible partnerships rather than monolithic platforms.

Toward Smart Labor, Not Cheap Labor

As manufacturing shifts from mass labor to smart labor, BuildOS positions itself as a bridge between generations of workers. The platform allows companies to capture the expertise of experienced operators and make it immediately available to new hires—reducing ramp-up time while maintaining consistency.

“Our edge isn’t just speed,” Aronshtein said. “It’s repeatability. BuildOS gives every engineer, technician, and team the ability to build something once, and then build it right every time after that.”

In an era defined by reshoring, workforce transition, and geopolitical pressure, Dirac is betting that the most valuable asset a factory can protect isn’t machinery—it’s memory. And as American manufacturing attempts to relight an industrial engine long deprived of its wiring diagram, platforms like BuildOS may determine whether that engine runs smoothly or stalls again.

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Dealership CRMs have spent years getting better at conversations—but far less time thinking about what happens when it’s time to get paid. DriveCentric is betting that gap is no longer acceptable.

The dealership-focused CRM and engagement platform has announced a strategic partnership with Dealer Pay, a payments platform purpose-built for automotive dealers, to enable compliant payment collection directly inside DriveCentric. The move is designed to let dealerships complete the full workflow—from customer interaction to revenue—without jumping between disconnected systems.

Crucially, DriveCentric isn’t becoming a payments company. Dealer Pay supplies the regulated payments infrastructure, while DriveCentric embeds the capability into the CRM experience on both desktop and mobile.

Why Payments Are Moving Front and Center

Payment collection in dealerships has traditionally lived in the back office, separated from sales, service, and customer engagement tools. That separation increasingly feels outdated. Customers expect frictionless digital experiences, regulators expect airtight compliance, and dealers want fewer manual handoffs that slow deals and introduce risk.

By integrating Dealer Pay, DriveCentric aims to make payments a natural extension of engagement rather than a separate operational step.

“As the premier customer-centric engagement platform, we believe CRMs should orchestrate the entire customer journey across every touchpoint and every department within the dealership,” said Matt Leone, CEO of DriveCentric. “By embedding payments directly into DriveCentric, we’re extending the CRM engagement lifecycle through the point of revenue collection.”

The implication is clear: engagement that stops short of revenue is incomplete.

Dealer Pay Handles the Hard Parts

For DriveCentric, the partnership is as much about focus as expansion. Rather than building payments in-house, the company is leaning on Dealer Pay’s domain expertise in dealership-specific compliance, accounting workflows, and regulatory requirements.

“Engagement alone isn’t enough anymore,” said Julie Douglas, Founder and CEO of Dealer Pay. “Dealers expect systems to deliver outcomes. This partnership brings payments expertise and compliance into the CRM experience—so engagement doesn’t stop short of revenue.”

That distinction matters in automotive retail, where payment handling is tightly regulated and deeply intertwined with dealership accounting systems. A generic payments layer wouldn’t cut it; Dealer Pay is designed specifically for dealership realities.

A Broader Platform Strategy

The announcement fits neatly into DriveCentric’s broader platform strategy: remain laser-focused on customer engagement while expanding its ecosystem through bi-directional partner integrations. Instead of turning the CRM into an all-in-one monolith, DriveCentric is positioning itself as the orchestration layer—where conversations, data, and now payments converge.

This approach mirrors a wider MarTech and AutoTech trend. Leading platforms are increasingly choosing partnerships over vertical integration, allowing specialists to handle complex domains like payments while the core platform concentrates on experience and workflow.

For dealerships, that could mean fewer tools, fewer logins, and fewer dropped balls between departments.

What Dealers Should Expect Next

The integrated payments capability is expected to be available in Q1 2026, and both companies plan to outline the operational implications during a keynote-style session at the DriveCentric booth at NADA 2026.

If successful, the partnership could signal a shift in how dealership CRMs are evaluated. The next generation of platforms may not be judged solely on lead management or messaging features, but on how effectively they help dealers move from conversation to cash—securely, compliantly, and without friction.

In that sense, DriveCentric’s move isn’t just about payments. It’s about redefining where the CRM’s responsibility ends—and increasingly, that endpoint looks a lot like revenue.

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