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What’s up everyone, today we have the pleasure of sitting down with Steven Aldrich, Co-CEO and Co-Founder at Ragnarok NYC.
Summary: Like the aftermath of Ragnarök according to Norse mythology, the martech world is emerging stronger, more focused, and ripe with potential. Rather than being overwhelmed by the chaos, marketers should use this time to rethink how to evaluate technology choices through the lens of business value. Prioritize platforms that drive real-world impact and avoid getting lured by features that blaze brightly for a moment, only to be swallowed by the tide of irrelevance.
Jump to a Section
- The Evolution of Martech and the Impact of Consolidation
- Blending Automation with Human Spark for Smarter Martech Strategies
- The Value of Data Science in Martech Optimization
- A Nuanced Approach to Evaluating Martech Vendors
- Uncover the Martech You Actually Need with a Bottom-Up Approach
- What the Best Martech Implementations Prioritize
- Measuring Marketing and Martech Impact Using the BEATS Model
About Steven
- Steven’s first job out of business school was a customs broker in Colombia, before his Visa ran out and he was forced to return to the US
- He started his marketing career as a Marketing and Comms Associate at a market research firm where he discovered the wonders of HTML, email development and Adobe dreamweaver
- While continuing his full time in-house career working in email and CRM roles for different industries, Steven and his co-founder Spencer launched Ragnarok, first as a side hustle where they spent their evenings moonlighting as marketing technology consultants
- In 2017, both co-founders decided to take the leap and go all in on their agency
- Today Ragnarok is a 50+ person full service martech agency that’s helped well known brands like zapier, dropbox, asana, adobe and many more!
The Evolution of Martech and the Impact of Consolidation
When asked about the future of martech, Steven immediately highlighted the ongoing consolidation in the industry. He pointed to acquisitions like Twilio snapping up Segment and Salesforce expanding its Customer Data Platform (CDP) offerings as clear signals. According to Steven, these moves indicate that we’re in the midst of a reshuffling phase—one that will shape how martech platforms are built and used over the next decade.
However, it’s not just about merging and acquisitions. Steven sees the next wave of growth stemming from generative AI. This technology, while still in its infancy for many organizations, will soon be as fundamental as marketing automation tools were a decade ago. Platforms are experimenting with Gen AI features like automated content creation, but they’re still scratching the surface. “Right now, a marketer isn’t likely to sit down and have their AI tool write an entire creative brief,” Steven noted. “But once the tech reaches a level where it’s drafting briefs and campaign strategies, it’ll fundamentally change what marketers do day-to-day.”
He also predicts that the next few years will separate the genuine innovators from the rest. Startups focusing on AI-powered automation and advanced integrations will emerge as key players. Those that fail to embrace this trend will struggle to maintain relevance. Steven pointed to companies like Castle.io as an example—a newer entrant that has managed to make a name for itself by rethinking traditional automation and going all-in on a warehouse-first approach.
Looking ahead, Steven envisions a future where marketers become more like strategic curators rather than operators. Instead of creating every campaign element manually, marketers will outline goals and high-level structures, and let the tools figure out the rest. “Think of a platform where you set your conversion goals, outline your audience, and the tool builds the journey for you,” he explained. Some companies are testing these capabilities internally, but we’re still far from a world where it’s the norm. To reach that stage, platforms need to overcome significant technical challenges and gain marketer trust.
Ultimately, Steven believes that by the ten-year mark, the martech industry will look entirely different. The focus will shift away from basic integrations and automation to more complex AI-driven orchestration. Platforms will evolve into decision-making engines, allowing marketers to focus on strategy, creativity, and innovation, leaving the grunt work to the machines.
Key takeaway: The martech industry is undergoing a consolidation phase as it readies itself for the next wave of innovation: generative AI. Startups that embrace AI-driven automation will emerge stronger, while legacy platforms must integrate these new capabilities or risk becoming obsolete. In the next decade, marketers will transition from hands-on campaign execution to strategic oversight, as tools handle more of the complex work autonomously.
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Blending Automation with Human Spark for Smarter Martech Strategies
When it comes to AI and automation in martech, there’s a spectrum of opinions. On one end, some marketers insist that only a human can truly understand and engage their audience. On the other end, there’s a growing camp eager to hand over the repetitive tasks to machines and focus on strategy. Steven pointed out that the real value lies in finding a balance between the two extremes, especially for industries with strict compliance requirements like FinTech and health tech.
Steven used abandoned cart programs as a foundational example of automation’s role in marketing. Not long ago, these campaigns were inconsistent and cumbersome. Companies like Klaviyo and Shopify stepped in, making abandoned cart emails table stakes for eCommerce. Now, if you abandon your cart, you can almost predict when you’ll receive that follow-up email offering a discount or reminder. “It’s just expected,” Steven explained. He believes this kind of automated functionality has become the baseline for what customers and marketers alike view as the norm.
But not every industry can afford to automate at that level. With sectors like finance or healthcare, there’s a need for humans to review and validate messages for compliance. “A legal person is at the end of every review,” Steven said. “It’s frustrating and time-consuming, but the cost of sending the wrong message at the wrong time is just too high.” He sees these industries gradually adopting AI where they can—incremental optimization, message testing—but keeping a human in the loop for quality assurance.
The evolution of martech, in Steven’s view, will be about advancing beyond these early stages. He predicts that the future will bring a seamless integration where humans set high-level goals, and AI takes care of execution. The role of the marketer shifts from managing individual campaigns to curating experiences and setting strategic parameters. Some platforms are already testing these capabilities, but they’re far from ready for mainstream adoption. “Imagine a future where marketers simply set their audience, goals, and content, and the tool builds the entire journey for them,” Steven envisioned. This approach would redefine what it means to be a marketing operator, giving professionals more time to think strategically rather than tactically.
Ultimately, Steven sees the evolution of martech as an interplay between speed and quality. Some companies will succeed by automating faster, launching multiple initiatives, and iterating based on outcomes. Others will opt for a more deliberate approach, spending more time crafting the perfect message. “There isn’t one clear winner,” Steven concluded. “It’s about choosing the right tool for the job and understanding what’s at stake when the human element is minimized.”
Key takeaway: Martech’s future lies in balancing automation with human oversight. While some industries can embrace full-scale automation, others need humans in the loop to maintain compliance and quality. Marketers must choose tools that fit their strategic goals—whether that’s rapid iteration or precision crafting.
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The Value of Data Science in Martech Optimization
When asked about the role of data science in marketing operations, Steven was quick to point out the distinction between what large enterprises can achieve versus what’s feasible for smaller companies and startups. Drawing from his experience, he explained that enterprise teams often have the luxury of dedicated data scientists focused exclusively on building sophisticated models, such as propensity scoring, to optimize campaigns. “These teams enable marketers to self-serve advanced analytics like uplift modeling or conversion likelihoods, something that’s hard to replicate in smaller organizations,” Steven noted.
For instance at WordPress.com, marketers could use internal CDPs to build models that predicted the probability of a free user converting to a paid plan based on specific user behaviors. These insights allowed the team to avoid unnecessary promotional discounts for users already likely to convert—saving on margins and increasing overall revenue. Such granular targeting would be impossible for smaller teams that lack the resources or infrastructure to set up similar systems. The solution for startups, Steven suggested, may lie in tools that can “decentralize” these capabilities and make them accessible to teams without the need for in-house data scientists.
Steven echoed this challenge when talking about the use of AI in smaller martech stacks. Even though off-the-shelf tools offer some level of automated optimization, the effectiveness is still heavily reliant on the amount of data available. “The issue isn’t always the tooling,” Steven pointed out. “It’s the volume of data needed to make these systems work effectively.” AI systems that drive automated messaging or personalization require large datasets to achieve accuracy—datasets that many smaller companies simply don’t have.
This is where Steven sees potential for future advancements in AI technology. If AI tools can improve their ability to handle fewer data points with higher accuracy, it could democratize access to advanced features currently reserved for enterprises. However, Steven also emphasized that even with AI taking over routine tasks like personalized send times or triggered messaging, there’s still a need for human oversight. “AI can automate the operation, but it can’t interpret nuances or business context,” he added. This lack of contextual understanding can lead to irrelevant messaging that misses the mark, even if the timing is right.
Ultimately, Steven’s vision for martech is one where AI and humans work hand-in-hand, with AI handling the operational heavy lifting while humans provide strategic direction and context. He referenced his own experience building these systems, noting that while automation can optimize for scale, there’s always a need for human input to fine-tune the messaging, ensure relevance, and craft a truly engaging experience.
Key takeaway: The role of data science in martech is critical for building advanced models like propensity scoring and uplift modeling, but it’s a luxury that only large enterprises can typically afford. To bridge the gap, AI tools need to become more efficient with smaller datasets and incorporate more business context. Marketers should use AI to handle operational tasks while maintaining strategic oversight to ensure relevance and effectiveness.
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A Nuanced Approach to Evaluating Martech Vendors
Steven shared some valuable insights into how his team at Ragnarok approaches evaluating new martech tools, shedding light on what it means to be platform agnostic in a sea of competing technologies. While many agencies prefer to stick with a core stack, Ragnarok takes a broader view—engaging with multiple vendors to deliver tailored solutions for their clients. “At the end of the day, there’s no room for bias in our evaluations. We’ve worked with so many different tools that it’s really about understanding what each one does well and where it might fit,” Steven explained.
A case in point is one of Ragnarok’s newer partners, Insider—a company based out of Turkey, making a mark in the sophisticated eCommerce automation space. Steven highlighted how Insider combines the power of a Customer Data Platform (CDP) with advanced automation features similar to what Klaviyo offers. “It’s interesting to see such an innovative platform being built outside of the U.S., in a market that’s already so big on eCommerce,” Steven noted. Insider’s unique approach to building out-of-the-box campaigns for notifications and multi-channel strategies stood out during the evaluation, making them a strong contender for complex client use cases.
But what’s Ragnarok’s actual process for vetting these vendors? Steven shared that it starts with a checklist of fundamental capabilities. “Do you have APIs that allow me to move data in and out seamlessly? Can you handle high data volumes and complex event structures?” These aren’t just nice-to-haves—they’re essential criteria for any platform that wants to partner with Ragnarok. Beyond technical requirements, they also look at the vendor’s long-term vision and industry focus. “Are you just looking to compete on feature parity, or are you truly solving a problem for a specific market segment?” Steven emphasized the importance of understanding where a tool is positioned and whether it can add incremental value to their clients’ tech stacks.
Steven also pointed out the nuanced decision-making involved in distinguishing between similar-looking tools. He referenced tools like Customer.io and Klaviyo, both powerful in their own right, but serving different segments and use cases. “At a surface level, they can seem identical, but you dig deeper and realize that Customer.io has built this hybrid B2B-B2C offering that’s incredibly effective in SaaS marketing,” he explained. It’s these subtleties that often define whether a vendor makes it onto their preferred list or not.
Ultimately, Ragnarok’s evaluation isn’t just about features—it’s about vision, specialization, and staying power. This strategic lens helps them advise martech buying committees with confidence, guiding them through a maze of competing vendors. As Steven put it, “You need to look beyond the sales pitch and really understand where the vendor is heading. That’s how we can deliver the best possible solutions to our clients, whether they’re in eCommerce, SaaS, or mobile-first markets.”
Key takeaway: Evaluating martech vendors goes beyond ticking off feature checklists. Agencies like Ragnarok look at a vendor’s API capabilities, scalability, industry specialization, and long-term vision to decide whether they’re a good fit. Understanding these nuances helps navigate the crowded martech landscape, enabling better decisions that align with specific client needs and strategic goals.
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How to Build Better Martech by Aligning Product Roadmaps with Real-World Applications
When asked about his involvement with martech vendors and their roadmaps, Steven shared his candid approach. He’s known for being forthright in these discussions—whether it’s critiquing new features or suggesting product enhancements. This directness hasn’t always been easy on the relationships, but it’s one of the reasons why companies like Klaviyo, Segment, and Braze have sought his input. “I may have burned a few bridges by being too honest,” Steven admitted. “But that’s how you get to the heart of what’s really needed.”
His role as a strategic advisor extends beyond just his clients. He frequently engages with product teams to provide feedback, often influencing their development paths. Take Klaviyo’s CDP or Iterable’s recent push into mobile—it’s all been shaped in part by Steven’s insights. He’s had a hand in shaping the trajectory of these products, ensuring they align not just with enterprise needs but also with real-world use cases. “It’s one thing to have a vision for a product, but another to make sure it’s actually solving a tangible problem for end-users,” Steven emphasized.
Steven’s unique position allows him to bridge two worlds: he’s deeply involved in advising clients on martech decisions, while also offering guidance to vendors on product strategy. This dual perspective means he often finds himself playing matchmaker between companies that might not realize they’re solving complementary problems. He shared that he’s facilitated conversations where competing vendors realized they could actually partner to create a stronger value proposition for their shared customers. “Sometimes it’s about seeing the bigger picture—where one tool’s strength complements another’s,” he noted.
A recent example involves connecting Segment with other data orchestration tools to extend their capabilities without stepping on the toes of partners like Braze or Iterable. His understanding of each vendor’s strengths helps him guide these companies toward mutual growth. “Vendors are usually focused on their own roadmaps and customer feedback, but being able to see across multiple products and use cases allows me to help them identify gaps or opportunities they might miss,” Steven explained.
Beyond feature discussions and roadmap reviews, Steven also plays a role in user experience improvements. He’s shared feedback on UI/UX elements like terminology and usability, which can often be overlooked but have a significant impact on user adoption. “Sometimes it’s the small things, like the way a button is labeled or how campaigns are organized, that make or break the user experience,” he shared. His feedback may not always be acted on immediately, but he believes it contributes to long-term product refinement.
Key takeaway: Martech advisors like Steven play a crucial role in bridging the gap between what vendors build and what customers need. By offering candid feedback and leveraging his experience across multiple platforms, he ensures that product roadmaps are aligned with real-world applications. The result? Better, more user-centric tools that deliver real value to marketers and their customers.
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Uncover the Martech You Actually Need with a Bottom-Up Approach
Steven discussed a unique framework he’s developed for evaluating what martech tools are needed for specific use cases and companies, something he calls the “bottoms-up martech analysis.” This approach focuses entirely on understanding and prioritizing a company’s use cases, rather than being swayed by shiny new features or vendor pitches. “You shouldn’t be looking at features and thinking, ‘Oh, that looks great, let me buy that,’” Steven explained. “You’ll end up buying capabilities you never activate or never get value from.”
The process starts with defining a set of use cases that each business wants to address—think of scenarios like abandoned cart recovery, re-engagement campaigns, or form completion follow-ups. Most brands have anywhere from 50 to 80 use cases, each with its own unique nuances and value to the organization. Steven emphasizes grounding any evaluation in these real-world scenarios. When issuing RFPs or inviting vendors to pitch, his team lays out these use cases and asks vendors to demonstrate how they would solve each one. The goal is to steer the conversation away from flashy features and focus on tangible results.
To further refine the process, Steven’s team uses a scoring model to compare vendors. While scoring models are standard in vendor evaluations, Steven’s method digs deeper. It considers not just whether a tool has a particular feature, but how well that feature solves the use case in question. For example, if evaluating email delivery capabilities, it’s not enough to know that both Braze and Iterable can send emails. His team looks at the email delivery provider (e.g., SendGrid, Mailgun, or MessageBird) and how well each platform integrates with custom MTAs, should that be a requirement.
This methodology also applies to more complex scenarios like multi-channel orchestration or call center integrations. For instance, Steven shared an example of evaluating a vendor’s ability to route calls through a CDP or data warehouse. The business value of real-time response rates can’t be understated—something that might not show up as a priority if only looking at feature lists. “Milliseconds matter when you’re trying to route calls and pull back a customer’s profile,” he pointed out. “A second may seem trivial, but in a call center context, it’s critical.”
Vendors like Tealium have responded to these evolving use cases by doubling down on real-time capabilities, emphasizing millisecond response times. What might have been acceptable as a 15-second delay a few years ago is now expected to happen almost instantaneously. It’s a clear example of how the industry shifts in response to business demands, and why use-case-driven evaluations are crucial. Tools that might look equal at a high level can reveal stark differences when measured against specific, real-world needs.
Key takeaway: Effective martech shopping exercises should be rooted in business use cases, not feature checklists. By grounding vendor comparisons in real-world scenarios, companies can identify which tools will genuinely add value and avoid getting distracted by capabilities they’ll never fully utilize. This approach leads to better alignment between technology and business objectives, ensuring that the chosen platforms deliver meaningful impact.
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What the Best Martech Implementations Prioritize
When it comes to evaluating and implementing martech solutions, Steven offered a clear perspective: start small, focus on value, and avoid biting off more than you can chew. He emphasized that the best implementations prioritize getting a first use case to market within 90 days. “You’re probably not tackling your most complex use case at that point—and that’s okay,” Steven noted. “The goal is to drive incremental value early on.”
This pragmatic approach is rooted in the need for martech investments to pay for themselves within two years. The first year, Steven explained, might just break even due to the high costs of initial setup, agency support, and internal resources. It’s only by the second year, when the team has successfully implemented more advanced use cases, that the tool’s ROI really starts to materialize. “By the time you reach your 15th or 30th use case, you’re not just doing things better—you’re doing entirely new and interesting things that can significantly impact the business,” Steven added.
Steven cautioned against the common pitfall of chasing shiny new features or trying to implement everything at once. He’s seen many teams become paralyzed by the sheer volume of possibilities, leading to delays and a lack of focus. Instead, he suggests using OKRs (Objectives and Key Results) to anchor around a specific number of use case deployments. “You need to deploy those 15 use cases this year—make that your team’s goal,” he advised. This clarity of purpose keeps teams focused on driving value rather than getting lost in the noise of feature overload.
A crucial element in making martech investments worthwhile is understanding the true cost of ownership. “If you’re spending $100,000 on a CDP,” Steven explained, “you’re actually spending closer to $450,000 when you factor in headcount and operational costs.” To make that investment worthwhile, companies need to generate substantial returns—often targeting 10x the initial cost. The key to achieving that, Steven emphasized, is using the tool’s capabilities to unlock incremental value over time. It’s not just about having the tool; it’s about deploying it in ways that generate real revenue.
Ultimately, Steven’s position is clear: focus on what drives incremental value for your business and avoid getting distracted by features you don’t need. “You already have a great list of use cases to go down,” he pointed out. “Exhaust that list before jumping to new, unproven features. That’s how you ensure the investment is worth it.”
Key takeaway: Successful martech implementations start small, prioritize value, and avoid the temptation of over-investment in unnecessary features. Aim to get your first use case live within 90 days and use OKRs to track your progress. Keep the focus on driving incremental business value and exhaust your existing use case opportunities before pursuing new ones.
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Measuring Marketing and Martech Impact Using the BEATS Model
When asked about how to evaluate marketing’s impact effectively, Steven introduced a concept he often references—the BEATS model. This framework starts by evaluating the health of the business and then works down through various layers of measurement, such as experiments and attribution models, to understand marketing’s contribution. “It doesn’t matter how great your attribution looks if the business isn’t making money,” Steven emphasized. This statement underscores the need to connect granular metrics to overall business outcomes.
Steven acknowledged the benefits of multi-touch attribution (MTA) for tracking and understanding channel performance but cautioned against losing sight of the bigger picture. MTA can lead to overemphasizing certain metrics while neglecting others. For example, a shift in attribution could make email campaigns appear less effective, but show significant gains in social media, which might prompt teams to allocate more resources there. “You end up optimizing for specific channels rather than focusing on the overall business impact,” he noted.
To prevent this, Steven recommended using a blend of models that includes incrementality testing and marketing mix modeling (MMM). Each method offers unique insights—MTA provides granular detail, while MMM and incrementality testing can help clarify broader impact, including brand awareness and lagged effects of campaigns. He pointed out that brand impact, in particular, is a notoriously difficult metric to capture with MTA alone. “Measuring the effect of a TV ad or a major sponsorship is challenging, but that doesn’t mean it’s less valuable,” Steven explained.
The BEATS model encourages a layered approach, starting with top-line business health metrics and then narrowing down through control group testing and specific campaign analyses. Steven shared that he often advises clients to prioritize business-level outcomes over isolated campaign successes. “You need to see the total effect at the business level before diving into attribution details,” he said. This perspective helps teams avoid over-indexing on less meaningful metrics and ensures they focus on what really moves the needle.
Ultimately, Steven believes that combining various models—whether MTA, incrementality, or MMM—is essential to paint a full picture of marketing’s impact. The goal is to understand which levers genuinely drive results and align those insights with business goals. “Don’t get lost in the granularity of attribution,” he advised. “Make sure every insight ties back to the overarching business objectives.”
Key takeaway: Use a layered approach to measure marketing impact, starting with top-line business performance and then diving into granular metrics like MTA and MMM. A combination of models helps avoid over-optimization in specific channels and provides a holistic view of marketing’s effectiveness. Ensure all measurement efforts connect back to broader business outcomes for meaningful insights.
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Finding Balance Between Career, Family, and Happiness
When asked how he manages to find balance amidst his many roles—co-CEO, podcast co-host, father, amateur carpenter, and more—Steven reflected on what happiness means to him. Growing up in a blue-collar household as the first in his family to graduate college, he’s grounded by a deep appreciation for the life he’s built. “I love working with my hands, reading, and doing all these things that really expand my imagination,” he shared. For Steven, happiness isn’t about striving for something unattainable; it’s about recognizing the distance he’s traveled and the life he’s created for himself.
He draws a lot of satisfaction from his role as a father, something he always knew he wanted to be. “I always wanted to be a dad and, you know, I’m a dad. That, to me, is one of the most fulfilling things in my life,” he said, adding that he likes to be called “Papa.” He takes a lot of pride in his family life, where the chaos of two kids, two dogs, and two cats often merges with the madness of running a business. But rather than seeing it as overwhelming, Steven finds comfort in it. It’s the kind of madness that represents a life well-lived.
Steven’s view on happiness comes from a place of self-comparison rather than external benchmarks. “You always ask yourself, ‘Am I happy?’ And then you think, ‘What do other happy people look like? How do I compare?’” But ultimately, he measures his own happiness by looking at the life he has built. It’s a process of introspection and self-assessment, weighing what he envisioned for his life against what he’s achieved. “I think back to the kind of life I wanted for myself and what I’ve built for myself, and it feels like fulfillment,” Steven added.
He finds a unique sense of satisfaction in knowing that he’s the architect of his own happiness—a sentiment his father, who owned a hobby train shop, instilled in him. “The best feeling my dad had was when he’d close up shop and say, ‘I built this,’” Steven recounted. For Steven, this same sense of pride extends beyond his career to his family and home life. No matter how chaotic the day ends—with kids running around, dogs barking, and cats knocking things over—he can look around and say, “I built this.”
It’s this feeling of ownership, of being in control of his happiness, that keeps him grounded and content. Every aspect of his life—whether professional or personal—represents something he’s worked for and nurtured. At the end of the day, Steven’s balance is found not in trying to separate his roles but in appreciating the complete picture they form.
Key takeaway: True happiness comes from recognizing and valuing what you’ve built. It’s not about comparing yourself to others but about looking inward and acknowledging the life you’ve created for yourself. Happiness, like success, is self-defined and achieved through appreciating the everyday chaos that represents a life well-lived.
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Episode Recap
Just as Ragnarök marks the end of one world and the birth of another, the martech landscape is going through its own seismic transformation. The battlefield is this sort of consolidation phase that’s pushing legacy platforms to either evolve or face obsolescence as generative AI and automation rise to prominence. We’re deep in a make-or-break era, where the survivors will be those who can meld human oversight with machine efficiency, forging a future where strategic thinking replaces manual execution. But unlike Norse mythology, this is less about an epic battle of destruction and more about making smarter choices and navigating the storm with foresight and agility.
In this episode, Steven sheds light on how marketers can approach this foretold series of impending events. Stop chasing every shiny feature and being swept up by every new entrant in the market. Instead, find a way to make grounded decisions by focusing on business use cases. Martech’s most successful implementations aren’t built on crazy long lists of functionality but on a thoughtful evaluation of what will move the needle for the business. The consolidation phase may look like chaos, but it’s also an opportunity to reassess, recalibrate, and rethink technology investments.
Steven also warns against overcomplicating the measurement of marketing impact. He argues that while multi-touch attribution has its place, there’s a tendency to get lost in metrics that don’t tie back to overarching business goals. That’s why his advice is to start with the basics—top-line business performance—and then work your way down to granular models. The goal isn’t to chase perfect attribution but to understand which levers genuinely drive growth. When you ground your strategy in business fundamentals, it’s easier to see past the hype and focus on what matters.
Evaluating martech vendors should follow the same principle: avoid being dazzled by a parade of features. Instead, prioritize API capabilities, scalability, and how well a platform fits within your unique tech stack. Steven’s team at Ragnarok navigates this by using a “bottom-up” approach—building vendor evaluations from real-world use cases and scoring based on how effectively each tool can meet those needs. It’s a structured method that ensures the technology serves the business, not the other way around.
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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)
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