A new challenger in the CRM market is making a bold pitch to startups frustrated with legacy platforms: moving your entire CRM stack should take about an hour.

Lightfield, an AI-native customer relationship management platform built for high-growth companies, has launched an automated migration agent designed to transfer data from platforms such as HubSpot with minimal manual work. The system processes exported CSV files and automatically maps contacts, companies, deals, custom fields, and pipeline stages—eliminating the manual data cleaning and field mapping typically required during CRM migrations.

The company says the tool can process up to 90,000 records per hour while preserving relationships across records, a step often cited as one of the most complex aspects of CRM switching.

A Fast-Growing AI-Native CRM

Lightfield emerged from stealth in November 2025 and has already gained traction among startups. According to the company, more than 2,500 organizations have created workspaces on the platform, with hundreds migrating directly from HubSpot.

The rapid growth highlights a broader trend in enterprise software: startups increasingly want AI-native tools that automate routine workflows rather than simply storing data.

Traditional CRM platforms were built for manual input. Sales teams log calls, update pipeline stages, and write notes after every customer interaction. AI-driven systems like Lightfield aim to replace that model by automatically capturing and analyzing communication data.

A Data Ownership Debate

The launch arrives at a moment when data ownership inside CRM platforms is becoming a sensitive topic.

During an investor call discussing HubSpot’s fourth-quarter 2025 results, CEO Yamini Rangan indicated that the company plans to “monitor, meter, and monetize” third-party agent access to customer data on its platform.

That stance suggests that as AI-powered development tools become more common, software vendors may increasingly control how external applications interact with platform data.

Lightfield CEO Keith Peiris argues for the opposite approach.

“Your data is yours—and you should be able to use it, unencumbered, with any agentic tool you choose,” Peiris said in announcing the migration agent. He added that all objects and attributes inside Lightfield are accessible through its API without egress fees.

Peiris frames the strategy as preparation for a future in which AI agents interact fluidly with enterprise systems rather than operating within tightly controlled software ecosystems.

“The future of work will be far more fluid than the last generation of SaaS,” he said.

The CRM Problem for Startups

For many startups, CRM systems are essential but frustrating infrastructure.

Sales teams often spend hours each week updating records—logging calls, entering meeting notes, and updating deal stages. Even with consistent effort, CRM databases frequently remain incomplete because information depends on manual entry.

As companies scale, the problem compounds. New hires inherit CRM records that may lack historical context, forcing founders and senior sales leaders to remain heavily involved in deals simply because institutional knowledge isn’t captured consistently.

The friction associated with switching CRM platforms has historically reinforced this dynamic. Migrating from systems like HubSpot often requires weeks of consulting work, extensive field mapping, and careful data cleaning to avoid losing critical information.

Lightfield’s migration agent aims to eliminate that barrier by automating the entire process.

How the Migration Agent Works

The system follows a structured, multi-step workflow designed to make CRM switching mechanical rather than manual.

First, users export their CRM data—typically contacts, companies, deals, and custom fields—as CSV files. The migration agent analyzes the structure of those files and confirms mapping before importing any data.

Next, the system configures the Lightfield workspace to mirror the original CRM structure, including pipeline stages and custom properties. Once configured, records are imported and linked automatically so relationships between contacts, accounts, and deals remain intact.

After migration, teams can connect their email and calendar accounts. Lightfield then ingests communication data to build contextual histories for every contact and opportunity.

Companies can also upload transcripts from recorded sales calls. The platform associates those conversations with the relevant contacts and deals, creating searchable context across the CRM.

An AI-Driven CRM Workflow

Once the migration is complete, Lightfield’s AI layer begins automating many of the tasks traditionally handled manually by sales teams.

The platform continuously logs calls, emails, and meetings, automatically generating summaries and suggested follow-up actions. Pipeline analytics are also generated directly from conversation data rather than relying on manually updated fields.

For founders and sales leaders, the shift could significantly reduce time spent on CRM maintenance.

Tyler Postle, co-founder of Y Combinator-backed startup Voker, described the difference after switching platforms.

“Using HubSpot, I was a data hygienist,” Postle said. “Using Lightfield, I’m a closer.”

The Rise of AI-Native Business Software

Lightfield’s launch reflects a broader movement across enterprise technology.

AI-native tools are emerging across categories—from productivity software to analytics platforms—designed to automate data capture and decision-making rather than simply organizing information.

In the CRM category, that shift could reshape long-standing incumbents whose platforms were built around manual workflows.

For startups adopting AI-driven development environments and automation tools, the ability to integrate CRM data seamlessly with external agents may become an increasingly important differentiator.

Lightfield is betting that reducing migration friction—and offering open access to business data—will accelerate that transition.

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Enterprise analytics provider SplashBI is strengthening its North American sales leadership as demand for AI-powered data intelligence continues to grow.

The company announced that Brian Morgan has joined the organization as Regional Sales Director for North America, bringing more than 15 years of enterprise technology sales experience and a background in workforce analytics and talent intelligence.

The appointment signals SplashBI’s continued push to expand its footprint among enterprise organizations seeking to transform fragmented data into actionable business insights.

Expanding Enterprise Analytics Adoption

SplashBI has been positioning its platform as a unified environment for enterprise reporting, analytics, and decision intelligence. The company’s AI-powered system integrates data from across organizational systems—finance, HR, and operational platforms—to surface insights automatically and deliver real-time answers to business teams.

For many organizations, the challenge is not collecting data but translating it into decisions. As enterprises generate massive volumes of workforce and operational information, analytics platforms are increasingly expected to provide predictive insights rather than static reports.

Morgan’s role will focus on accelerating adoption of SplashBI’s platform among North American enterprises navigating that shift.

“Organizations today are under increasing pressure to get more from their data—faster and with greater confidence,” the company noted in announcing the appointment. Morgan’s experience working with finance and workforce leaders positions him to guide those conversations as companies evaluate AI-driven analytics platforms.

Deep Experience in Workforce and People Analytics

Morgan joins SplashBI from Crunchr, where he served as Director of Sales for North America. At Crunchr, he focused on enterprise workforce analytics solutions that help organizations interpret employee data to support planning and performance decisions.

His broader career includes leadership and strategic sales roles at companies focused on HR technology and workforce intelligence, including Workhuman, Gloat, and SkyHive.

That background reflects a growing area of enterprise analytics: translating workforce data into operational insights. HR and talent systems generate significant datasets—from performance metrics to workforce planning signals—but many organizations still struggle to integrate and interpret that information at scale.

Morgan’s expertise in this space could help SplashBI connect its analytics platform to one of the fastest-growing data categories inside large enterprises.

Strengthening SplashBI’s North American Strategy

In his new role, Morgan will focus on expanding SplashBI’s enterprise presence across North America. The position involves working closely with the company’s marketing, customer success, and product teams to introduce organizations to the platform’s analytics capabilities.

The strategy reflects a broader trend in enterprise software: platforms that combine reporting, analytics, and AI-powered insights into unified environments rather than isolated tools.

Organizations increasingly want analytics systems that can serve multiple departments—from HR and finance to operations—while maintaining consistent data governance and trusted insights.

SplashBI’s platform aims to address that need by connecting data sources across the enterprise and automatically surfacing insights that teams can act on in real time.

AI-Driven Decision Intelligence Gains Momentum

The appointment also comes as enterprise analytics platforms evolve from traditional reporting tools into decision intelligence systems.

Rather than simply presenting dashboards or historical reports, modern analytics solutions incorporate machine learning models that identify patterns, flag anomalies, and recommend actions based on current data signals.

As companies seek faster and more confident decision-making, platforms capable of combining automation, analytics, and AI are becoming central to enterprise technology stacks.

Morgan’s experience selling workforce intelligence and AI-driven analytics solutions aligns with that direction, positioning him to help organizations adopt tools that transform raw data into strategic insights.

Academic Background

Morgan holds a Bachelor of Business Administration in Marketing from the Isenberg School of Management at University of Massachusetts Amherst.

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Artificial intelligence is rapidly redefining how digital marketing strategies are built, executed, and optimized. As organizations manage larger datasets and increasingly complex consumer behavior patterns, AI-powered systems are becoming central to marketing operations across industries.

From audience targeting to campaign optimization, AI-driven platforms are helping marketing teams analyze data faster and adapt strategies in near real time. The shift marks a departure from traditional marketing workflows that relied on static datasets and scheduled adjustments, replacing them with dynamic systems capable of responding continuously to changing performance signals.

From Static Campaigns to Adaptive Marketing

Historically, digital marketing campaigns were planned around fixed timelines and periodic optimization cycles. Campaign managers would analyze performance reports, make adjustments, and relaunch initiatives based on historical results.

AI-driven systems are changing that model. Modern marketing platforms now process real-time engagement signals and automatically refine campaign parameters such as messaging, timing, and distribution channels.

This adaptive approach allows campaigns to evolve as consumer behavior shifts. For example, if engagement trends change mid-campaign, AI tools can modify audience targeting or ad placement without waiting for manual intervention.

The result is a more responsive marketing environment where performance improvements can occur continuously rather than through scheduled optimization cycles.

Audience Targeting Becomes More Behavioral

Audience segmentation is also undergoing a transformation as AI systems analyze behavioral signals at scale.

Instead of relying solely on demographic attributes, AI-powered platforms increasingly evaluate interaction history, browsing behavior, and engagement patterns to identify audience intent. These insights enable marketers to build highly granular segments and tailor campaigns to users whose behavior indicates a higher likelihood of interest or conversion.

This shift reflects a broader trend toward individualized digital experiences. As personalization becomes an expectation rather than a novelty, AI systems provide the analytical backbone that enables marketers to deliver more relevant messaging.

AI Influences Content Strategy

Content marketing workflows are also being influenced by AI-assisted analysis.

Marketing teams are increasingly using predictive tools that evaluate search behavior, keyword trends, and audience interest patterns. These insights help guide editorial planning, allowing brands to develop content aligned with current demand.

Rather than relying entirely on manual forecasting, marketers can now incorporate predictive signals into content calendars. This approach improves consistency across channels while helping teams respond more quickly to emerging topics and shifting search trends.

For organizations managing large content ecosystems, these tools can significantly streamline planning processes while supporting more data-driven decision-making.

Advertising Automation Expands

AI is also playing a growing role in paid advertising management.

Many advertising platforms now incorporate automation features capable of adjusting campaign parameters in real time. Budget allocation, bidding strategies, and audience targeting can be optimized algorithmically based on engagement and conversion data.

These automated adjustments are designed to improve campaign efficiency and maximize return on ad spend while reducing the need for constant manual oversight.

For marketers, the shift means that strategic planning increasingly focuses on campaign objectives and creative direction, while optimization tasks are handled by automated systems.

Email Marketing Adopts AI Optimization

Email marketing platforms are adopting similar AI-driven capabilities.

Automation tools are being used to determine optimal send times, personalize messaging, and refine customer journey workflows. By analyzing recipient behavior—including open rates, click patterns, and past engagement—AI systems can continuously refine email campaigns to improve performance.

These systems also enable marketers to create more responsive automation sequences that adjust based on subscriber behavior, helping maintain relevance throughout the customer lifecycle.

Search Strategies Adjust to AI Algorithms

The influence of artificial intelligence extends beyond marketing tools themselves. Search engines and digital platforms are increasingly relying on AI-driven algorithms to determine content visibility.

Search ranking systems now place greater emphasis on user intent, contextual relevance, and overall experience. As a result, digital marketing strategies are evolving to prioritize structured content, technical site performance, and accessibility.

Automated SEO monitoring tools are also becoming more common, helping marketing teams track site health and identify performance issues that could affect search visibility.

Social Platforms Lean on AI Distribution

Social media platforms have also embedded AI deeply into their content distribution models.

Feed algorithms analyze engagement patterns and interaction history to determine which content appears in front of users. For marketers, this means that engagement signals—such as comments, shares, and watch time—play a significant role in determining content reach.

As a result, social media strategies increasingly emphasize content designed to drive meaningful interaction rather than simply maximizing posting frequency.

Data Consolidation Becomes Essential

The growing role of AI in marketing is also changing how organizations approach data analysis.

Modern marketing operations generate large datasets across websites, advertising platforms, social media channels, and email campaigns. AI systems are increasingly used to consolidate and interpret these datasets, enabling organizations to identify trends and uncover insights that would be difficult to detect manually.

This centralized analysis supports more informed strategic decisions and helps marketers respond quickly to shifts in consumer behavior.

Balancing Automation With Human Strategy

Despite the expanding capabilities of AI systems, human expertise remains a critical component of digital marketing.

Strategic planning, brand positioning, and creative storytelling continue to rely heavily on human insight. AI tools can process data and optimize execution, but defining campaign objectives and crafting compelling narratives still requires human direction.

Brett Thomas, owner of the New Orleans-based firm Jambalaya Marketing, emphasized the evolving relationship between automation and strategy.

“Marketing strategies are becoming more dynamic as AI systems process data and adjust campaigns in real time,” Thomas said. “The focus is shifting toward systems that respond to behavior rather than relying on static planning.”

Governance and Data Integrity Challenges

As organizations integrate AI technologies into marketing workflows, operational considerations are also emerging.

Maintaining data accuracy and consistency is essential for ensuring reliable AI outputs. Inaccurate or incomplete data can lead to flawed insights and ineffective campaign decisions.

Companies are also evaluating governance frameworks to ensure that data usage aligns with privacy regulations and transparency standards. These considerations are becoming increasingly important as AI-driven marketing systems rely on large volumes of behavioral and engagement data.

The Next Phase of AI-Driven Marketing

The role of artificial intelligence in digital marketing is expected to expand as platforms continue to evolve and data availability increases.

Future developments will likely focus on deeper automation, predictive intelligence, and cross-channel integration. Marketing ecosystems are moving toward systems capable of responding to consumer behavior in real time while continuously optimizing performance.

For organizations navigating competitive digital environments, AI is increasingly becoming not just a tool—but a core operational capability shaping how marketing strategies are developed and executed

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Moments captured on smartphones rarely come out perfectly. Someone blinks, the lighting is off, or the camera angle misses the mark. Instead of settling for those imperfect memories, Wondershare believes AI can give users a second chance.

The creativity and productivity software company has introduced Relumi, a new AI-powered mobile application designed to improve flawed photos and effectively “retake” images after the moment has passed. The app combines advanced AI models with image reconstruction capabilities to fix common photo issues, adjust expressions, and even modify camera angles—while preserving the original context of the scene.

The launch reflects a broader shift in consumer imaging software, where artificial intelligence is increasingly moving beyond traditional editing tools toward generative enhancement. Instead of simply tweaking brightness or cropping frames, modern AI apps are beginning to rebuild visual elements to create more polished versions of real-world moments.

AI Moves Beyond Photo Editing

Smartphone photography has exploded over the past decade, fueled by social media sharing and increasingly powerful camera hardware. But despite better sensors and computational photography, real-life conditions—lighting, timing, and positioning—still lead to imperfect captures.

Wondershare positions Relumi as a solution to that gap. Rather than requiring users to retake photos in the moment, the app uses AI to reconstruct and refine images afterward.

According to the company, the technology is powered by advanced AI models, including an integration with Nano Banana Pro, which enables the app to analyze visual elements in a photo and intelligently modify them while maintaining realistic results.

“Photos are more than images—they are memories,” said Dirk, Head of Product at Wondershare. “With Relumi, we are redefining what it means to ‘retake’ a photo. Instead of missing the moment, users can now go back and take it better.”

Fixing Faces, Poses, and Group Photos

One of Relumi’s primary capabilities focuses on repairing portrait flaws. The Photo Flaw Repair feature automatically detects issues such as closed eyes, awkward facial expressions, or minor pose inconsistencies. Once identified, the system adjusts the affected elements while preserving the original lighting and background details.

For group photos—a notoriously difficult category—Relumi introduces Multi-Person Photo Repair. The feature isolates individual subjects within a group image and corrects problems like blinking or unflattering expressions independently.

That capability could prove particularly useful for event photography, where retakes are often impossible. A single group shot from a wedding, conference, or family gathering can now be refined without altering the rest of the scene.

AI-Driven Lighting and Atmosphere

Beyond facial adjustments, Relumi also focuses on environmental improvements. The Smart Environment Preset Retake feature analyzes the scene’s lighting conditions and recommends optimized mood presets.

Users can apply cinematic-style enhancements that modify lighting, tone, and atmosphere without manual editing. The system attempts to maintain natural realism while giving photos a more polished aesthetic—something typically reserved for professional editing workflows.

Changing Camera Angles After the Fact

Perhaps the most technically ambitious feature is 3D Angle Adjustment Retake, which uses AI-based modeling to reconstruct images from alternative perspectives.

By generating a three-dimensional understanding of the scene, the tool can correct distorted selfies or shift the apparent camera viewpoint. For example, a selfie taken from an awkward angle could be rebalanced to appear more natural.

This type of AI-driven perspective correction is emerging as a new frontier in computational photography, combining elements of image generation, depth estimation, and 3D reconstruction.

Turning Photos into Animated Memories

Relumi also expands beyond still images with its Photo-to-Video with Sound feature.

The tool animates static photos by generating subtle movements such as facial micro-expressions or environmental motion. It can also add audio-enhanced elements to create short video clips suitable for social media sharing.

As platforms like TikTok and Instagram increasingly favor video content, this functionality allows users to transform older photos into dynamic posts without recording new footage.

Targeting Creators, Families, and Memory Keepers

Wondershare says Relumi is designed for a wide range of users—from casual smartphone photographers to social media creators.

Families preserving milestone moments, travelers capturing scenic experiences, and digital creators looking for more engaging visual content could all benefit from the app’s AI capabilities. By automating complex editing processes, the platform aims to make high-quality image enhancement accessible without professional design skills.

This approach mirrors a broader industry trend. AI-driven creative tools are rapidly lowering the technical barrier for photo and video production, enabling everyday users to achieve results that previously required advanced editing software.

Availability

Relumi is available as a mobile application for both iOS and Android devices. Users can download the app and explore its features directly on their smartphones, with additional details available through Wondershare’s official website and social media channels.

As generative AI continues to reshape creative software, tools like Relumi highlight how the next phase of photo editing may focus less on correction—and more on reconstruction.

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As AI-powered search rapidly reshapes how companies evaluate software, G2 is rolling out a set of new product innovations aimed at reinforcing something many algorithms still struggle with: trust.

The B2B software marketplace and review platform announced a series of updates designed to help vendors increase visibility in AI-driven discovery while providing buyers with more credible signals during software evaluations. The new capabilities include richer buyer-generated content, LinkedIn-based identity verification for reviewers, an AI integration powered by Anthropic’s Claude, and expanded market intelligence tools for go-to-market teams.

Together, these updates reflect a larger shift in the B2B buying journey. As organizations increasingly rely on AI assistants and conversational search to shortlist vendors, the quality of the underlying data—reviews, buyer behavior, and market signals—becomes a decisive factor in which products get surfaced.

According to G2, its platform already processes signals from more than 200 million annual buyers and hosts over six million verified reviews. The company now wants those signals to become foundational inputs for AI-driven decision-making.

“AI is transforming how companies analyze markets and make decisions, but those systems need trusted data signals to produce meaningful insights,” said Alexis Zheng, Chief Product and Technology Officer at G2. Zheng described the new releases as part of the company’s effort to position G2 as the “trust layer” for the software ecosystem in the AI era.

New Buyer Content Designed for Humans—and AI

One of the biggest changes comes in how G2 structures user-generated feedback. The company is introducing several new formats aimed at extracting richer context from reviewers while making the information easier for AI engines to interpret.

The first is structured category FAQs. These provide authoritative answers to common questions across software categories, helping potential buyers quickly understand key features, limitations, and use cases. At the same time, the standardized format makes it easier for AI systems to ingest and reference the information when generating answers.

G2 is also introducing guided discussion prompts within reviews. Rather than leaving feedback entirely open-ended, these prompts encourage users to discuss implementation experiences, real-world use cases, and product trade-offs. The result is deeper contextual insight into how software performs beyond marketing claims.

Another update focuses on feature comparisons. Using signals from review data, G2 now automatically identifies how users naturally describe product capabilities and converts those insights into structured feature lists. This allows buyers to compare tools more quickly without manually scanning dozens of reviews.

Collectively, the goal is to make the buying journey more grounded in real user experiences—while simultaneously improving how AI systems interpret that information.

LinkedIn Verification Adds Identity to Reviews

In an era where AI-generated content is becoming increasingly difficult to distinguish from authentic feedback, G2 is also doubling down on reviewer credibility.

The company has expanded its partnership with LinkedIn by integrating LinkedIn’s identity verification directly into G2’s moderation workflow. Reviews can now display verification signals tied to a user’s professional identity, including their employer or educational background.

The move is intended to ensure that reviews reflect genuine professional experiences rather than anonymous commentary or automated submissions.

Early results from the integration suggest measurable improvements. Since launching the verification process, G2 reports collecting more than 100,000 reviews from LinkedIn-verified users. The platform has also seen a 40 percent drop in review rejection rates and a 13-point increase in approval rates, while moderation efficiency improved by roughly 25 percent.

“Trust in B2B buying starts with credibility,” said Adam Kahn, Senior Manager on LinkedIn’s Trust Team. “As AI-generated content becomes more prevalent, visible verification signals matter more than ever.”

Claude Integration Connects AI Assistants to G2 Data

Perhaps the most technically ambitious announcement is G2’s new Model Context Protocol (MCP) architecture, which allows AI assistants to directly access G2’s structured buyer intelligence.

The first integration connects the platform to Claude, the AI assistant developed by Anthropic.

Rather than relying solely on general web content or scraped information, the integration enables AI tools to reference verified buyer reviews, competitive research signals, and marketplace data from G2 in real time.

In practice, that means teams can ask AI systems questions like which competitors buyers are evaluating, which product strengths appear most frequently in reviews, or whether customers might be considering alternatives.

These insights could help sales, product, and customer success teams spot potential churn risks earlier—or identify accounts actively researching competing platforms.

The approach reflects a broader industry trend: enterprises increasingly expect AI tools not just to summarize public information but to integrate proprietary and high-quality datasets into workflows.

New Market Intelligence for Go-to-Market Teams

Alongside its AI integrations, G2 is expanding its analytics capabilities with new intelligence features aimed at helping vendors understand shifts in buyer behavior and competitive dynamics.

One of the key additions is Competitive Pulse, a dashboard that combines CRM opportunity data with G2 buyer intent signals and competitor research activity. The feature highlights deals that may be at risk and identifies areas where rival vendors are gaining traction.

Another addition is Churn Threat detection, which surfaces signals when existing customers begin researching competing products on G2. For customer success teams, that early warning could provide a crucial window to intervene.

G2 is also introducing analytics focused on Answer Engine Optimization (AEO), a growing discipline focused on how brands appear within AI-generated answers. The new AEO traffic insights show how often buyers discover products through conversational AI responses or AI-driven search results.

Meanwhile, expanded buyer intent data reveals which categories and vendors companies are actively researching across the G2 marketplace.

For investors and market analysts, the company is also introducing spend and contract intelligence based on more than $100 billion in SaaS purchasing agreements. By linking purchasing activity with research behavior, the dataset aims to provide a more accurate picture of category momentum and vendor growth.

The Bigger Picture: Trust as the New SEO

The timing of these announcements reflects a broader shift across the B2B technology landscape.

For years, software discovery has largely been driven by traditional search engines, vendor websites, and analyst reports. But as generative AI tools increasingly answer research questions directly, the sources those systems rely on are becoming strategic assets.

Platforms that host credible, structured, and verified data—like G2—are positioning themselves as the underlying knowledge layers for AI-driven buying decisions.

That dynamic could fundamentally reshape how vendors approach visibility. Instead of focusing solely on ranking in search results, companies may increasingly compete to appear in AI-generated answers backed by trusted third-party signals.

G2 unveiled the new capabilities during its latest quarterly Innovation Event, titled “Winning with Trust in AEO,” where executives emphasized that verified identity, authentic buyer voice, and real behavioral data will become essential inputs for AI-driven discovery.

If that vision holds, the future of software marketing may depend less on who shouts the loudest—and more on whose customers speak most credibly.

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Attribution has a trust problem—and it usually shows up when marketing, finance, and BI teams compare numbers.

MessageGears is aiming to fix that with a new conversion reporting suite built directly on top of the data warehouse. The pitch is simple: if everyone relies on the same data source, everyone should see the same results.

It’s a pragmatic take on a long-standing issue in marketing analytics—fragmented data pipelines and inconsistent reporting across platforms.

Why Warehouse-Native Changes the Game

Most marketing platforms rely on SDKs and APIs to pull in conversion data. That works—until it doesn’t.

Because those systems only capture partial customer interactions, they often diverge from the “source of truth” housed in enterprise data warehouses. The result? Conflicting metrics, endless reconciliation, and diminishing trust in marketing reports.

MessageGears flips that model.

Instead of copying or syncing data into another system, it reads data directly where it already lives—in the warehouse. Conversion reporting is built on that same foundation, tying revenue and campaign performance directly to trusted datasets.

In theory, that eliminates discrepancies between marketing dashboards and financial reporting.

Aligning Marketing, BI, and Finance

For large enterprises, attribution isn’t just about measuring performance—it’s about organizational alignment.

When marketing reports don’t match finance numbers, decision-making slows down. Teams spend more time debating data than acting on it.

MessageGears’ approach aims to solve that by:

• Using the warehouse as the single source of truth
• Feeding attribution data back into the same environment
• Ensuring consistency across departments

That means marketers, analysts, and executives are all working from the same dataset—no translation required.

What’s New in Conversion Reporting

The new capabilities go beyond basic campaign tracking.

Key features include:

• Real-time event ingestion: Live purchase and behavioral data pulled directly from the warehouse
• Cross-channel attribution: Revenue tied to email, SMS, push, in-app, and embedded messaging
• Custom event tracking: Teams can define and standardize event labels across reports
• Unified dashboard: A centralized view of conversions, revenue, and performance trends
• Campaign-level insights: Metrics like purchases, revenue, and average order value

Attribution defaults to a 24-hour last-click model, with customizable windows by channel.

Flexibility That Legacy Platforms Lack

One of the more notable differences is flexibility.

Most platforms require marketers to define conversion events before launching campaigns—and lock those definitions in place. Change your mind later, and you’re out of luck.

MessageGears removes that constraint.

Once events are configured at the warehouse level, they can be applied to any campaign at any time—even retroactively. That opens the door to deeper analysis without forcing teams to predict every reporting need upfront.

The Bigger Trend: Data Warehouses as the Marketing Core

MessageGears’ launch reflects a broader shift in martech architecture.

As companies centralize data in cloud warehouses, the traditional model—where each tool maintains its own dataset—is starting to break down. Increasingly, the warehouse is becoming the operational hub, not just a storage layer.

That shift is driving demand for “warehouse-native” tools that operate directly on centralized data rather than duplicating it.

The Bottom Line

MessageGears isn’t introducing a flashy new AI feature—it’s solving a more fundamental problem: data trust.

By anchoring conversion reporting in the warehouse, the company is betting that accuracy and alignment matter more than additional layers of analytics.

If it works, marketers may finally spend less time defending their numbers—and more time improving them.

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Startup infrastructure is expensive—especially when it comes to building reliable, global customer communications from scratch. Infobip has spent the past five years trying to remove that barrier.

The company says its Startup Tribe Programme has now supported thousands of startups and scaleups across more than 120 countries since its launch in 2021, offering a mix of financial credits, technical infrastructure, and ecosystem access.

A Shortcut to Global Communication Infrastructure

At its core, the program is designed to help early-stage companies skip one of the most resource-intensive steps: building and managing communications systems.

Participants get access to up to $60,000 in credits for Infobip’s services—covering messaging, email, authentication (like OTP), and channels such as WhatsApp—along with connections to advisors, investors, and accelerators.

That’s not just a cost-saving measure. It’s a strategic one.

By outsourcing communications infrastructure, startups can redirect resources toward product development, sales, and growth—areas where speed often determines survival.

Real-World Impact: From Cost Savings to Growth

For startups like HotelSync and Cleanster, the benefits go beyond free credits.

The program enables automation of core workflows—transactional messaging, user verification, and customer engagement—while reducing operational overhead. More importantly, it frees up budget that can be reinvested into go-to-market strategies.

That’s a common pain point for startups: infrastructure costs can quietly consume capital that would otherwise fuel growth.

Why This Matters Now

The timing aligns with a broader shift toward API-first, cloud-based communications platforms (CPaaS), where companies plug into existing infrastructure instead of building their own.

Infobip’s approach mirrors similar ecosystem plays by major cloud providers—but with a sharper focus on startups and scaleups navigating early growth stages.

As AI-driven customer engagement becomes more critical, access to scalable, intelligent communication tools is increasingly a competitive advantage.

More Than Credits: Building an Ecosystem

Infobip is positioning Startup Tribe as more than a perks program—it’s an ecosystem play.

Beyond credits, startups gain access to:

• A global network of investors and accelerators
• Technical mentorship and onboarding support
• Scalable communication tools powered by AI

That combination aims to create long-term platform loyalty while helping startups grow into enterprise customers.

It’s a familiar strategy in SaaS: invest early in startups, and grow with them over time.

The Bigger Picture: Competing in the CPaaS Market

The cloud communications space is crowded, with major players competing on pricing, global reach, and developer experience.

Infobip’s differentiation lies in its global footprint and its focus on enabling omnichannel engagement—from SMS and email to OTT platforms like WhatsApp—all under one roof.

Programs like Startup Tribe help the company expand its footprint among early-stage companies, a segment that can deliver outsized long-term value.

The Bottom Line

Five years in, Infobip’s Startup Tribe reflects a simple but powerful idea: remove infrastructure friction, and startups can move faster.

As the company celebrates its 20th anniversary, the program underscores its broader strategy—embedding itself deeper into the growth journeys of startups worldwide.

For founders, that means one less system to build—and one more lever to scale.

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Audience targeting has a freshness problem—and Cognitiv thinks it has the fix.

The company has launched AudienceGPT™, a new AI-powered tool designed to help marketers move beyond static audience segments and outdated behavioral data. Instead of relying on past actions, AudienceGPT focuses on predicting what consumers are likely to do right now.

It’s a shift from retrospective targeting to real-time intent modeling—and one that could reshape how campaigns are planned and executed.

From Static Segments to Living Audiences

Traditional audience targeting hasn’t evolved much: group users based on past clicks, visits, or searches, then keep targeting them long after those signals have gone cold.

AudienceGPT takes a different approach.

Powered by Cognitiv’s deep learning engine—the same foundation behind ContextGPT—the platform analyzes behavioral signals to map where consumers are in their purchase journey. It then builds predictive audience profiles that update as frequently as every 15 minutes.

That means campaigns can adapt in near real time, aligning spend with current intent rather than historical behavior.

How It Works: AI Meets Media Buying

Instead of manually building segments or relying on predefined taxonomies, marketers can describe their ideal audience in plain language via a chat-based interface.

AudienceGPT then:

• Generates dynamic persona recommendations
• Explains the reasoning behind each audience profile
• Identifies similar users programmatically across channels
• Adjusts audience size and precision based on campaign needs

Under the hood, the system uses LLM-powered reasoning and synthetic consumer journey modeling—essentially simulating how people move through decision-making processes.

The result is a targeting model that evaluates individuals, not just segments.

Built for Omnichannel Activation

AudienceGPT isn’t limited to a single channel. It’s designed to work across CTV, digital audio, social, and programmatic environments.

Marketers can activate audiences through Cognitiv’s DSP or export them as Deal IDs and segments into external DSPs and SSPs—making it flexible within existing adtech stacks.

Key integrations include:

• Magnite for CTV and streaming inventory
• Index Exchange for real-time supply-side activation
• LiveRamp Marketplace for scalable audience distribution

These partnerships help extend AudienceGPT’s reach across display, video, CTV, and audio—areas where targeting precision has historically lagged.

Why This Matters Now

The timing is critical.

Consumer behavior is shifting faster than ever, especially across streaming and digital audio platforms. Yet most targeting systems still operate on delayed signals, creating a gap between insight and action.

AudienceGPT is designed to close that gap—bringing targeting closer to real-time decision-making.

It also addresses a growing industry challenge: signal loss. As cookies fade and privacy regulations tighten, marketers need new ways to understand intent without relying on traditional identifiers.

Cognitiv’s approach—using deep learning to infer intent rather than track it directly—could offer a path forward.

The Bigger Picture: AI Reshapes Audience Strategy

Cognitiv’s launch reflects a broader trend in adtech: the move from rule-based targeting to AI-driven prediction.

Instead of defining audiences manually, marketers increasingly describe goals and let AI systems build and optimize segments dynamically. That’s faster, more scalable, and potentially more accurate—if the models hold up.

AudienceGPT also builds on Cognitiv’s recent momentum. Its ContextGPT product saw significant growth in 2025, suggesting strong demand for AI-driven targeting solutions.

The Bottom Line

AudienceGPT isn’t just another targeting tool—it’s an attempt to redefine how audiences are created, updated, and activated.

If it works as advertised, marketers could spend less time building segments and more time acting on real-time insights—reaching consumers when intent is highest, not after it’s passed.

In a market where timing is everything, that’s a compelling advantage.

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