Introduction​

In 2025, Simtel.AI found itself building two very different products that quietly shaped each other: Neonyte (www.neonyte.com) and Axon OS (www.axonos.ai). Neonyte, an e-commerce marketplace on the Open Network for Digital Commerce (ONDC), exposed the realities of operating inside a large, distributed system—millions of sellers, inconsistent catalogs, fragmented data, and no single source of truth. Automating catalog cleanup, normalization, and deduplication quickly became less of an engineering task and more of a systems problem. Alongside this, we were also generating large volumes of marketing content—images, video, and audio—adding another layer of long-running, compute-heavy workflows to the mix.

That work planted the seed for Axon OS. As AI automation platforms matured, it became clear that existing tools struggled with real-world demands: workflows broke at scale, orchestration was brittle, and speed degraded as complexity grew—especially once AI, media processing, and external systems were combined. What started as internal tooling to manage Neonyte’s catalog and content chaos evolved into a broader insight: AI workflows needed to be built for production, not demos. Axon OS was created to close that gap, treating scale, speed, and orchestration as first-class concerns.

Neonyte.com​

What Worked​

Building Neonyte as a buyer-side app on the Open Network for Digital Commerce (ONDC) flipped the usual marketplace playbook. Supply came pre-built. Millions of sellers were already on the network, which meant Neonyte could focus on demand, discovery, and user experience instead of onboarding merchants one by one. The protocol unlocked reach without ownership and scale without lock-in. For buyers, this translated into real choice; for sellers, visibility without surrendering control. The network did what platforms usually hoard—distribution.

The Challenges​

The hard part was everything the protocol doesn’t solve. Catalog management emerged as the sharpest edge: the same product appeared multiple times, with different names, images, units, and prices. Deduplication wasn’t a cleanup task—it was a core product problem. Catalog quality varied wildly, making search relevance and trust fragile. Pricing added another layer of complexity, with inconsistent MRP, discounts, and regional variance across sellers. In a distributed marketplace, there is no single source of truth—only competing versions of it. Building a usable experience meant constantly reconciling that reality.

Axon OS: Building AI Workflows for the Real World​

Axon OS didn’t start as a product idea.
It started as friction.

While working on Neonyte, we had to automate workflows that were messy by nature—catalog ingestion, enrichment, deduplication, validation, retries, rollbacks. These workflows were long-running and failure-prone. AI helped in parts, but the systems around it didn’t keep up.

Most AI automation tools assumed a clean world.
Small inputs.
Short-lived tasks.
Straight lines from start to finish.

That wasn’t our world.

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The First Attempt​

The first serious automation was built on n8n.

At first, it felt right. It was visual, flexible, and fast to prototype. For simple flows, it worked well enough. We shipped quickly and learned fast.

But as workflows grew heavier and more complex, the cracks showed.

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Where It Broke​

Complex automations needed constant workarounds.
State management was fragile.
Retries were coarse and unsafe.

Video and audio processing couldn’t run inside n8n at all. Those steps had to live in separate services, exposed as custom APIs, then stitched back into the workflow. What should have been one system became several loosely connected ones.

Each workaround solved a problem.
Together, they created a bigger one.

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Patchwork Architecture​

Over time, n8n stopped being the system and became the diagram.

Business logic lived in APIs.
Heavy computation lived elsewhere.
n8n just connected pieces it didn’t control.

Debugging meant jumping across tools.
Scaling meant adding more patches.

It worked—until it didn’t.

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The Lesson​

n8n wasn’t the wrong tool.
It just wasn’t built for production-grade AI orchestration.

When orchestration can’t own execution, complexity leaks everywhere. That insight became the foundation for Axon OS.

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The Core Problems​

As we looked closer, AI workflows failed in three predictable places.

Scale broke first. As data grew, workflows slowed or collapsed.
Speed followed. Latency compounded across steps until pipelines became unusable.
Orchestration failed last. Conditional logic, retries, human approvals, and state tracking were brittle or missing.

These weren’t edge cases.
They were the default.

Axon OS was designed with these failures in mind.

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Axon OS: Why It Works​

Axon OS works because it was designed as a system, not a collection of features. Every architectural choice came from production failures we had already experienced—especially while trying to scale AI-driven workflows beyond prototypes.

At the core of Axon OS is a template-based JSON schema for AI-native code generation. Instead of prompting LLMs with free-form instructions, workflows are defined using structured templates with explicit inputs, outputs, constraints, and validation rules. This gives AI just enough freedom to generate code, while keeping execution predictable. The result is repeatability without killing flexibility—something prompt-based systems consistently struggle with.

On top of this sits a drag-and-drop workflow UI, but the UI is not the system of record. It’s a projection of the underlying execution graph. Workflows can be authored visually, programmatically, or generated by AI itself without drifting out of sync. The UI helps humans reason about complexity; it doesn’t pretend the complexity isn’t there.

Under the hood, Axon OS is fully container-native. Every execution step runs inside isolated containers orchestrated by Kubernetes. This separation keeps workflow logic independent of compute concerns. Heavy workloads—video, audio, data processing, AI inference—run where they belong, without leaking operational complexity back into the orchestration layer.

Security was treated as a first-class constraint, not a feature add-on. Axon OS follows a zero-trust architecture by default. Credentials are never embedded in workflows or containers. Secrets are encrypted at rest, encrypted in transit, and ultimately stored in a secure vault. Execution uses short-lived tokens that expire automatically, minimizing blast radius even in failure scenarios. Trust is scoped, temporary, and explicit.

Orchestration is powered by a dedicated scheduler and worker system. The scheduler understands dependencies, parallelism, retries, and timeouts. Workers are stateless, disposable, and horizontally scalable. This makes long-running and high-volume workflows stable by default—not through retries layered on top of fragile systems.

At the center is the automated workflow execution engine. Inputs and outputs are resolved dynamically at runtime. Each node declares what it needs and what it produces; the engine handles wiring, propagation, and validation. This eliminated entire classes of glue code that previously lived in APIs, callbacks, and scripts.

Because state is tracked centrally and explicitly, workflows can pause, resume, replay, or partially re-execute without guesswork. Failures don’t force full restarts. Human approvals fit naturally into the flow. Observability is built in, not bolted on later.

Axon OS works because it treats AI as one component in a larger system—powerful, probabilistic, and unreliable on its own. By giving AI structure, boundaries, security, and orchestration, the system absorbs uncertainty and turns it into something usable.

Not faster demos.
But durable systems.

Why Stay Bootstrapped in the AI Era​

In an age where AI startups raise millions on potential alone, staying bootstrapped looks counterintuitive. For us, it wasn’t hesitation—it was intent. AI moves fast, but hype moves faster, and capital often forces conclusions before systems have faced reality. Bootstrapping bought us time to let things break, learn from them, and rebuild without a narrative deadline.

Being bootstrapped kept us anchored to real usage instead of demos. If something didn’t scale, didn’t recover cleanly, or couldn’t explain its own failure, it simply didn’t ship. Revenue became the feedback loop. That discipline mattered more in AI, where impressive prototypes routinely collapse under production load.

It also preserved alignment. Without external pressure, we could choose reliability over growth, infrastructure over features, and correctness over novelty. Axon OS became shaped by operational truth, not pitch decks. Decisions stayed close to users and failures stayed visible.

Bootstrapping isn’t about rejecting capital forever. It’s about optionality. By staying independent early, we kept control over pace, architecture, and direction. In an AI era defined by noise, patience turned out to be a competitive advantage.

How AI Helped Us Build the Platform​

AI didn’t replace engineering at Simtel.AI—it removed friction. Through Microsoft for Startups, we had effectively unlimited access to Copilot, and that changed the tempo of building entirely. Iterations became extremely fast. Ideas moved from thought to code in minutes. The cost of trying—and discarding—approaches dropped close to zero.

This speed mattered because we weren’t just writing features. We were testing architectures. Frameworks. Prompting techniques. Failure modes. Copilot handled the mechanical work while we focused on structure and intent. Engineers stopped worrying about syntax and started spending time on system boundaries and contracts.

A key accelerator was our JSON-driven, template-based architecture. Because workflows, prompts, and code generation were schema-first, we could rapidly experiment with different prompting strategies and execution patterns without rewriting the system. AI fit naturally into this model—it generated code within constraints, not around them. That kept iteration fast without letting complexity leak.

Compute support from Microsoft, NVIDIA, and AWS closed the loop. We could run real workloads, test failures at scale, and push the system hard without hesitation. The result wasn’t AI-built software—it was human-designed systems, accelerated by AI to a pace that would have been unrealistic just a year earlier.

Looking Ahead to 2026​

By 2026, the focus shifts from building to proving. The core expectation is product–market fit—not as a declaration, but as a signal. Fewer conversations about what Axon OS could be, and more clarity around what it is consistently chosen for. Usage patterns, repeat deployments, and workflows that refuse to be replaced will matter more than feature velocity.

Sales will stay deliberate. The strategy isn’t broad outbound or hype-led adoption, but narrow, high-conviction entry points. Teams with real orchestration pain. Systems already buckling under scale, speed, or reliability issues. Early sales won’t be about volume; they’ll be about learning where the product holds strongest and where it doesn’t belong.

Revenue, at first, will trickle—not surge. That’s intentional. Early revenue is less about numbers and more about signal: who pays, why they stay, and what they rely on when things go wrong. If 2025 was about building something dependable, 2026 is about discovering where that dependability turns into a business.

What This Means for the Industry​

We see this as a major step toward AI-native orchestration platforms. By aligning human-readable definitions with machine-executable workflows, we reduce friction, improve collaboration, and unlock entirely new possibilities for autonomous applications.

At Simtel.ai, we’re not just building tools — we’re building the language of the future for humans and machines to co-create software.

if you are interested, do checkout https://www.axonos.ai and use the free account to test your ideas!

What is creative destruction?​

Creative destruction is an economic concept describing the process through which new innovations replace outdated technologies, products or business models.
The term explains how progress often requires dismantling existing structures to make space for more efficient and productive ones.
As new ideas emerge, they disrupt incumbents, reallocate resources and reshape industries.
Though disruptive in the short term, this cycle is essential for long-term economic growth and rising living standards.
Creative destruction highlights that innovation is both constructive and destabilizing, forming the core mechanism of sustained economic development.

The term creative destruction was originally coined by the German economist Werner Sombart.
It was later popularized and given its modern economic meaning by Joseph Schumpeter in his 1942 work Capitalism, Socialism and Democracy.

The 2025 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel was awarded to Joel Mokyr, Philippe Aghion and Peter Howitt “for having explained innovation-driven economic growth”. oai_citation:0‡NobelPrize.org

• Joel Mokyr was recognized “for having identified the prerequisites for sustained growth through technological progress.” oai_citation:1‡NobelPrize.org
• Philippe Aghion and Peter Howitt were awarded “for the theory of sustained growth through creative destruction.” oai_citation:2‡NobelPrize.org
  Their collective work shows that economic growth is not automatic: it depends on innovation, competition, and the replacement of outdated systems. oai_citation:3‡reuters.com

AI Innovation​

AxonOS embodies the principle of creative destruction—the idea that progress emerges not from preserving the old, but from continually replacing it with something better. Rooted in the Nobel-recognized economic theory, this philosophy shapes how AxonOS is designed, built, and evolved. Rather than accumulating technical debt over time, the platform is architected to adapt, improve, and reinvent itself with each iteration.

At its core, AxonOS treats workflows, nodes, and automations as modular, versioned entities. Each unit can be upgraded, deprecated, or replaced independently—without disrupting the overall system. This modular design ensures that innovation can happen continuously and safely. New layers—such as AI-native nodes, agentic workflows, and real-time orchestration—are introduced to supersede older constructs, while legacy components are phased out once superior alternatives emerge.

This deliberate cycle of renewal ensures that AxonOS keeps advancing in performance, reliability, and developer experience. By fostering internal competition of ideas, the platform encourages multiple approaches to the same problem, promoting only the best-performing ones. This built-in Darwinism keeps the architecture agile and prevents stagnation, freeing engineering teams from maintaining outdated or inefficient code.

In practice, AxonOS enables users to build and evolve AI-powered workflows at an unprecedented pace. Its modular nodes can be assembled to create automations, intelligent agents, or data pipelines. Developers can prototype rapidly, compare competing designs, and retain only the versions that deliver optimal results. Through built-in versioning, workflows evolve naturally—improving without losing their historical context.

AxonOS’s AI-native components further streamline integration with models, embeddings, decision logic, and external actions. Because each module is swappable, users can upgrade or replace specific parts of a system without rewriting entire workflows. This flexibility encourages experimentation: new variants can be tested, measured, and adopted quickly based on data-driven performance insights.

Behind the scenes, AxonOS abstracts away the operational overhead of deployment, scaling, and orchestration, allowing users to focus entirely on creative problem-solving. Integrated monitoring and analytics tools help identify bottlenecks and continuously refine automations.

In essence, AxonOS transforms the theory of creative destruction into an operational reality. It turns innovation into an ongoing cycle—where every idea can be created, tested, improved, and replaced. By doing so, AxonOS not only evolves as a platform but also empowers its users to evolve alongside it—building systems that are as dynamic and adaptable as the ideas that power them.

Creative destruction guides AxonOS by shaping both how the platform is built and how users innovate on it.​

As a principle, it emphasizes that progress comes from replacing outdated systems with superior ones. AxonOS adopts this by designing every component—nodes, workflows, models and integrations—to be modular, swappable and upgradable. Old implementations are not preserved indefinitely; they are intentionally retired when better solutions emerge. This keeps the platform lean, adaptable and focused on continuous improvement. For users, AxonOS becomes a tool for innovation because it enables rapid experimentation. Multiple workflow versions can be created, compared and iterated without fear of breaking the system. Underperforming ideas can be discarded, and stronger ones take their place—mirroring the cycle of creative destruction. As a result, AxonOS supports a dynamic environment where innovation is not a one-time event but an ongoing process. It empowers users to build, test, refine and evolve ideas quickly, turning creative destruction into a practical engine for innovation.

Axon OS Workflow Execution Engine​

A vivid example of creative destruction within AxonOS comes from the evolution of its workflow execution engine—shaped through a series of deliberate experiments and architectural competitions. Rather than committing early to a single design, the AxonOS team explored multiple parallel approaches, each representing a different philosophy of orchestration and performance.

One prototype used a pub/sub model, where every node emitted events into a lightweight broker. This approach offered highly decoupled execution and fine-grained scalability. In parallel, an event-driven executor was developed to trigger node execution purely based on state transitions, minimizing orchestration overhead and improving responsiveness.

To evaluate runtime efficiency and isolation, AxonOS engineers compared in-memory execution, prized for its speed but limited in isolation guarantees, against a containerized model that offered stronger sandboxing at the cost of startup latency. For data transfer between nodes, the team tested two paradigms: low-latency in-memory transfers (similar to XCom) for lightweight pipelines, and disk-based handoffs for large payloads or cross-container communication.

Each approach was rigorously benchmarked across real-world workloads—spanning branching graphs, multi-model orchestration, and large embedding transfers. The outcome revealed that no single model excelled universally. Instead, a hybrid architecture emerged as the optimal solution: • Event-driven triggers for adaptive orchestration • In-memory transfers for small artifacts • Disk-backed spillover for large datasets • Containerized isolation for untrusted or resource-intensive nodes

This hybrid executor combined the best of all experiments, replacing the earlier monolithic system with a modular, high-performance architecture. The process exemplified AxonOS’s philosophy of creative destruction: systematically testing, comparing, and replacing inferior designs with superior ones. Through this disciplined cycle of experimentation and renewal, AxonOS continuously strengthens its foundation while maintaining the agility to evolve with future demands.

What This Means for the Industry​

We see this as a major step toward AI-native orchestration platforms. By aligning human-readable definitions with machine-executable workflows, we reduce friction, improve collaboration, and unlock entirely new possibilities for autonomous applications.

At Simtel.ai, we’re not just building tools — we’re building the language of the future for humans and machines to co-create software.

if you are interested, do checkout https://www.axonos.ai and use the free account to test your ideas!

For decades, business schools and boardrooms alike have leaned on the timeless 4Ps of marketing—Product, Price, Place, and Promotion—as the foundation for strategy. Yet as artificial intelligence reshapes industries, automates decision-making, and redefines competition, these four pillars are shifting in profound ways. In the AI era, what once felt like stable ground is suddenly fluid, adaptive, and in constant motion.

Product: From Features to Personalization and Speed​

In the traditional sense, a product was a bundle of features, a differentiated design, or an experience that stood apart from competitors. But AI changes this dynamic. In a world where algorithms can replicate functionality overnight, product differentiation through features becomes fragile. What matters more is not what you ship but how quickly and intelligently you evolve.

Products in the AI era are not static objects; they are living systems. They continuously learn from customer behavior, adapt in real time, and personalize experiences down to the individual level. Two companies may offer nearly identical features, but the one that uses AI to fine-tune recommendations, anticipate needs, and build trust through reliability wins. Speed of iteration, personalization depth, and the trust customers place in your system become the true differentiators.

Price: From Fixed to Fluid and Adaptive​

Pricing, once a carefully planned exercise, is now a dynamic and context-driven game. AI enables businesses to move away from fixed tags and toward adaptive, algorithmic pricing models. No longer do all customers face the same price; instead, prices may fluctuate based on demand, individual willingness to pay, or even churn risk.

Just as Uber normalized surge pricing, the broader economy is moving toward AI-driven adjustments that are invisible yet constant. Subscription tiers, pay-per-use models, and micro-segmentation experiments are tested at scale, often in real time. In this new environment, price becomes less of a static decision and more of a living conversation between business and customer, mediated by AI.

Place: From Distribution Channels to Algorithmic Visibility​

Traditionally, “place” referred to distribution: the shelves your product sat on, the stores you sold through, or the digital marketplaces you occupied. In the AI world, place is about visibility inside algorithmic ecosystems.

Recommendation engines, search rankings, voice assistants, and AI agents are the new retail shelves. Being present in the right place no longer means securing physical shelf space, but instead ensuring your product or service is discoverable when AI intermediaries are guiding consumer attention. For B2B businesses, AI systems can identify hidden micro-markets, automate outreach, and even negotiate deals. In other words, place becomes algorithmically optimized omnipresence.

Promotion: From Campaigns to Contextual Conversations​

Promotion has always been about telling your story and persuading your audience. But in the AI-driven era, the very nature of communication changes. Instead of mass campaigns and broad messaging, businesses now rely on hyper-personalized, context-aware interactions.

AI allows companies to run thousands of creative experiments simultaneously, tailoring tone, message, and channel for each micro-segment of customers. Promotion becomes less about broadcasting and more about dialoguing—meeting the customer where they are, with content that resonates at the exact moment of need. While AI handles the scale and personalization, human marketers still matter deeply: trust, empathy, and authenticity remain irreplaceable.

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AI-as-a-service

Software-as-a-Service (SaaS) defined the last two decades of enterprise technology. But in the age of artificial intelligence, SaaS feels increasingly outdated. What matters now is AI-as-a-Service (AIaaS)—platforms that deliver intelligence, not just software, through APIs, agents, and adaptive workflows. Marketing in this new landscape does not follow the same rules as SaaS; instead, it is being reshaped at every level by AI itself. Let’s examine how the classic 4Ps—Product, Price, Place, and Promotion—transform in the AIaaS world.

Product: Intelligence That Evolves in Real Time​

In AIaaS, the product is not a static bundle of code or features. It is a living system of models, data, and adaptive capabilities. Competitors can replicate surface-level functionality quickly, but what cannot be cloned is the depth of personalization, the proprietary data pipelines, and the speed of iteration.

Here, differentiation comes from three key dimensions:

1. Personalization – AIaaS must shape its responses, recommendations, or workflows uniquely for each user or enterprise.
2. Data moats – proprietary datasets and fine-tuned models that create defensibility.
3. Trust and governance – customers will choose providers that are transparent about bias, reliability, and security.

The product in AIaaS is not merely “software delivered via the cloud.” It is intelligence delivered continuously, with trust and adaptability as the defining features.

Price: Dynamic, Usage-Based, and Value-Linked​

Pricing in AIaaS cannot remain fixed or static. Instead, it naturally evolves toward dynamic, usage-based models that scale with consumption. Whether it is per API call, per token, or per agent-run, customers expect to pay in proportion to value delivered.

AI makes this even more fluid. Providers can adjust prices in real time based on:

• Workload intensity (e.g., higher rates for GPU-heavy jobs).
• Customer value (outcome-based pricing, tied to business KPIs).
• Retention risk (AI dynamically offering discounts to prevent churn).

This means no two customers may pay the same rate. Pricing engines continuously optimize, just like ad auctions. For AIaaS, price becomes a conversation mediated by algorithms, where fairness and perceived value matter as much as revenue optimization.

Place: Distribution Through AI Ecosystems​

In the AIaaS world, “place” is not about shelves, storefronts, or even just marketplace listings. It is about meeting people where they actively explore, learn, and form opinions about AI.

Discovery for AIaaS happens in social outlets—LinkedIn threads, X/Twitter debates, Discord and Slack communities, YouTube explainers, Substack essays, and niche AI newsletters. These platforms have become the modern “storefronts,” where people test ideas, seek recommendations, and validate credibility before engaging with a product.

Distribution is no longer only about embedding inside ecosystems; it is about being present in the conversations where trust and authority are built. Word-of-mouth has shifted into digital-first thought leadership, where every post, demo video, or open-source contribution becomes a channel for discovery.

For AIaaS providers, this means the strategy is two-fold:

1. Be visible in social outlets where communities gather to learn and evaluate AI tools.
2. Enable self-serve exploration with freemium trials, open APIs, and sandbox environments that lower the barrier to adoption.

In short, place in the AIaaS era is less about traditional distribution and more about social discoverability and community-driven validation.

Promotion: Intelligent Conversations Over Static Campaigns​

Traditional SaaS relied on content marketing, webinars, and automated funnels. AIaaS goes further—promotion becomes contextual, conversational, and AI-powered itself.

• AI can generate personalized messaging for every prospect, adapting tone and value proposition dynamically.
• Real-time experimentation allows thousands of ad variations to run simultaneously, each tuned to micro-segments.
• Conversational agents handle the majority of the sales cycle—educating, demoing, and even negotiating—before handing off to humans for trust-based closures.

Promotion in AIaaS is not about broadcasting campaigns; it is about orchestrating ongoing, intelligent, trust-driven dialogues with customers at scale.

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The Future of Marketing in AI-as-a-Service​

The AIaaS era does not kill the 4Ps—it redefines them:

• Product becomes adaptive intelligence, fueled by personalization, data moats, and trust.
• Price becomes dynamic, usage-based, and tied to outcomes.
• Place becomes distribution through AI ecosystems and intermediaries.
• Promotion becomes intelligent, personalized conversations rather than one-way campaigns.

AIaaS is not just “SaaS with smarter features.” It is a new category where marketing, like the product itself, must be adaptive, intelligent, and continuous. Companies that understand this shift will not just market AI services—they will market with AI, through AI, and for AI.

In short, the 4Ps in the AI world are not disappearing—they are transforming. Product becomes about personalization and speed. Price becomes adaptive and algorithmic. Place becomes about algorithmic visibility. Promotion evolves into contextual, conversational marketing. Businesses that recognize and act on these shifts will find themselves not just surviving in the AI age, but shaping it.