--Originally published on Apparel Brands--

Through this strategic partnership, product ranges will be created for men, women, and youth, and will be rolled out through key retail partners. Design and collaboration between 

 is already underway, ensuring the first collections reflect Castore’s engineering ethos and Apparel Brands’ category expertise from day one.

Apparel Brands will leverage its full-service brand platform, encompassing design innovation, product development, and international distribution to bring the new Castore socks and underwear range to market. The new range will incorporate Castore’s signature precision and attention to detail.

 Chief Commercial Officer of Castore said:

Castore, the premium performance sportwear brand and Apparel Brands have announced a five-year partnership commencing in 2026, with Apparel Brands taking on the license to develop, manufacture and distribute Castore-branded socks and underwear across the UK and Europe.

Castore and Apparel Brands Announce Strategic Partnership

In our last post we looked at LLM hallucinations - when a language model confidently gives false answers. Here we will examine a different class of risk: when a customer-facing assistant strays from its core purpose, engaging in irrelevant or risky topics. This is a question of 

 rather than model decay — it threatens brand safety, wastes compute, and undermines trust.

Amazon’s Rufus AI shopping assistant launched last year offers a cautionary example of where this risk can lead to issues . Reports surfaced of users coaxing Rufus into political discussions or even code generation — exploits likely enabled by using a generalist LLM without sufficiently strict topic constraints. This story underscored how even technically sophisticated and capable systems can fall down if consumer usage is left unchecked.

The crux of the issue here is not the AI getting its facts wrong, it’s that it might offer a beautifully reasoned answer to a question you never intended it to entertain.

But this risk is not insurmountable. With a modest investment in intent control, brands can preserve the fluid, helpful nature of an LLM-powered assistant while keeping it squarely in your domain. Below are a some examples of easy-to-implement guardrails and industry best practices for aligning an assistant to your business’s goals.

Core guardrail strategies for mitigating misalignment risk

Before sending a user’s question into your LLM pipeline, run a quick semantic check: compare the embedding of the prompt against a “domain of intent” space (for example, product requests, comparisons, shopping guidance). If the similarity score is too low, politely refuse or redirect:

This blocks casual attempts to hijack your assistant into unrelated domains, conserves compute, and sets expectations clearly for users.

Even with a valid prompt, the model might drift in its response. After generation, run a second check: score the response’s semantic affinity to your allowed domain. If it falls below threshold, suppress or replace it with a fallback:

Alternatively, re-prompt or regenerate with stricter constraints. This gate ensures your assistant cannot leak into unwanted topics, even when the model is tempted.

Complementary best practices to reinforce safety

While input/output gating forms the backbone, a few additional layers greatly strengthen your protection:

A. Scoped system prompts and refusal policies

Embed a clear policy in your initial system message: define the assistant’s identity, allowed scope, and a refusal strategy for disallowed topics. This “soft boundary” helps the model self-regulate before your downstream checks catch issues. But on its own it is not enough — it must be paired with runtime checks.

B. Rule-based topic filters and deny lists

Maintain a list of forbidden content types (e.g. coding instructions, legal advice, politics, violence). Use keyword, pattern, or semantic filters on both user inputs and candidate outputs. Many modern guardrail toolkits support this “deny list” enforcement. (These filters help with fast, deterministic blocking.)

 provide programmable, processable rails around your LLM. They let you embed domain constraints, define conversation flows, and monitor compliance in real time. (For example, LlamaFirewall has been proposed as a security guardrail layer designed to detect prompt injections or prevent unsafe code generation.)

Domain misalignment is a subtle but pervasive threat — it quietly erodes brand trust, burdens systems, and confuses users - but it can be tamed from day one with layered intent controls. The strategies above are already in practice among AI-savvy organisations. Semantic gating is lightweight; rule filters and denial lists are deterministic; and runtime guardrail frameworks formalise control.

That said, there is no perfect guardrail, stronger security often comes with lower flexibility. The goal is not to lock down the assistant into rigidity — it is to let it run freely 

. With these layered measures, you can deploy conversational assistants confidently, knowing the risk of domain leakage, off-topic distraction or brand misalignment is much reduced.

This is the latest blog in our series on key risks associated with LLMs and how to overcome them. Here we unpack the potential issues that arise when used to power consumer-facing features/applications without sufficient guardrails and a handful of simple strategies to prevent them.

Large Language Models: Consumer Facing Applications - Expansive Knowledge, Expansive Risks 

The Creator Economy’s Next Wave: Why Now Is the Moment to Build

 driven by macroeconomic factors and a loss of trust in the industry

But downturns also reset the playing field. We believe the creator economy is now entering its next major wave, and this time the opportunity is larger, deeper and more durable than before.

1. The Attention Crisis: Why Brands Are Rethinking Distribution

For the last 20 years, consumer brands have been built on paid digital advertising as a reliable way to reach audiences and drive conversion. 

, Joe pointed out that agentic AI will transform commerce by shifting it from human attention to agent-driven transactions. In this new reality, the question becomes: where does attention go? More and more, the answer is creators.

2. Community as the New Distribution Channel

Parallel to this attention crisis is a cultural one. In the midst of a loneliness epidemic, people are spending more time online, seeking belonging in smaller communities, and anchoring identity in digital spaces.

Creators form the nucleus of that movement. They're not just "influencers" sharing products anymore. They have transformed into community leaders, trusted points of access where users congregate, interact and buy.

This is showcased in the types of media users are consuming:

The top of the funnel is decentralising. Distribution no longer flows through ad platforms, it comes from communities and networks anchored by creators.

Until recently, producing professional-grade content was a privilege of scale. 

 Producing high production-quality video, editing, and design required teams and budgets

This changes the dynamics for both sides:

In spite of all that, most creators (particularly small and mid-tier players) still have disconnected and largely ineffective infrastructures.

Creators don’t fit a standard ICP, they differ in income, vertical, and ambition. That makes sales and conversion tough, but also leaves the market wide open for those who crack it. Europe is even trickier, with a more fragmented and unevenly developed landscape. Winning here will come down to exceptional founders, the ones able to navigate complexity and turn it into an advantage!

5. Beyond Generative: The Coming Wave of Agentic AI

If generative AI decreased the cost of creation, agentic AI will alter the very nature of creation and operations altogether.

Agentic systems combine planning, tool use, and memory to act, not just generate. Applied to the creator economy, this means:

This next generation of companies will offer creators studio leverage. On the brand side, agents will also change how influencer campaigns get run: discovery, outreach, negotiation, reporting.

 But AI cannot take away the human layer. 

Creators, especially larger ones, still want managers and agents to help them steer careers, be creatively aligned, and not feel alone.

 Founders entering this space need to consider who their product serves, the brand, the creator, or the manager,  and how automation and trust can coexist.

The creator economy has already seen multiple waves: YouTube ad networks, Instagram influencers, Patreon, link-in-bio tools. Each added value but had limitations. This time feels different because of two forces converging:

From a venture perspective, the focus has shifted. Early capital went into platforms. Today, the real opportunity lies in creator enablement: workflow consolidation, AI-powered agency infrastructure, agentic automation, scalable monetisation models and community infrastructure.

The space is still early. Fragmentation, lack of standardisation, and sales complexity remain. But those are exactly the kinds of challenges that reward ambitious founders. If you’re building in this space, we’d love to hear from you.

Camille Templé explores the next wave of growth and opportunity in the creator economy, and why now is the pivotal moment to build tools and infrastructure for creators. 

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News

Castore and Apparel Brands Announce Strategic Partnership

Insight

Large Language Models: Consumer Facing Applications - Expansive Knowledge, Expansive Risks

Insight

The Creator Economy’s Next Wave: Why Now Is the Moment to Build