Companion Agent works alongside frontline teams in real time, helping them prepare, respond, take action, and complete after-call work with more consistency and speed

Observe.AI, the Agentic Platform for customer experience, announced the launch of Companion Agent, the new multi-agent interface for frontline teams built to support customer service agents before, during, and after every customer interaction.

The launch expands Observe.AI’s agentic platform, which brings together AI Agents for Customers, AI Agents for Frontline Teams, and AI Agents for Operations into a single connected system. As customer-facing AI Agents automate more routine interactions, human agents are increasingly responsible for the moments that require judgment, empathy, exception handling, and deeper problem-solving. Those interactions are higher-stakes, but many frontline teams are still supported by outdated tools that provide static scripts, basic knowledge lookup, or limited real-time guidance.

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Companion Agent is designed for this new operating model. It does not just suggest what to say next. It listens to the conversation, understands the context, guides the agent through required steps, surfaces the right information from deep knowledge bases or backend systems, triggers actions, and helps complete work after the interaction. The result is a more consistent experience for customers and a more supported experience for humans.

“Customer service teams are entering a new era where AI and human agents work together across the entire customer journey,” said Swapnil Jain, CEO and Co-Founder of Observe.AI. “With Companion Agent, we are giving frontline teams an AI partner that can listen, reason, guide, and act in real time. This is an important step in our broader agentic platform vision, where AI Agents for Customers, Frontline Teams, and Operations work together to improve every conversation.”

Companion Agent gives front-line teams real-time, context-aware support across the full interaction lifecycle:

• Before the call: Companion Agent prepares frontline teams with relevant customer history, prior interaction context, profile details, and stated intent so they can start each conversation with confidence.
• During the call: Companion Agent provides step-by-step guidance, surfaces knowledge, prompts required compliance actions, detects behavioral cues, supports soft-skill coaching, and triggers workflows in real time.
• After the call: Companion Agent generates editable summaries, extracts key details, classifies dispositions, updates systems of record, triggers actions in other systems, and identifies coaching opportunities.

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Unlike legacy real-time agent assist tools built on rigid NLU models, Companion Agent is easier to configure, maintain, and improve. Teams can build and adjust guidance instantly with plain-English prompts. That means faster time to value, moving from months of configuration to days.

Companion Agent also works in concert with AI Agents for Customers, preserving context when a conversation moves from automation to a human agent. And because it connects with AI Agents for Operations, leaders can evaluate performance, trigger personalized in-the-moment coaching, and continuously improve frontline execution across every interaction.

“Customer Service teams have invested heavily in automation and self-service, but their human agents are still underserved,” said Cory Ondo, Paycor. “With Companion Agent, we’re closing that gap by ensuring every human agent has the agentic intelligence at their fingertips. It’s not just about improving productivity; it also delivers better customer experiences.”

Customers deploying Companion Agent have already seen measurable ROI through reduced average handle time, higher first-call resolution, and improved CSAT. Observe.AI offers a single, agentic platform to automate, assist, and analyze every interaction. Designed to seamlessly integrate with critical business systems and telephony providers, Observe.AI helps leaders drive efficiency, improve consistency, and unlock insights that continuously improve performance. For customers, it means faster resolutions and better experiences across every channel.

**About Observe.AI ** Observe.AI is a CX-native AI Agents platform that enables enterprises to deploy specialized agents that understand context, reason, and take action across the customer experience lifecycle. With built-in orchestration, integrations, and governance, Observe.AI powers intelligent automation that scales performance, accelerates resolution, and continuously improves outcomes.

But the surfaces are noisy. Your prospect can scroll past the ad in their feed, filter the email, skip the pre-roll, or fast-forward through the podcast break. During my time at Meta, we told marketers they had three seconds before the scroll. That pressure hasn’t gotten easier. It’s compounded.

This is the gap TV fills. Not instead of the stack you’ve already built, but alongside it.

The Buyer Is Already Watching

The people approving vendor budgets and signing software contracts are the same people watching TV after work. Reaching them there isn’t a departure from B2B marketing logic. It’s an extension of it.

Streaming or CTV ads have made this practical in ways that weren’t possible five years ago. Audience-based buying, geo tests, frequency controls, survey attribution — the measurement mechanics B2B performance marketers already know map directly onto CTV. Relay, the fintech platform for small businesses, started on CTV because it offered tighter targeting and faster feedback loops than TV, with measurement they could actually explain to a CFO. Early results showed a direct lift in branded search and site traffic. That’s not a brand metric. That’s demand generation.

IAB data put CTV ad spend at $23.6 billion in 2024, up 16% year over year. The channel is no longer experimental. Most B2B marketers just haven’t caught up to that yet.

What TV Does That Digital Can’t

Search ads get six words. LinkedIn posts compete with every other hot take and humblebrag in the feed. A 30-second TV spot is unskippable. It gets the full screen and the viewer’s attention in a way that no digital format can guarantee. Your prospect can keep scrolling past your social ad. They can skip your pre-roll. They cannot skip the TV spot.

For B2B brands with complex products, that guaranteed attention is valuable. Gusto, the payroll and HR platform, builds its TV strategy around live tentpole moments, major sporting events and cultural moments, because that’s when their customers are most engaged and most likely to be thinking about the problems Gusto solves. It’s awareness-building timed to purchase intent.

One tactic that connects TV directly to the performance stack: CTV retargeting. Someone visits your pricing page on Tuesday. By Thursday, they’re seeing your ad in their living room, on a full screen, in an environment that carries more weight than another banner in a crowded feed. It closes the loop between your ABM motion and a channel your competitors almost certainly aren’t using against the same accounts.

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TV Raises the Bar (and Maybe Your Next Round)

There’s a credibility effect to TV that doesn’t get discussed honestly enough in B2B circles. It isn’t just about awareness scores. It’s about what showing up on TV signals to the people evaluating you.

Consider the AI SDR space. Dozens of companies competing for the same accounts with near-identical pitches. If your brand has been on TV and your competitors haven’t, your prospect takes the call. You’re no longer one of many vendors in an inbox. You’re a company that operates at a different scale. That perception change happens before your sales team says a word, and it makes everything downstream more efficient.

The CMO is certainly focused on building brand awareness among their ICP, but corporate marketing is another area they own, one focused on raising the company’s profile within its industry and ultimately increasing its perceived value.

When a founder or board member sees their company’s ad during a live sporting event, their phone lights up. Fellow founders text. Investors notice. Raising a round is a different conversation when your brand has been on TV. Acquisition discussions go differently when the other side’s partners recognize your name. This rarely gets framed as marketing’s job. It is.

The Stack Is Good. It’s Just Missing a Layer.

The B2B marketers seeing the biggest results from TV aren’t treating it as a replacement for their performance programs. They’re using it as the layer those programs can’t provide: broad, credible, high-attention reach that introduces the brand to future buyers before they’re searching, and reinforces it with buyers already in your funnel.

Otter, the AI-powered meeting intelligence platform, found that well-crafted TV spots drive immediate engagement even for a complex multi-platform product. Viewers, particularly on mobile, check out the product right away. Top-of-funnel reach converting to bottom-of-funnel action. That’s the full motion.

Your ABM programs, your intent tools, your LinkedIn campaigns are all more effective when the buyer has already seen your brand somewhere that commanded their full attention. TV is that somewhere. The stack you’ve built is good. This is the layer it’s missing.

About Tatari

Tatari is building the infrastructure to modernize TV advertising for Brands, Agencies, and Publishers.

New framework and platform capabilities help CIOs turn AI agents from fragmented experiments into governed production systems with measurable business impact

Celonis, the global leader in Process Intelligence, launched the Celonis Context Model (CCM) and announced it has signed a definitive agreement to acquire Ikigai Labs, a leader in AI-powered Decision Intelligence.

“AI is only as good as the context it has. Every organization needs to give its Enterprise AI a holistic, living model of how a business truly operates. This has never been possible until now.” Carsten Thoma, Celonis President.

As organizations around the world attempt to deploy Enterprise AI, they face a critical challenge: ensuring AI does not have blind spots in understanding how the business operates. Without this understanding, AI agents cannot make a real impact, so companies struggle to see meaningful returns on their Enterprise AI investments.

The CCM fixes this by providing a dynamic, real-time digital twin of operations, which translates the business into a language AI understands. Built on process data and business knowledge from every system, application, device, and interaction across the business, the CCM gives Enterprise AI the operational clarity it needs to reason correctly, act reliably, and deliver results at scale.

The acquisition of Ikigai Labs will bring state-of-the-art enterprise Decision Intelligence and cutting-edge AI innovation — which includes planning, simulation, and forecasting capabilities — to the CCM, enabling organizations to model future-state scenarios, predict and prevent process breakdowns, and make sensible, reliable decisions.

The Operational Context Imperative

With the introduction of the CCM, Celonis is defining a new critical layer in the enterprise technology stack — the context layer. This layer unifies process data, business knowledge, operational and decision intelligence to ground Enterprise AI in reality and power its effective execution — continuously evolving as it learns from actions and outcomes across the business.

“AI is only as good as the context it has. Every organization needs to give its Enterprise AI a holistic, living model of how a business truly operates. This has never been possible until now, with the Celonis Context Model,” said Carsten Thoma, Celonis President. “And with Ikigai Labs, we’re making our market-leading platform even stronger: extending its intelligence beyond how your business runs today to how it should — and could — run tomorrow. This is what every enterprise needs to make AI work and deliver meaningful returns.”

“Precision is paramount in the healthcare industry, and you can’t accept AI that’s only right most of the time,” said Jerome Revish, SVP/Chief Technology Officer, Digital and Technology Services, Cardinal Health. “We use AI as a tool to accelerate operational insight — process context enables agents to support our team in acting with precision. Defining guardrails then gives us the confidence to act. Ultimately, context is what makes the difference between AI that’s impressive in a demo and AI that’s trusted and safe to deploy.”

“Our goal at Cosentino is to build a digital workforce of AI agents that can run and improve our business operations at scale. What we’ve learned is that an agent is only as good as the context you give it,” said Rafael Domene, CIO, Cosentino. “When you provide AI with a real understanding of your processes — the data, the business rules, the decision logic — it stops being a tool you experiment with and becomes one you trust to act. That’s what makes the difference between an agent that makes a recommendation and one that runs a process.”

“At Mondelez International, we’re in the middle of one of the most consequential technology transformations in our history while simultaneously building the foundation for agentic AI, with strong initial focus on improving our E2E flows and global shared services,” said Filippo Catalano, Chief Information and Digital Officer, Mondelez International. “We’ve learned you cannot sustainably deploy and run trusted AI agents across a landscape as complex and varied as ours, unless those agents understand and act based on the reality of how your processes run across every market, system, and function – not just how they were designed in theory. Operational context isn’t a nice-to-have; it’s the assurance for AI investments generating real value versus adding another layer of complexity.”

AI Agents You Can Trust

The acquisition will unite Ikigai Labs’ world-class talent — with deep expertise in AI, machine learning, tabular and time-series modeling, causal inference, and large-scale simulation — with the global Celonis team. Ikigai Labs was founded on nearly two decades of groundbreaking MIT research, and their experts have worked with some of the world’s most complex enterprises to reduce planning and forecasting cycles in areas like supply chain from months to minutes. As part of the agreement, Celonis will gain exclusive rights to MIT-owned patents, which Ikigai Labs had licensed from MIT, and MIT will become a shareholder in Celonis.

“Ikigai Labs was built on a simple but firm conviction: better enterprise decisions require AI that works with enterprise data. Ikigai Labs has proven foundation model technology for structured data at scale; Celonis has encoded enterprise processes. Together, we provide the fullest operational representation of business reality,” said Devavrat Shah, Ikigai Labs co-Founder, Chaired Professor of AI at MIT, and Chief Scientist, Enterprise AI at Celonis. “With the Celonis Context Model, AI agents have the hindsight, insight and foresight to intelligently adapt — and can be trusted to deliver the expected business outcomes. I am excited to continue our mission with Alex, Basti, Carsten, Martin and the entire Celonis team.”

The Context Model Powers the Trusted Platform to Industrialize Enterprise AI

The Celonis Platform and ecosystem provide end-to-end capabilities to analyze, design, and operate AI-driven processes and drive business transformation. The Platform enables customers to not just give AI the context it needs, but also to identify the best opportunities to deploy AI strategically, and to orchestrate agents, humans, and systems to work together.

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Celonis has partnered with the leaders in both the underlying data layer and the agentic execution layer to build this new context layer that bridges the two. The Celonis Platform brings data together from across the enterprise with zero-copy integrations to sources like AWS, Databricks and Microsoft Fabric (with Snowflake to be available soon), as well as pre-built connectors to systems of record like Oracle and other leading ERP and CRM platforms. Celonis has also built deep integrations with the leading agentic platforms — including Amazon Bedrock, Anthropic’s Claude Cowork, Databricks Agent Bricks, IBM watsonx Orchestrate, Microsoft Copilot and Agent365, and Oracle OCI Enterprise AI — ensuring that, however customers are building agents, the Context Model is accessible and consumable by them.

“Enterprise AI faces a reliability gap because scale isn’t enough; agents need a deep understanding of how a business actually runs,” said Heather Akuiyibo, Global VP, GTM Integration, Databricks. “By combining Celonis with the Databricks platform, companies can enable their employees to chat with their data and get trusted answers instantly with Genie and build, govern, and operationalize AI with Agent Bricks. And they can do this all with the Celonis business context required to make better decisions, faster.”

The Future of the Enterprise is AI-Driven and Composable

Celonis views the Context Model as an important step in the journey to the AI-driven, composable enterprise. In this future operating model, organizations’ systems, data, processes, people, and AI agents work together with shared context, allowing them to improve continuously, adapt instantly, and innovate freely.

“Celonis already sits at the operational core of thousands of the world’s largest enterprises, capturing how work actually happens at unprecedented depth,” said Sandesh Patnam, Managing Partner, Premji Invest. “Layering Ikigai Labs’ simulation and decision intelligence on that foundation creates a flywheel where every operational signal becomes a sharper decision and every decision sharpens the operational model – a moat competitors will struggle to replicate.”

“This is our context graph thesis made real. Celonis has built the deepest operational understanding of how enterprises actually function — as a live, process-native model of how work happens, why it breaks, and what should happen next,” said Ashu Garg, General Partner, Foundation Capital. “With the acquisition of Ikigai Labs, they’ve added the decision intelligence and simulation capabilities that make it truly effective. The companies that control this layer will define the next era of enterprise software. Celonis is that company.”

WINGIE, the leading travel marketplace in the MENA, is expanding its multilingual platform from 19 to 27 languages. This expansion reinforces WINGIE’s position as a regional technological powerhouse, making travel planning more inclusive and localized for millions of users across MENA and international markets.

Highlighting that this expansion marks an important step in WINGIE’s global growth journey, Orkun Ozkan, Chief Flights Officer of Wingie Enuygun Group, said, “Our priority is to make travel planning clearer, faster, and more seamless for users across different geographies. By increasing WINGIE’s language support from 19 to 27 languages, we are helping users search, compare, and book travel options in their native languages. This expansion is an important step in our mission to eliminate language barriers and make travel more accessible globally.”

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Özkan added that the expanded language portfolio enables WINGIE to respond quickly to local needs and strengthen its position as a leading travel platform in MENA. “As a company rooted in MENA, we are committed to bringing our localized approach to a global scale. By adding more languages, we can provide travelers worldwide with an accessible, tailored, and innovative experience,” he said.

The new language support allows WINGIE to better align with cultural nuances and user habits, while maintaining a traveler-first approach. WINGIE now offers services in 27 languages, including Arabic, English, Turkish, German, Spanish, Russian, Azerbaijani, French, Italian, Kazakh, Kyrgyz, Lithuanian, Dutch, Polish, Portuguese, Romanian, Serbian, Tajik, Uzbek, Chinese, Georgian, Hindi, Japanese, Korean, Urdu, Tamil, and Thai, reflecting its commitment to regional accessibility and global innovation.

They are tuning it out.

Not because AI lacks value, but because the way it is being marketed often fails to align with what buyers actually prioritize. For organizations making high-stakes, long-term technology decisions, the fundamentals still matter most: engineering quality, technical expertise, reliable delivery, and reduced risk.

The growing disconnect between what vendors emphasize and what buyers need is creating friction in the buying process and, in some cases, eroding trust.

The AI Messaging Overload

Right now, nearly every platform, service, and solution touts some form of AI integration. While this proliferation reflects genuine advancements, it has also created a crowded and often confusing landscape.

For buyers, the challenge isn’t access to innovation—it is clarity.

When every vendor claims to be “AI-driven,” true differentiation actually becomes more difficult. Messaging starts to sound the same across the board, leaving buyers unsure what genuinely matters versus what’s just baseline capability. In this crowded landscape, bold AI claims without clear context or proof points don’t signal innovation, they blend into the noise.

To break through, marketers can’t rely on generic AI language alone. They need to be more technically fluent and deeply informed about emerging technologies, so they can engage increasingly sophisticated buyers and craft messaging that rises above vague, cookie-cutter AI narratives.

What Buyers Actually Prioritize

Despite the emphasis on AI, B2B buyers consistently return to a core set of priorities when evaluating solutions:

1. Engineering Quality

Buyers want to know that a product is well-built, scalable, and designed to perform under real-world conditions. According to recent BIXA research of 480+ business buyer decision-makers, quality of engineering and technical expertise are statistically tied for the most important attributes they look for in a vendor.

2. Technical Expertise

Beyond the product itself, buyers assess the depth of knowledge behind it. Research shows that technical expertise and guidance are tied as the most important attributes buyers look for in a vendor. They prioritize teams that understand their industry, technical challenges, and implementation complexities. For instance, 97% of buyers say it is important that a vendor both understands and uses AI technologies in their own processes.

Expertise ultimately signals credibility: 41% of tech leaders who augment their existing teams with external engineers say certified AI experts make a vendor stand out—and that credibility directly reduces perceived risk.

3. Reliable Delivery

Execution matters as much as vision. Buyers need confidence that timelines will be met, deployments will go smoothly, and ongoing support will be dependable. Efficient delivery is a top-five priority for buyers, and it is the single most important factor for 17% of UK-based decision-makers. Overpromising, particularly in emerging technologies, can quickly erode confidence when delivery does not keep pace. Buyers are also pragmatic about how to build that confidence quickly — 47% value paid workshops specifically because they accelerate project momentum. Buyers also increasingly expect AI to reinforce that reliability through faster code generation and automated code reviews.

4. Risk Reduction

At its core, every B2B purchase is a risk management decision. Whether it’s financial risk, operational disruption, or reputational impact, buyers are evaluating how a solution minimizes uncertainty. A bold guarantee is the top determining factor for buyers, carrying 40% of the relative importance in their decision-making process. In fact, 88% of buyers would choose a vendor offering a 100% bug-free guarantee even if their price was 30% higher than competitors. Clear documentation, proven use cases, and transparent communication, such as through de-risking workshops favored by 34% of buyers, all contribute to lowering the #1 hesitation in the market: concerns over code quality and security.

These priorities are not new. What has changed is how easily they can be overshadowed by trend-driven messaging.

The Cost of Misalignment

When marketing narratives focus on AI but ignore these foundational concerns, a gap forms between expectation and reality. This misalignment leads to clear consequences:

• Longer sales cycles, as buyers seek additional validation and clarity
• Increased skepticism, particularly toward bold or vague claims
• Missed opportunities, when solutions fail to resonate despite strong underlying value

In some cases, the emphasis on AI can even distract from a company’s true strengths. A well-engineered product with a track record of reliable delivery may be far more compelling than a newer, AI-heavy offering that lacks maturity. But if the messaging doesn’t reflect that strength, buyers may never fully recognize it.

Reframing the Narrative

The solution isn’t to move away from AI, it’s to stop pretending the tools are the transformation.

The teams winning with AI aren’t the ones with the most tools. They’re the ones that changed how their engineers work. This is a people and process problem, not a procurement decision—and most vendors avoid saying it because it’s harder to sell.

The shift in messaging is simple but demanding: stop leading with what the technology is and start with what it takes to make it work. That means structured workflows, validated output at every stage, and a clear acknowledgment that AI without governance doesn’t reduce costs—it increases them. AI usage is not free; it is metered in tokens and accumulates quickly.

There’s also a risk that almost no transformation partner raises: internal resistance. AI champions inside a client organization pull ahead. Resistors create drag. If you don’t address adoption at the engineer level from day one, the transformation fails at the people layer, not the technology layer. Buyers should be asking their vendors how they handle this. Most can’t answer.

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Questions to challenge yourself and your teams:

• Is your AI architected well enough that the economics actually work?
• Do you have a structured methodology or just a capability?
• What happens when your engineers resist?

Building Trust Through Substance

Trust is the currency of B2B relationships. It is built through consistency, transparency, and proof. In a market saturated with AI claims, substance is the differentiator.

• That substance shows up in four ways:
• Clear, specific use cases that demonstrate real-world impact
• Technical depth that proves how solutions are built and maintained
• Evidence of reliability, backed by performance metrics and long-term customer outcomes
• Honest communication about capabilities and limitations

When buyers see that a company is willing to go beyond surface-level messaging, it signals confidence—and that confidence is often more persuasive than any single feature or capability.

The Opportunity Ahead

The current wave of AI enthusiasm is real, and so is the backlash forming underneath it. Buyers aren’t rejecting AI. They’re rejecting the version of AI that showed up late, overpromised, and left their teams holding the complexity.

The companies that will win this window aren’t the ones with the boldest AI narrative. They’re the ones who can answer the questions a sophisticated buyer will eventually ask: Is your AI architected well enough that the economics actually work? Do you have a structured methodology or just a capability? And what happens when our engineers resist?

Bad AI is expensive AI. Buyers are starting to do the math, and the vendors who can’t show their work are going to lose deals they don’t even know they’re losing.

The fundamentals haven’t changed. Proof, expertise, reliable delivery, and reduced risk. What’s changed is that AI has raised the stakes on all of them. The companies that understand that distinction – and can demonstrate it – are the ones that will eventually define this market.

About Vention

Vention is the premier global leader in software engineering, synonymous with technology designed for scale and the common denominator behind the world’s most successful tech-empowered enterprises, industry innovators, and startups.

Unveils first products in Circle Agent Stack to extend open infrastructure for the machine economy

Texas-based ContactPoint 360 delivers AI-operated customer experience services with human-led support through a global operating network serving enterprise and high-growth brands across multiple sectors and languages

ContactPoint 360, a privately held customer experience and outsourcing company, is presenting a CX model centered on AI-powered service and human-led empathy for enterprise clients and high-growth brands. The company works with enterprise companies to manage customer experience operations at scale.

Customer experience is no longer support infrastructure; it is growth infrastructure.

Founded in 2007, ContactPoint360 combines nearly two decades of CX expertise, more than 12 global strategic centers, and services in over 31 languages to deliver AI-operated, enterprise-scale customer experience solutions with the strategic agility modern brands demand.

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This is not conventional outsourcing.

This is enterprise customer experience, reengineered.

Built for the New Economics of Customer Experience

ContactPoint 360’s operating model reflects a larger structural shift reshaping enterprise strategy. Customer experience is no longer measured as a support cost. It is increasingly evaluated through its impact on revenue growth, customer retention, brand equity, and operational performance.

To meet these demands, ContactPoint 360 has built a unified global CX ecosystem that integrates omnichannel orchestration, operational governance, embedded AI, and customer journey optimization. This AI-operated framework positions ContactPoint 360 beyond transactional delivery, functioning as a CX partner designed to drive measurable business outcomes.

AI Embedded at the Core of Execution

While many providers position AI as an enhancement, ContactPoint 360 has embedded AI directly into the operational foundation of customer experience delivery.

Through its AI + Human model, ContactPoint360 integrates omnichannel and multilingual customer engagement, technical support, customer retention, sales, and back-office operations into a unified service ecosystem engineered to strengthen customer loyalty, operational performance, and enterprise growth. This infrastructure creates a more intelligent service model that simultaneously improves speed, precision, service quality, and enterprise scalability. The impact is an AI-operated customer experience designed for commercially measurable performance.

“In a world where products can be copied, and pricing can be matched, the one thing competitors cannot replicate is how a company treats its customers. After hundreds of client engagements across the globe, we’ve learned that AI changes the economics of CX, but people define the experience. That’s why we built ContactPoint360 around one principle: People Over Everything,” says Asad Mirza, CEO at ContactPoint 360.

Enterprise Scale Without Enterprise Rigidity

ContactPoint 360’s infrastructure supports major global organizations across healthcare, finance, insurance, telecommunications, retail, ecommerce, travel, gaming, and additional high-complexity sectors.

Its operational footprint enables:

• 24/7 multilingual support
• 99.8% SLA compliance
• Cross-market consistency
• Regulatory adaptability
• Global customer continuity

Yet unlike traditional large-scale providers burdened by inflexible delivery structures, ContactPoint 360 maintains a customer-centric partnership model that balances enterprise operational power with strategic responsiveness. This combination allows ContactPoint 360 to serve both large enterprise ecosystems and rapidly scaling brands equally effectively.

Defining the Future of Customer Experience

With more than 16+ years of operational maturity, ContactPoint 360 is entering the market not simply as another CX provider. But as part of a broader redefinition of what enterprise customer experience should deliver.

Its positioning reflects a larger transformation already reshaping the industry –

• From outsourced service to operational growth engine,
• From vendor relationship to strategic business enabler,
• From support function to competitive advantage.

The Bottom Line

ContactPoint 360’s formal market introduction signals more than company growth. It signals the rise of a new enterprise CX category, where AI, operational scale, and customer expertise converge to create measurable business transformation.

In a market crowded by legacy providers and surface-level automation claims, ContactPoint 360 is positioning itself for enterprises that require a higher standard:

• Greater operational sophistication.
• Stronger performance accountability.
• Deeper strategic impact.

As customer experience increasingly defines enterprise market leadership, ContactPoint 360 is not entering the future of CX; it is helping define it.

Retailers are sitting on more customer data than ever. Most of it is contradicting itself

Most organisations can agree on how many customers they have. Sometimes, the agreement ends there. Ask which channel best reaches a specific customer, what their lifetime value actually is, or whether they already own the product you are about to recommend, and you will get different answers from every team.

Retailers have invested heavily in omnichannel strategies, AI-powered customer experiences, and personalisation tools, yet many are building those capabilities on a foundation of data that does not align across systems. The investment is real. The unified view of the customer it depends on often is not.

Amperity’s recent expansion into the AWS Asia-Pacific (Sydney) and Asia-Pacific (Melbourne) regions brings that challenge into sharper focus for local enterprises, for whom data residency and governance are now operational requirements, not just strategic considerations.

Every team has their own version of customer data governance. Marketing deduplicates aggressively to maximise campaign reach. Analytics applies strict matching rules to avoid inflating customer counts. Operations relies on whatever the CRM says. Loyalty uses its own member ID. Each team’s logic is defensible in isolation.

However, when those conflicting views feed the same personalisation engine, the same AI models, or the same board report, the brand cannot deliver the experiences leadership is asking for.

The customer count might line up. But the loyalty programme cannot reconcile purchase history across channels because each channel defines “same customer” differently. And that is before you account for the customers who forget to scan their loyalty card, share an account with someone in their household, or never enrol in the programme at all despite being high-value repeat buyers.

Marketing sends reactivation campaigns to customers who are active in the loyalty programme but dormant in the email platform. The data is not wrong in any one system. It is wrong in aggregate.

One Amperity customer discovered that a single shopper appeared as four separate profiles in their system because they used email as their golden record. Each profile had a different lifetime value and different shopping preferences. None was a complete or accurate representation of the actual person. When that happens at scale, personalisation is not just imprecise. It is fiction.

Why customer data governance breaks down without identity resolution

Most companies govern data at the system level, and some agree on an overarching standard like email or loyalty ID. But no single identifier captures every customer interaction.

Each platform, be it email, point of sale, loyalty, support, or the data warehouse, still applies its own matching rules, its own thresholds, its own definition of what makes two records the same person. Over time, the gaps between those definitions add up.

This is the core challenge of customer data unification: not collecting more data, but connecting the data you already have into a unified customer profile that every team trusts.

Customer identity resolution connects fragmented records across systems, linking identifiers like email addresses, phone numbers, device IDs, loyalty accounts, and transactions into a single, accurate customer profile.

Identity resolution approaches fall on a spectrum. Deterministic matching links records through exact identifiers, such as a shared email address or login credential. Probabilistic and AI-based methods go further, evaluating patterns across data points to surface connections that exact matching misses, like when the same person uses different email addresses across channels or checks out as a guest in-store.

The most effective systems combine both, using deterministic rules as a foundation and machine learning to find the connections that rules alone cannot.

That gap compounds with every new tool and data source, each introducing its own governance logic. And when leadership asks the brand to personalise at scale, to recommend the right product on the right channel at the right time, the teams cannot deliver. Not because they lack the tools or the talent, but because no one has a complete picture of the customer to work from.

Try this thought experiment: pick a customer at random. How long would it take to gather enough detail to confidently send the right message, on the right channel, to drive their next purchase? Now imagine doing that for every customer.

How contextual identity graphs produce a unified customer profile

Before you can contextualise a customer, you need a complete picture. You cannot recommend the right product if you do not know what they have already purchased or returned.

You cannot choose between a discount code via SMS and an exclusive preview via email if you do not know which channel drives their purchases. You cannot calculate real lifetime value if the same person exists as four separate records.

That complete profile is the foundation. Contextual identity is what makes it useful.

Preferences change. A customer who never buys from a particular category might be shopping for a gift next week, or for someone else in their household. A full-price buyer exploring a new category for the first time might or might not respond to a promotional code.

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A single, static customer identity graph cannot handle that complexity. It forces every team into the same rigid view, and someone is always compromising.

Amperity’s Customer Data Cloud takes a contextual identity approach: purpose-built identity graphs optimised for each use case, all constructed from the same resolved foundation using first-party identity resolution.

Marketing: maximise reach. Identity graphs tuned for broad audience coverage so campaigns connect with as many real customers as possible, without duplicates inflating the numbers.

Analytics: consistency. Identity graphs built for accurate customer counts, reliable lifetime value calculations, and reporting that holds up across teams and time periods.

Operations: precision. Identity graphs optimised for transactional accuracy, where matching the right record to the right person at the right moment matters most.

Every graph is built from your first-party data. IDs stay consistent day to day. When data changes, the system learns and adapts. Connections are transparent, rules are tuneable, and every decision is auditable. No black box. No third-party data spine. No vendor lock-in.

One resolved foundation. Multiple purpose-built views. Every team works from the same truth, expressed for their specific need.

Identity infrastructure is now a compliance requirement

Transparency in data handling carries legal weight. Organisations cannot make accurate disclosures about automated decision-making unless they have clear visibility into how personal data moves through their live systems.

Consent signals, data lineage, and access controls need to be built into the foundation of customer data infrastructure from the outset.

As mentioned, Amperity’s platform is available in the AWS Asia-Pacific (Sydney) and Asia-Pacific (Melbourne) regions, allowing organisations to keep customer data resident locally while supporting performance and scalability requirements for real-time customer intelligence.

Brands that treat identity resolution as a compliance exercise end up reacting to problems. Those that build it into their data infrastructure from the start solve them before they surface, with a governed, trusted customer view that serves marketing, analytics, operations, and regulators alike.

About Amperity

Amperity’s Customer Data Cloud empowers brands to transform raw customer data into strategic business assets with unprecedented speed and accuracy. Through AI-powered identity resolution, customisable data models, and intelligent automation, Amperity helps technologists eliminate data bottlenecks and accelerate business impact. More than 400 leading brands worldwide, including Accent Group, Alaska Airlines, DICK’S Sporting Goods, BECU, and Wyndham Hotels & Resorts, rely on Amperity to drive customer insights and revenue growth. Founded in 2016, Amperity operates globally with offices in Seattle, New York City, London, and Melbourne. For more information, visit amperity.com or follow us on LinkedIn, X, Facebook and Instagram.

Celonis announced that it has been named a Leader in the 2026 Gartner Magic Quadrant for Process Intelligence. Celonis was placed in the Leaders’ Quadrant, being positioned highest on the Ability to Execute axis and furthest on the Completeness of Vision axis. Prior to this recognition, Celonis was named a Leader in the Gartner Magic Quadrant for Process Mining Platforms for three consecutive years.

“We are honored to be recognized as a Leader in the market. We owe this recognition to our customers, partners, and employees who keep pushing the boundaries of the possible,” said Alexander Rinke, co-founder and co-CEO of Celonis.

“We are honored to be recognized as a Leader in the market. We owe this recognition to our customers, partners, and employees who keep pushing the boundaries of the possible,” said Alexander Rinke, co-founder and co-CEO of Celonis. “We believe this recognition confirms the importance of Celonis Process Intelligence in enabling our customers to optimize their operations and provide their Enterprise AI with the operational context it needs to succeed.”

Marketing Technology News: MarTech Interview With Jay H. Lee, Chief Marketing and Growth Officer @ Five9

“At Florida Crystals, we’ve learned that data and public LLMs aren’t enough for our business; Enterprise AI needs the right context to drive intelligent decisions and actions,” said Kevin Grayling, Chief Information Officer at Florida Crystals Corporation. “Celonis acts as our core intelligence layer, providing the operational context our AI agents need to do the right thing. It’s the foundation that allows us to deploy AI that drives the most value across our business.”

The Celonis Process Intelligence Platform is the foundation for Enterprise AI, empowering enterprises to turn their AI ambition into compounding and meaningful value through three critical pillars:

Operational Context: It provides the operational context Enterprise AI needs to understand and improve the business through the Process Intelligence Graph—a process-centric, dynamic, and system-agnostic digital twin of operations built using advanced object-centric process mining (OCPM) capabilities.

Strategic Deployment: It helps enterprises deploy AI strategically by identifying the most impactful use cases. With the Celonis Build Experience, businesses can analyze, design, and operate composable, AI-driven processes. Our partner ecosystem shortens time to value for customers by extending the Platform with pre-built industry-specific solutions.

Seamless Integration: It provides zero-copy, bi-directional integrations with leading data lakes using Data Core and intelligently orchestrates people, agents, and existing automations through Orchestration Engine.

“To benefit from AI, you need good data that’s well-structured, and that’s where Process Intelligence and Celonis come into play,” said Julien Nauroy, Domain Leader – Process Intelligence & AI Catalyst at Renault Group. “Using object-centric process mining, we can go from having the data as it is in the original system to a well-structured model that makes sense to the AI, to be used to give more accurate answers. Ultimately, this combination of AI and Process Intelligence will be the catalyst for evolving our core processes, making them more agile and resilient.”