Zoho just scored a notable enterprise win—this time in the building materials industry.

Zoho Corporation announced the successful deployment of Zoho CRM at Acme Brick Company, one of the largest brick manufacturers in the United States and a subsidiary of Berkshire Hathaway. The rollout replaced Acme Brick’s previous CRM provider in a tightly executed migration completed within five months.

For a 130-plus-year-old manufacturer operating across 13 states and more than 40 sales locations, that’s not a minor upgrade. It’s a structural shift in how sales operations are managed.

From Frustration to Full Migration

Acme Brick signed with Zoho in March 2025, activated Zoho CRM in August, and completed a full migration from its previous provider by October.

The timeline is significant in enterprise CRM terms. Large, distributed sales organizations often face extended deployment cycles—especially when legacy integrations and entrenched workflows are involved.

According to Stan McCarthy, Senior Vice President of Sales at Acme Brick, the previous CRM experience was marked by limited support and third-party implementation gaps.

“With our previous CRM provider, it felt like they didn’t have any skin in the game regarding our success,” McCarthy said. “They recommended a third-party implementation partner that disappeared after our contract ended, leaving us unsupported.”

In contrast, Zoho’s Enterprise Business Solutions (EBS) team handled the implementation directly and remained engaged post-deployment.

“We weren’t handed off to an implementation team, because Zoho was our implementation team,” McCarthy added.

That continuity of support appears to have been a deciding factor.

Customization Without Rewriting the Business

After a failed CRM deployment, Acme Brick evaluated 10 vendors—including major players like Salesforce and HubSpot. The common sticking point: most platforms would have required the company to significantly alter its existing sales processes to extract value.

For an organization with decades-long employee tenure and deeply embedded workflows, that was a nonstarter.

“We needed an intuitive, integratable CRM that our salespeople, some of whom have been with the company for 30 or 40 years, would actually use,” said Julie Lloyd, Sales Enablement Manager at Acme Brick.

Zoho CRM ultimately won out due to its customization capabilities, low- and no-code development features, and integration flexibility—allowing Acme Brick to modernize systems without restructuring a business model that has been successful for more than a century.

That philosophy aligns with what Zoho calls “progressive modernization”—updating infrastructure without forcing disruptive process overhauls.

Deep Integration Across 13 States

The deployment wasn’t limited to basic CRM functionality.

Acme Brick has already:

• Built custom functions within Zoho CRM

• Integrated the platform with legacy systems and workflows

• Activated adoption across 40+ sales locations

• Standardized operations across 13 states

The result, according to Zoho, has been improved engagement from prospective clients and stronger solution adoption internally.

In industries like manufacturing and building materials—where digital transformation often trails SaaS and retail sectors—ease of use and integration depth are critical. Adoption failures are common when platforms feel imposed rather than embedded.

Why This Matters for Zoho

Zoho has long positioned itself as a value-driven alternative to heavyweight CRM vendors. But enterprise-scale wins—particularly with established, diversified businesses—signal growing traction beyond SMB markets.

Ajay Kummar Bajaj, Global Head of EBS at Zoho, framed the partnership as a long-term alignment.

“Acme is a diversified, unique building materials business requiring a powerful yet adaptable sales platform that is simple to implement, simple to use, simple to develop, and simple to maintain,” Bajaj said.

The emphasis on simplicity stands out. In the CRM market, feature density often increases complexity. Zoho’s pitch here centers on adaptability without forcing operational reinvention.

CRM Market Context

The CRM space remains fiercely competitive, with Salesforce continuing to dominate enterprise accounts and HubSpot expanding upmarket. At the same time, AI-driven CRM enhancements—from predictive forecasting to automated engagement—are reshaping buyer expectations.

For legacy-heavy industries like manufacturing, however, foundational needs often outweigh bleeding-edge AI features:

• Reliable integration with ERP and legacy systems

• Scalable support across distributed sales teams

• Configurability without heavy developer overhead

• Vendor involvement beyond contract signature

Zoho’s direct implementation model through its EBS team may resonate with organizations burned by partner-led deployments that fade post-launch.

A Century-Old Company, Modernized

Acme Brick services residential and commercial projects across direct and distributor markets, operating from 45 sales locations in its primary 13-state footprint.

Modernizing CRM across such a distributed footprint—without alienating long-tenured sales teams—is often the hardest part of digital transformation.

If the early adoption signals hold, Zoho’s deployment could become a reference case for progressive modernization in traditional manufacturing sectors.

For Zoho, it reinforces a broader narrative: enterprise CRM doesn’t have to mean enterprise complexity.

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Algolia is taking its GenAI search strategy to one of Europe’s biggest AI stages.

The AI Search and Retrieval Platform—powering more than 1.75 trillion queries annually—announced that Chief Technical Officer Xavier Grand will speak at AI Day 2026 in Paris on February 10. Hosted at Station F and organized by France Digitale, the 10th annual AI Day is expected to gather 2,000 AI and NoCode executives, researchers, and investors.

For Algolia, the appearance isn’t just another conference slot. It’s a signal of how the company sees enterprise search evolving in 2026: less keyword box, more conversational engine.

From Search Box to GenAI Experience

Grand’s session, titled “Algolia & The GenAI UX Revolution: Merging Search and Conversation,” will be delivered during dotAI’s “The Tech Track” in the Workshop Area from 12:20–12:35 PM.

The core theme: enterprises must rethink search as a hybrid experience that blends traditional retrieval with conversational and context-aware AI systems.

“At Algolia, we’re trusted by thousands of retailers and millions of developers around the world to equip them with fast, intuitive AI search solutions that build lasting customer loyalty and drive engagement and conversions,” Grand said in a statement. “In 2026, this means enterprise search strategy must be all-encompassing to include agentic, generative, and search experiences.”

That framing reflects a broader industry shift. As generative AI interfaces increasingly sit on top of structured search infrastructure, companies are working to unify conversational AI with real-time retrieval systems rather than treating them as separate tools.

Why This Matters for Enterprise Search

Search has long been one of the highest-converting touchpoints in digital commerce and SaaS platforms. But GenAI has raised user expectations. Consumers no longer just want results—they want synthesized answers, personalized recommendations, and conversational guidance.

For enterprises, that creates a balancing act:

• Maintain speed and precision in traditional search

• Integrate generative responses without hallucinations

• Support agentic workflows and context-aware interactions

• Ensure scalability across global traffic volumes

Algolia’s positioning centers on merging deterministic retrieval with generative layers, an approach that mirrors the broader Retrieval-Augmented Generation (RAG) movement across enterprise AI.

With more than 18,000 businesses and millions of developers using its platform, Algolia is well positioned to influence how production-grade GenAI UX is implemented—not just prototyped.

A Broader Technical Lineup

Grand’s talk will share the stage with other technical leaders:

• Mirakl data scientists Mehdi Elion and Clément Labrugere will discuss advancements to their ad platform.

• Guillaume Moigneu, Field CTO at Upsun (formerly Platform.sh), will present on measuring agent code readiness—a topic gaining urgency as autonomous agents move from concept to deployment.

The session lineup underscores a recurring theme at AI Day: AI maturity. The conversation is shifting from experimentation to operational readiness.

A Founding Engineer’s Perspective

Grand brings a long institutional memory to the stage. A founding engineer at Algolia, he joined when the company had just five employees and helped scale it to more than 750. His focus has consistently centered on building infrastructure that is reliable, predictable, and usable at global scale—no small task when orchestrating trillions of annual queries.

That background lends weight to his emphasis on practicality. While much of the GenAI conversation focuses on model capabilities, infrastructure leaders increasingly emphasize reliability, latency, governance, and integration.

In other words, what works in a demo must also work in production.

The European AI Context

AI Day, now in its 10th year, has become a flagship gathering for France’s AI ecosystem. Hosted at Station F—often described as the world’s largest startup campus—the event reflects Europe’s growing ambition in AI innovation.

As regulatory frameworks like the EU AI Act begin to shape deployment standards, enterprise AI discussions in Europe are increasingly tied to governance, transparency, and production resilience.

Algolia’s emphasis on merging search and conversation within structured enterprise frameworks aligns with that environment: ambitious, but operationally grounded.

The Bigger Picture

The shift from keyword-driven search to conversational, agentic experiences is accelerating. But enterprises can’t afford to abandon the reliability of structured retrieval systems in favor of purely generative interfaces.

If anything, the GenAI UX revolution depends on stronger search foundations—not weaker ones.

At AI Day 2026, Algolia is expected to make the case that the future isn’t search versus conversation. It’s search plus conversation—at scale.

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CoreWeave is stepping out from behind the infrastructure curtain.

The AI-focused cloud provider (Nasdaq: CRWV) has launched its first fully integrated brand campaign, “Ready for Anything, Ready for AI,” featuring Chance the Rapper. The move signals a shift from purely technical positioning to a broader identity play—one that aims to cement CoreWeave as what it calls “The Essential Cloud for AI.”

The timing isn’t accidental. As AI development moves from research labs into full-scale production environments, infrastructure providers are racing to define their role in the next phase of growth.

From GPU Provider to AI Backbone

CoreWeave has built its reputation as a purpose-built cloud optimized for AI workloads—particularly GPU-intensive training and inference tasks. Unlike legacy hyperscalers retrofitting general-purpose clouds for AI, CoreWeave markets itself as infrastructure designed from day one for machine learning at scale.

The new campaign leans into that narrative.

“AI is entering a moment where performance, scale, and durability shape what’s possible,” said Jean English, CoreWeave’s Chief Marketing Officer. “‘Ready for Anything, Ready for AI’ expresses our belief in what innovators need next: an AI cloud designed to perform at scale, evolve with ambition, and carry bold ideas forward.”

In other words: AI experimentation is over. Production-grade AI demands production-grade infrastructure.

Why a Brand Campaign Now?

Infrastructure companies rarely lead with celebrity-driven campaigns. But the AI cloud market is no longer niche—it’s strategic.

CoreWeave’s brand debut comes amid rapid expansion, both organically and through acquisitions. Recent additions such as Weights & Biases, OpenPipe, and Monolith have broadened the company’s footprint across the AI development lifecycle.

That creates a new challenge: stitching together a unified narrative.

The campaign serves as a branding consolidation moment, aligning acquisitions under a single CoreWeave identity while signaling confidence to enterprise buyers and investors.

It also reflects intensifying competition. Hyperscalers like AWS, Microsoft Azure, and Google Cloud continue to pour billions into AI infrastructure. Meanwhile, specialized AI clouds are emerging to serve labs, startups, and enterprises seeking performance advantages.

In that environment, differentiation increasingly hinges on more than hardware specs. Brand perception matters.

The Production Shift in AI

CoreWeave’s messaging taps into a broader industry transition: AI moving from experimentation to deployment at scale.

In early generative AI cycles, developers focused on model innovation. Now, the conversation centers on reliability, scalability, cost optimization, and performance under load. Enterprises aren’t just building demos—they’re running customer-facing applications.

That shift elevates infrastructure as a strategic differentiator.

CoreWeave positions itself as the “critical backbone” for AI innovators, emphasizing:

• Purpose-built AI cloud architecture

• High-performance GPU access

• Scalable infrastructure for training and inference

• Enterprise-grade reliability

The implication is clear: when AI systems power real-world products, downtime and bottlenecks aren’t theoretical risks—they’re business risks.

Why Chance the Rapper?

Featuring Chance the Rapper signals an effort to humanize and mainstream a highly technical brand.

While details of the campaign creative weren’t fully outlined, the choice suggests CoreWeave is targeting a broader innovation audience—not just ML engineers, but founders, executives, and cultural pioneers investing in AI.

As AI becomes embedded in creative industries—from music generation to content production—the crossover appeal isn’t random. It reflects how AI infrastructure is increasingly tied to cultural as well as commercial breakthroughs.

Consolidating After Acquisitions

CoreWeave’s recent acquisitions—including AI developer platform Weights & Biases—expand its footprint beyond compute infrastructure into tooling and workflow management.

That positions the company closer to a vertically integrated AI platform model, rather than a pure-play cloud provider.

The new campaign acts as a unifying layer across these assets. Instead of marketing disparate tools, CoreWeave is presenting a single promise: readiness for AI at scale.

For enterprise buyers evaluating long-term AI partnerships, cohesion matters. Fragmented branding can signal fragmentation in execution.

The Competitive Context

The AI infrastructure market is heating up:

• Hyperscalers are bundling AI compute with foundation models and enterprise contracts.

• Nvidia’s ecosystem continues to shape GPU availability and pricing dynamics.

• Emerging AI-native clouds are competing on performance and specialization.

CoreWeave’s challenge is to maintain differentiation while scaling rapidly.

By emphasizing “Ready for Anything,” the company leans into flexibility and performance—two attributes enterprises prioritize when AI workloads are unpredictable and compute demands spike overnight.

The Bigger Picture

AI infrastructure providers are no longer invisible enablers. As AI becomes central to enterprise strategy, the companies powering it are stepping into the spotlight.

CoreWeave’s first integrated brand campaign marks a maturation point—not just for the company, but for the AI cloud category itself.

When infrastructure becomes mission-critical to innovation, brand trust and clarity become strategic assets.

CoreWeave is betting that the next chapter of AI won’t just be about smarter models. It will be about the clouds that carry them.

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HitPaw, known for its AI-powered visual enhancement tools, has announced that global content creation platform Comfy is integrating the HitPaw Image and Video Enhancement API directly into its workflow. The move embeds professional-grade upscaling, denoising, and generative restoration tools inside Comfy’s ecosystem—no external apps required.

For creators and platforms juggling compressed images, low-light footage, or AI-generated content (AIGC), the promise is simple: better visuals, fewer steps.

Enhancement Without Leaving the Platform

The integration allows Comfy users to apply HitPaw’s enhancement models directly within the platform. That includes one-click portrait and scene upgrades, dual-model pipelines for faces and backgrounds, and super-resolution options at 2x and 4x.

Rather than applying blanket sharpening filters, HitPaw’s approach splits processing between subject and environment. Portraits get texture-aware skin treatment while backgrounds are sharpened independently—a workflow increasingly standard in high-end editing suites but now accessible via API.

Key capabilities include:

• One-click portrait and scene enhancement

• Dual-model face and background pipelines

• 2x and 4x super-resolution

• High-fidelity upscaling for DSLR and AIGC images

• Diffusion-based generative recovery for heavily compressed visuals

• Batch processing and API automation

That last point matters for platforms like Comfy, where creators are often working at scale.

A Full Stack of Image Models

HitPaw’s Image Enhancer integration includes a range of specialized models designed for different visual contexts:

Face Clear Model (2x, 4x)
Dual-model portrait upscaling with softened facial rendering and sharpened background detail.

Face Natural Model (2x, 4x)
Texture-preserving enhancement that maintains realistic skin detail.

General Enhance Model (2x, 4x)
Super-resolution tuned for animals, plants, architecture, and general scenes.

High Fidelity Model (2x, 4x)
Premium enhancement for high-resolution DSLR photos, posters, and AI-generated imagery.

Sharp Denoise and Detail Denoise Models (1x)
Noise reduction for mobile and standard camera photos.

Generative Portrait and Generative Enhance Models (1x–4x)
Diffusion-based restoration designed for heavily compressed or degraded images.

The inclusion of diffusion-based generative models signals how restoration workflows are evolving. Instead of simply enhancing existing pixels, newer models reconstruct plausible detail—a trend increasingly visible across AI creative tooling.

Video Enhancement Goes Multi-Frame

The integration extends beyond still images. Comfy also incorporates HitPaw Video Enhancer, bringing frame-aware restoration and ultra HD upscaling into the platform.

Video restoration presents a tougher challenge than static enhancement. Inconsistent face sharpening across frames can produce flicker or unnatural transitions. HitPaw addresses this with multi-frame processing pipelines designed for temporal consistency.

Key features include:

• Multi-frame face restoration

• Face-first enhancement pipelines

• GAN- and diffusion-based defect repair

• HD-to-Ultra HD upscaling

• API support for automated workflows

For creators producing social video, marketing assets, or repurposed archival content, maintaining facial identity across frames is critical. The platform’s “face-first” approach prioritizes identity retention and skin texture accuracy over aggressive smoothing.

Video Model Lineup

Face Soft Model
Noise and blur reduction optimized for facial regions.

Portrait Restore Model
Multi-frame fusion to enhance facial detail with smooth transitions.

General Restore Model
GAN-based restoration for broader video use cases.

Ultra HD Model
Premium upscaling designed to generate natural textures.

Generative Model
Diffusion-driven repair for severely degraded or low-resolution footage.

As video continues to dominate digital engagement, automated enhancement at scale is becoming table stakes—particularly for platforms serving global creator communities.

Why This Matters in 2026’s Creator Economy

The integration reflects a broader shift in martech and creator tooling: AI enhancement is moving from standalone desktop software into embedded APIs and creator ecosystems.

Platforms increasingly compete on workflow efficiency. If creators must export assets to third-party tools for polishing, friction rises. Embedding enhancement directly into the creative environment reduces that friction—and potentially increases platform stickiness.

There’s also a market signal here. With AIGC content proliferating and mobile-first capture still producing imperfect assets, demand for upscaling and restoration continues to rise. At the same time, audience expectations for visual quality keep climbing.

HitPaw’s partnership with Comfy positions enhancement as infrastructure rather than optional post-production.

Competitive Context

The AI enhancement space is crowded, with tools from Adobe, Topaz Labs, Runway, and emerging generative platforms pushing real-time restoration and AI-assisted editing. What differentiates API-driven partnerships like this one is distribution.

Instead of competing for end users directly, HitPaw embeds its capabilities into platforms already serving creator bases. That model mirrors trends seen across AI transcription, translation, and content moderation APIs.

For Comfy, integrating enhancement could become a differentiator in attracting creators who prioritize visual polish without added complexity.

The Bigger Picture

As digital platforms move toward integrated AI stacks—generation, editing, enhancement, and distribution inside one environment—partnerships like HitPaw and Comfy’s suggest the future of creative tooling is modular but seamless.

Creators don’t necessarily want more tools. They want better output with fewer steps.

Embedding AI enhancement at the workflow level may be the clearest way to deliver that promise.

Get in touch with our MarTech Experts.

Brandi AI, an enterprise platform focused on AI visibility and Generative Engine Optimization (GEO), has released its 2026 predictions for how brands will compete in an era dominated by AI-generated answers. The company argues that visibility inside tools like ChatGPT, Gemini, Perplexity, and Google’s AI Overviews will soon matter as much—if not more—than traditional search rankings.

If SEO defined the last decade of digital marketing, Brandi AI believes GEO and Answer Engine Optimization (AEO) will define the next.

“Even when a search starts on Google, it now often ends with an AI-curated summary,” said Leah Nurik, CEO and co-founder of Brandi AI. “That shift has quietly changed the rules of visibility, thought leadership, and customer acquisition.”

The company outlines eight trends it says will separate market leaders from laggards by the end of 2026.

1. GEO Becomes a Standard Marketing KPI

According to Brandi AI, by mid-2026 marketing teams will track brand mentions inside AI-generated answers the same way they track keyword rankings today.

GEO and AEO won’t replace SEO—but they’ll sit alongside it. SEO ensures discoverability; GEO ensures AI systems understand, cite, and recommend a brand.

That distinction matters. In a world where users increasingly accept a single AI-generated response instead of clicking through ten blue links, citation frequency may become the new ranking position.

2. “Content Is King” — Again

Brandi AI predicts a return to scaled, consistent publishing—but with a twist. This time, the goal isn’t just ranking. It’s authority reinforcement for AI models.

The company claims brands publishing 12 new or optimized pieces of digital content see up to 200x faster visibility gains compared to brands publishing four. AI systems, it says, favor:

• Clear, expert-authored material

• Recent and frequently updated content

• Evidence-backed insights

• Consistent publishing velocity

In other words, thin content written for search algorithms won’t cut it. AI models appear to reward demonstrable expertise and freshness—signals aligned with Google’s broader E-E-A-T standards.

3. A Widening Gap Between GEO Leaders and Laggards

Brandi AI warns of a compounding advantage effect.

Brands that proactively manage AI visibility will increasingly shape category narratives inside AI responses. Those that ignore it may simply stop appearing in consideration sets altogether.

In a buying journey where AI summaries act as de facto research assistants, omission can equal invisibility. And invisibility can equal lost pipeline.

This dynamic mirrors early SEO adoption in the 2000s—except the feedback loop could be faster, since AI answers consolidate influence into fewer visible outcomes.

4. The Collapse of the Clickstream

Perhaps the most disruptive prediction: fewer clicks across the web.

As AI-generated answers reduce the need to visit multiple sites, traditional pay-per-click advertising models could see diminished returns—particularly in B2C markets.

Brandi AI suggests brands will increasingly treat AI visibility as a performance channel in its own right. Instead of optimizing solely for traffic, marketers may optimize for:

• Inclusion in AI-generated recommendations

• Accurate AI summaries

• Positive contextual framing

Website optimization strategies will also shift as AI-referred traffic enters through nontraditional paths, such as deep blog content rather than homepage funnels.

The implication: clicks may decline, but influence may not—if brands adapt.

5. PR Becomes a Growth Lever for AI Influence

Public relations may experience a strategic renaissance.

Because AI models draw heavily from authoritative third-party content, earned media could directly influence how brands are described inside AI responses.

Agencies, Brandi AI argues, will need to think beyond journalist placements and consider how coverage shapes machine-readable narratives. AI visibility KPIs may soon sit alongside impressions and share of voice in PR dashboards.

In effect, PR moves from “reputation management” to “AI narrative engineering.”

6. A GEO Tool Gold Rush

As measurement formalizes the discipline, a new category of AI visibility platforms is emerging.

Brandi AI predicts rapid growth in software tools designed to:

• Track brand mentions across AI engines

• Audit AI-generated summaries

• Benchmark against competitors

• Provide actionable recommendations

This mirrors the early days of SEO tooling, when rank trackers and backlink analyzers reshaped the marketing stack.

The company positions itself as a leader in this category, targeting mid-market and enterprise clients that want structured governance over AI-driven discovery.

7. Advertising Moves Inside AI Answers

One of the more forward-looking predictions involves advertising models embedded within AI responses.

Rather than bidding for clicks, brands may pay for transparent, clearly labeled placements within AI-generated recommendations. Ethical disclosure, trust signals, and responsible integration will become central concerns.

While standards remain immature, early experimentation is already underway across major platforms. If AI becomes the primary decision interface, monetization will inevitably follow.

8. Influencer Marketing Gets Rewritten

Follower counts may matter less than citation impact.

Brandi AI predicts brands will begin evaluating influencers based on whether their content meaningfully shapes what AI systems learn and repeat. Blogs, expert commentary, and attributable long-form content may influence AI outputs more than viral social posts.

The metric shift: from engagement metrics to AI citation footprint.

Why This Matters Now

The broader context supports the company’s thesis. Google’s AI Overviews, Microsoft’s Copilot integrations, and the explosive adoption of ChatGPT-style assistants are accelerating the shift from search-driven discovery to answer-driven discovery.

In B2B markets especially, where research cycles are long and information density is high, a single AI-generated summary could frame an entire vendor shortlist.

That changes the economics of visibility.

SEO optimized for rankings. GEO optimizes for inclusion in answers.

The question marketers face isn’t whether AI will influence buyer journeys—it already does. The real question is whether brands will measure and manage that influence proactively or let competitors define the narrative.

Brandi AI is betting that by 2026, AI visibility won’t be experimental. It will be operational.

Frequently Asked: GEO and AI Visibility

What is Generative Engine Optimization (GEO)?
GEO focuses on ensuring AI systems like ChatGPT and Gemini accurately understand, summarize, and recommend a brand. It complements SEO rather than replacing it.

How does GEO differ from SEO?
SEO drives traffic through rankings and clicks. GEO drives inclusion and citation inside AI-generated answers.

Why does AI visibility impact growth?
If AI answers shape buyer research, brands excluded from those summaries risk being excluded from purchase consideration entirely.

As AI platforms increasingly become the interface between brands and buyers, visibility may hinge less on where you rank—and more on whether you’re mentioned at all.

That’s a subtle shift. But if Brandi AI’s predictions hold, it may be the most consequential one of the decade.

Get in touch with our MarTech Experts.

ising and visibility initiative designed to support The Filipino American Museum in Las Vegas (TFAM) while spotlighting local leaders, entrepreneurs, and advocates.

The campaign aims to enroll 100 Legacy Circle Members, each committing to a $1,500 total contribution structured to ensure both direct nonprofit support and sustainable media visibility:

• $1,000 donated directly to The Filipino American Museum in Las Vegas

• $500 allocated to professional video podcast production and promotional visibility

Direct Support for Cultural Preservation

The $1,000 museum donation goes directly to TFAM and supports operational costs, cultural programming, preservation initiatives, and long-term sustainability. By routing donations straight to the museum, the program emphasizes financial transparency and donor confidence—a growing priority in community-based fundraising initiatives.

“This project is about legacy, transparency, and community impact,” said Jocelyn Bett, Co-Founder and CEO of Optimize Media Marketing. “By clearly separating the museum donation from production sponsorship, we ensure the museum receives direct support while honoring contributors with meaningful visibility.”

Visibility as a Force Multiplier

The remaining $500 sponsorship funds the production and promotion of a professionally produced video podcast feature, designed to amplify both the donor’s story and the cultural mission of TFAM. The sponsorship covers filming, editing, multi-platform distribution, and promotional support across OMM’s local and community-focused media network.

As part of the Local Visibility Legacy Package, each Legacy Circle Member receives:

• A professionally produced video podcast interview, recorded either on-site at TFAM or at the OMM office and studio

• Strategic promotion across high-visibility community media platforms

• Official recognition as a TFAM Legacy Circle Member

Podcast interviews will be hosted by Jocelyn Bett and filmed inside the museum when possible, creating a setting that connects personal stories with Filipino American history, culture, and advocacy.

Multi-Platform Community Reach

Each Legacy Circle feature will be distributed across OMM’s community media ecosystem, including:

• FilipinoSpotlights.com

• FilipinoTownLVDirectory

• LasVegasSpotlights.com

This multi-channel approach is designed to extend reach beyond a single audience, helping donors increase local visibility while reinforcing awareness and engagement around TFAM’s mission.

Open Through April 30

The Legacy Circle Giving Project is open now through April 30, with all museum donations made directly to The Filipino American Museum in Las Vegas. OMM emphasizes that this structure ensures clarity, accountability, and measurable community impact.

By combining cultural philanthropy with modern storytelling and media amplification, the Legacy Circle Giving Project reflects a growing trend toward impact-driven visibility—where support for community institutions and personal brand storytelling reinforce one another.

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As B2B buying journeys become longer, less linear, and increasingly shaped by search—both human and AI-driven—Angelfish Marketing is repositioning itself for that reality. The UK-based digital marketing agency has unveiled a new brand identity and redesigned website, signaling a clear evolution toward search-led growth for SaaS, technology, and B2B brands.

The refresh introduces a modern visual identity, updated color palette, and refined messaging, but the changes go deeper than aesthetics. The new positioning reflects how Angelfish now frames its work: helping B2B companies win visibility, demand, and revenue in an environment where search engines, AI assistants, and paid media increasingly influence decisions long before sales conversations begin.

Built Around How B2B Buyers Actually Search

Unlike traditional agency rebrands that focus on tone or personality, Angelfish’s website redesign is structured around how modern B2B buyers research, evaluate, and convert. Content and navigation are designed to map to real-world buying behavior—multiple touchpoints, extended evaluation cycles, and a growing reliance on search as the first and last mile of demand generation.

The agency’s updated messaging emphasizes its expanding expertise across:

• SEO and organic search

• Paid search and performance media

• Content strategy for demand and pipeline

• AI-driven search visibility, including optimization for emerging discovery models

That blend reflects a wider industry shift. As AI-generated answers, zero-click search results, and intent-driven paid media reshape visibility, B2B marketers are under pressure to prove not just traffic—but impact.

A Sharper Focus on Measurable Growth

Angelfish’s refresh reinforces its focus on performance and ROI, a theme increasingly central to B2B marketing budgets in 2026. The agency positions itself as a partner to in-house teams tasked with showing clear links between digital activity and pipeline outcomes—no small feat in complex, multi-stakeholder sales cycles.

“This refresh is about better representing the work we’re doing and the results we’re delivering,” said Dom Moriarty, Head of Growth at Angelfish Marketing. “Over the past few years, we’ve built deep experience supporting SaaS and technology brands with search-led strategies that drive visibility, pipeline, and revenue.”

That emphasis mirrors a broader trend across B2B marketing services: fewer generalists, more specialists who can tie search performance directly to commercial metrics.

From SEO Agency to Search-Led Partner

The new site positions Angelfish explicitly as a B2B digital marketing, search, and SEO agency, but the framing is less about channels and more about outcomes. The agency works alongside internal marketing teams to increase qualified demand, improve visibility at key buying moments, and clarify the impact of digital investment.

Angelfish’s client base spans SaaS, technology, recruitment, professional services, and financial services, sectors where trust, expertise, and discoverability are tightly linked.

Rather than acting as an execution-only vendor, the agency emphasizes its role as a strategic growth partner, combining data-led insight with hands-on delivery designed to scale.

Why This Matters Now

The timing of the rebrand is notable. As AI reshapes search behavior and B2B buyers self-educate more deeply before engaging sales, agencies are being forced to rethink how they approach visibility and demand generation.

Search-led growth—where SEO, paid media, content, and AI discovery are treated as a unified system—is emerging as a core strategy for B2B teams under pressure to do more with leaner budgets.

Angelfish’s repositioning reflects that shift, aligning its brand with how B2B marketing actually works today—not how it worked when SEO was just about rankings.

Marking the Relaunch

To coincide with the new brand and website launch, Angelfish is offering a free B2B marketing consultation, aimed at helping companies assess their current search performance and identify growth opportunities heading into 2026.

For B2B and SaaS brands navigating increasingly complex search and discovery landscapes, the relaunch underscores a simple message: visibility isn’t just about being found—it’s about being found at the right moment, for the right reasons.

Get in touch with our MarTech Experts.

Enterprise AI teams don’t lack ideas—they lack time. Between standing up infrastructure, wiring data sources, enforcing security, and proving compliance, building production-ready AI agents often takes longer than the business will tolerate. Qubika is aiming to close that gap.

The company has announced the public launch of QBricks, a Built on Databricks solution designed to streamline the entire lifecycle of enterprise AI agents, from development and evaluation to deployment and ongoing observability.

Already in use across multiple Qubika client environments, QBricks now enters the market as a centralized accelerator built for scale, compliance, and real-world enterprise constraints.

From Experimentation to Production—Faster

QBricks is positioned as a response to one of the biggest friction points in AI adoption: the amount of undifferentiated work required just to get started. Teams often spend weeks—or months—setting up infrastructure, building connectors, configuring monitoring, and hardening security before an agent ever reaches production.

QBricks abstracts much of that groundwork.

By running natively on the Databricks Data Intelligence Platform, the accelerator allows development teams to focus on agent logic and business outcomes, rather than plumbing. The result, according to Qubika, is dramatically reduced time to value for intelligent agent initiatives.

Built for Enterprise Reality, Not Demos

Unlike many low-code or no-code agent builders that prioritize ease over governance, QBricks is explicitly designed for regulated, enterprise-grade environments.

Key benefits include:

• Secure and compliant by default, adhering to SOC 2, GDPR, and ISO 27001 standards

• Enterprise-grade data privacy, with all data encrypted, access-controlled, and fully contained within the customer’s cloud or preferred infrastructure

• Native Databricks integration, supporting deployment across any major enterprise cloud setup

• No vendor lock-in, with agents delivered as reusable, standalone code that can run on mainstream agent orchestrators outside of Qubika

That last point is especially notable in a market increasingly wary of proprietary AI platforms that are easy to start—but hard to leave.

A Full Agent Lifecycle, in One Place

QBricks isn’t just an infrastructure shortcut. It provides a production-ready agent ecosystem with tooling designed to support long-term operation, not just initial deployment.

Core capabilities include:

• A library of pre-built agents and workflows

• A visual agent workflow builder

• An evaluation framework to test and compare agent performance

• End-to-end observability dashboards for monitoring behavior, performance, and reliability

The platform also includes a curated library of agent templates covering common enterprise use cases such as retrieval-augmented generation (RAG) systems, translation workflows, and API-driven automations—patterns many teams are already building from scratch.

Why Databricks Matters Here

QBricks is built using Databricks Lakebase, Vector Search, and GraphFrames, tying agent behavior directly to the same data platforms enterprises already rely on for analytics and machine learning.

That alignment reflects a growing trend in enterprise AI: agents are no longer standalone tools—they’re becoming extensions of the data platform itself.

Databricks, which now serves more than 20,000 organizations, has been positioning its platform as the foundation for analytics, AI applications, and agent-based systems. QBricks effectively layers enterprise-ready agent acceleration on top of that foundation.

Differentiation in a Crowded Agent Market

The AI agent ecosystem is rapidly filling with tools promising faster builds and easier workflows. According to Sebastian Diaz, SVP of Data & AI at Qubika, QBricks’ differentiation comes down to data-native design and portability.

“The key differentiator of QBricks compared to other low-code/no-code AI workflow builders is that it has native data integration with data platforms and external data sources,” Diaz said. “We ensure that the agents developed are fully portable and our clients can continue to manage, deploy, and evolve them completely independently.”

That emphasis on portability and independence directly addresses a growing enterprise concern: how to scale AI initiatives without surrendering architectural control.

The Bigger Signal for Enterprise AI

QBricks’ launch reflects a broader shift in enterprise AI adoption. As organizations move beyond pilots, they’re demanding platforms that deliver:

• Governance and compliance out of the box

• Deep integration with existing data estates

• Observability and evaluation at scale

• Freedom from vendor lock-in

In that context, accelerators like QBricks are becoming less about speed alone—and more about making AI operationally sustainable.

For enterprises building intelligent agents that must live inside complex, regulated environments, that sustainability may matter more than novelty.

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