Kustomer Architect Aims to Replace CX Deflection Metrics With Business Outcomes

The Gist

• Unified CX platform. Kustomer Architect integrates data, workflows and human agents in one AI-native system.
• Business outcome focus. The platform emphasizes retention, loyalty, efficiency and revenue over support volume.
• CX team impact. Leaders gain tools tying agent actions to measurable outcomes across the customer lifecycle.

Kustomer on May 21 announced Kustomer Architect, an expansion of its AI-native customer experience (CX) platform. According to company officials, the tool guides brands through AI transformation toward outcomes including customer satisfaction, retention, operational efficiency and revenue growth.

The New York-based company said Architect reinforces its approach of unifying customer data, conversation history, workflows, knowledge, automation and human agents in a single platform — positioning it against legacy support systems where AI is bolted on.

Table of Contents

• AI Designed Around Outcomes, Not Just Interactions
• What Bolt-On AI Actually Costs
• Inside Kustomer Architect
• The Revenue Driver Argument — and What HexClad Demonstrates
• What CX Leaders Should Be Reporting Instead
• Kustomer Architect Capabilities
• What's on Kustomer's Radar?
• AI-Native CX Platforms Turn Support Into Revenue
• Kustomer Background

AI Designed Around Outcomes, Not Just Interactions

In an exclusive interview with CMSWire, Kustomer CEO and Co-Founder Brad Birnbaum made the case that the metrics most support organizations report against — deflection rates, handle time — are measuring the wrong thing entirely.

"Deflection tells you how many conversations did not reach a human," Birnbaum told CMSWire. "It tells you almost nothing about what happened to the customer. Did they get the answer they needed? Did they stay? Did they come back? Did they spend more? Did they tell someone else to avoid your brand?"

The consequence, he argued, is that companies can post strong deflection numbers while quietly losing customers — because the metric was designed to measure workload distribution, not customer outcomes.

"A company can have outstanding deflection numbers and still be hemorrhaging customers," Birnbaum said. "Because deflection was never designed to measure customer outcomes. It was designed to measure workload distribution."

His pitch to CFOs reframes the conversation accordingly. "What I say to CFOs is this: you have been optimizing for a cost metric in a function that can drive revenue. That is not a philosophical argument. That is a math argument."

What Bolt-On AI Actually Costs

Kustomer positions Architect against what Birnbaum described as the dominant — and problematic — pattern in enterprise CX: accumulating AI tools layered onto infrastructure never designed to support them.

"Nobody has a clean stack," he told CMSWire. "The reality we walk into is not 'brand using legacy platform.' It is a helpdesk plus three AI tools plus an internal build plus a QA layer plus a VoC tool, all stitched together with workflow automations that nobody fully understands anymore."

The damage from that complexity, he said, tends to be slow and quiet rather than dramatic — which is precisely what makes it hard to address. He offered a concrete example: an AI agent that deflects a ticket because it cannot access a customer's order history, prompting the customer to call in at five times the cost and a worse experience than if AI had never touched the interaction at all.

"The deflection number goes up. The customer satisfaction number goes down. Nobody connects the two," Birnbaum said.

A second failure pattern he cited: a customer who has contacted a brand four times about the same issue, but whose history is invisible to the AI because conversation data and order data live in separate systems. "So it responds as if this is a first contact. The customer feels invisible. That feeling compounds over time," he said.

The phrase he said he hears most often from CX leaders stuck in this dynamic: "Our AI is working, but it's not actually resolving anything end-to-end." His read on that diagnosis — deflection is happening, resolution is not — is central to how Kustomer is positioning Architect as an alternative.

Related Article: Inside CX Now: How Kustomer's AI-Native Breakthrough Highlights Enterprise Readiness

Inside Kustomer Architect

Birnbaum described Architect as the company's response to a recurring problem: CX leaders with a clear vision for AI-powered customer experience who cannot execute on it because the path from vision to production is too long, too complex and too dependent on technical resources they don't have.

"The most important thing to understand about Architect is that it is built on the belief that the people who understand your customers best — your CX operators, your team leads, the people who have been designing support experiences for years — should be the ones building your AI workflows," he said. "Not engineers. Not consultants. The people who know your business."

The platform, he explained, is built around what the company calls goals-driven AI: rather than defining procedures and hoping the AI follows them, operators define the outcome they are trying to reach — retain a customer, resolve an issue, protect a relationship — and the platform works backward from that outcome to orchestrate AI, workflows and human collaboration accordingly.

Birnbaum also pointed to open platform architecture as a differentiator, noting that Architect supports MCP (Model Context Protocol), allowing it to connect to external systems — order management, returns platforms, recommendation engines, internal data sources — that expose an MCP server.

"That is not a configuration project. That is a connection," he said. "Great AI workflows are only as good as the data they can see. Most platforms lock you into their data model. We made the opposite decision."

The Revenue Driver Argument — and What HexClad Demonstrates

The reframing of CX from cost center to revenue driver has circulated in the industry for years. Birnbaum acknowledged it directly.

"You are right that this has been a talking point for a long time," he told CMSWire when questioned about an old CX tale. "And for most of that time, it was largely aspirational. The tooling did not exist to actually close the loop between a CX interaction and a business outcome."

What changed, in his view, is AI operating against unified customer context — the ability to surface a customer's full purchase history, prior interactions, order status, and likelihood to return, and use that context in real time to make decisions that affect retention, revenue, or loyalty.

HexClad, cited in the announcement as a customer reference, represents Kustomer's current proof point for that argument. Birnbaum was specific about what the cookware brand's results do and don't demonstrate.

"They are not a company with an abstract CX problem," he said. "They are a fast-growing brand with real operational pressure: managing high contact volume, maintaining strong customer satisfaction, and doing both without scaling headcount linearly with revenue."

The outcome Kustomer points to — reduced cost-to-serve without a corresponding drop in CSAT — is, Birnbaum argued, the combination that makes the revenue driver case meaningful. "If you are cutting costs by degrading experience," he said, "you are just deferring the churn. You have not solved anything."

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What CX Leaders Should Be Reporting Instead

When CMSWire asked what metrics should replace deflection and handle time in C-suite reporting, Birnbaum pointed to three: customer retention influenced by CX interactions, revenue protected through support interventions and CSAT correlated to AI interaction type — not as a satisfaction measure in isolation, but as a predictor of next purchase behavior.

He was candid that the shift is not purely a technology problem. "Changing the metric you report against means changing the accountability structure around your team. That takes organizational will, not just better tooling," he added.

His advice for CX leaders not yet positioned to make that ask: start building the data before making the case. "Even if you are still reporting deflection to the board, start tracking the retention correlation internally. Start capturing the revenue influence numbers. Build the case before you make the ask."

"The metric always follows the proof," Birnbaum said. "Build the proof first." 

Kustomer Architect Capabilities

According to Kustomer, the platform shifts CX teams from reactive support to outcome-driven operations.

What's on Kustomer's Radar?

Since regaining independence from Meta at a reported $250 million valuation — down from its $1 billion acquisition price — Kustomer has pursued aggressive AI-native repositioning. The company closed a $30 million Series B in August 2025 led by Norwest, with Battery Ventures, Redpoint Ventures and Boldstart Ventures participating. The raise coincided with Anna Fisher's appointment as CMO.

In December 2025, Kustomer expanded the platform with AI assistants for automation and observability, plus Data Explorer, a natural-language analytics layer for querying operational data.

In March, it launched Kustomer AI as a standalone platform layering predictive and rule-based AI onto existing Zendesk environments without migration, with Salesforce connectors to follow. On April 2, the company launched Signals, surfacing real-time sentiment, escalation risk and behavioral context to agents across Kustomer and Zendesk environments — with Gametime as an early deployment.

AI-Native CX Platforms Turn Support Into Revenue

AI-native CX platforms are reorganizing support around unified customer profiles, turning a cost center into a revenue source. Kustomer's shift to an AI-native architecture illustrates the trend, connecting conversations, context and actions across touchpoints to address what the company described as persistent industry failures: disconnected data, contextless AI and revenue lost inside support queues.

ROI Starts Behind the Scenes

Early evidence points to operational gains before customer-facing improvements materialize. Agentic CX returns show up in routing accuracy, reduced handle time and fewer escalations.

• AI agents resolve up to 40% of inquiries across chat, email, voice and WhatsApp
• Generative AI-enabled agents drove 14% increases in issue resolution per hour
• GetVocal's governed AI agents produced 31% fewer escalations and 70% deflection rates
• Organizations using AI in CX report up to 25% gains in customer satisfaction scores

Kustomer Background

Kustomer, founded in 2015, targets mid-market and enterprise support organizations. The platform consolidates customer data and conversation histories into a single workspace with omnichannel communication, AI-powered workflows, intelligent routing and analytics.

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