Everything you need to know before building an AI copywriter
So, you want to build a bespoke AI copywriter agent for your ecommerce brand? Here’s what you need to know before you start. This comprehensive guide breaks down the key features of an agentic AI copywriter, how to know when your team is ready to start building, and the core costs of building your own AI agent in-house.
With modern agents and off‑the‑shelf LLMs, suddenly, it’s enticingly do-able to prototype an AI copywriter in-house. Unfortunately, while building a prototype is an exciting start, that doesn’t mean it’s easy to build a workable agentic AI copywriter from the ground up and operate it reliably at scale.
As we’re now seeing, failure rate for in-house AI programmes remains as high as ever. Budgets are being pulled, teams are disbanding, and governance burdens are rising.
So, what makes the difference between building an in-house AI agent that succeeds – and one that fails?
Why “we’ll just build it” is so tempting
With so much hype and so little practical guidance, it’s no real surprise that a significant number of agentic AI projects never make it past the starting line. After all, it’s all too tempting for teams to dive in on building, without completing the necessary groundwork.
The working brief seems sensible: teams just want to build an AI copywriter that’s genuinely brand-aligned, SEO‑ready, and resilient across answer engines like Perplexity and ChatGPT. They’re counting on achieving speed and scale on their own clock, with the added bonus of no vendor lock‑in.
But, as the last decade has taught us, proof of concept is easy; production is hard.
Right now, the flurry of activity building AI agents in house recalls a pattern that should be familiar from the big‑data wave. Teams are staffing up, starting to build bespoke engines with the best will in the world… but then, for some reason, those projects stall before they ever reach production.
In 2024-25, 42% of companies reported scrapping at least one gen‑AI initiative, with leaders citing complexity, cost, and unclear value. And in July this year, an MIT study of enterprise generative‑AI programs found most implementations fail to move the P&L. With agentic AI, as with any emerging technology, internal projects often stall at pilot because teams underestimate ongoing data, evaluation, and governance work.
Against this background, it’s important for organisations to carefully match their approach to their specific business case. Don’t view building as the default go-to if another option makes more sense for your team.
How much does it cost to build an AI agent in-house?
While every team’s process will look a little different, one thing ought to be clear. Every month spent building an in-house AI copywriter agent is a month in which you’re de-prioritising existing workflows. Depending on the size or structure of your team, this can ripple into delays getting products live, refreshing copy, and reacting to the rapidly shifting ecommerce landscape.
A simple cost-of-delay formula can help you judge whether your team has the bandwidth to take on a build.
Plug your own numbers into the following formula:
Incremental revenue per month = traffic × baseline conversion × average order value × expected lift
This equation gives you a quick way to translate funnel improvements into pound or dollar terms. “Traffic” is the number of visitors to your site, “baseline conversion” is the percentage of visitors who already purchase, “average order value” is how much they spend, and “expected lift” is the incremental gain from ongoing improvements. Multiply these figures together, and you’ll see the revenue you risk leaving on the table while your team’s attention shifts to building.
If your typical product‑page improvements lift conversion even modestly, the compounded revenue impact of waiting for an in-house build often dwarfs the software license fees you’d spend on buying an external solution.
Then, add to that the hidden costs of…
People - Contemporary compensation data suggests costs for a minimal internal AI team (including a Product Manager, Machine Learning Engineer, Data Engineer, Machine Learning Ops Lead, Full‑Stack Engineer, and Quality Assessor) will run well into the high six to low seven figures annually in the U.S. That’s without even taking contractors, localisation, or design into account.
Today, tools like GitHub Copilot or Amazon Q Developer make it easier and faster for engineers to write code or spin up internal tools. All the same, these tools don’t erase production complexity (including observability, governance, SEO compliance, and cost management) – all of which require serious human input.
Computation - Inference is an operating expense that scales with tokens generated and model family. Industry analyses show per‑token prices are declining but vary considerably by task and output length; without aggressive token budgeting and caching, unit economics can bite.
Evaluation & safety - You will need a living evaluation suite (including factuality, policy, brand voice, SEO fitness) and governance. The UK’s AI Safety Institute has made clear that model safeguards are brittle, which means continuous testing is a core part of your job if you build.
Maintenance - Google’s classic “technical debt” papers warn would-be AI engineers that machine learning systems accrue notoriously high‑interest maintenance costs – including data shifts, feedback loops, and brittle integrations. This is exactly the kind of work that turns a 12‑month MVP into something that winds up as obsolete the day it ships.
When you should build
All the same, – sometimes teams really are better placed to build rather than buy.
This could be you, if:
You have a unique objective or constraint. Your regulatory or brand voice requirements are so specific that even bespoke vendor solutions cannot comply.
You can clearly articulate the differentiator. You’ve developed proprietary taxonomy or attribute extraction that materially improves PDPs, and are scoping the build to that layer.
You want to use data that you can’t share with vendors. By building, you can directly use first-party data (catalogue attributes, reviews, call transcripts, returns data) without sharing with vendors. This can give you a better factual grounding layer for your copywriter, since it’s wired into systems only you control.
You’re resourced for the boring parts. When building your AI agent for ecommerce, you’ll need to prepare yourself for evals, logging, rollback, model/version control, schema validation, and localisation QA. If you can’t fund these, don’t build. (NIST gives you the checklist.)
You’re in it for the long haul. Although time-to-value is usually slower, over a 3–5 year horizon some companies find they can drive per-SKU costs down by fine-tuning smaller models, batching inference, or leveraging open-weights. This only works if you have both the volume and the in-house skill to make those optimisations pay off.
What would a competitive in-house AI copywriter look like?
Brands who weigh up their options and decide to go ahead and build their own agentic AI copywriter should bear the following features in mind.
Brand governance by default. Your AI copywriter should enforce brand governance by default, ensuring every output adheres to your bespoke tone of voice, legal standards, and brand claims policies. This includes built-in safeguards for regulated categories (e.g., health, finance, or sustainability) in particular. Governance should be automatic rather than bolted on, giving teams confidence that copy not only reflects brand identity but also meets ethical and regulatory requirements at scale.
Grounded outputs. Outputs must be grounded - using RAG or equivalent - to reduce hallucinations, with measurable factuality and compliance safeguards in place. While RAG materially lowers risk, it never eliminates it, so continuous evaluation is essential.
Scale and latency. Your system should reliably generate copy for thousands to millions of SKUs across multiple locales, at predictable costs. Inference remains the dominant ongoing expense, which is highly sensitive to token volume and architecture. Although prices are falling, trends vary by task, making optimisation techniques like prompt design, token budgeting, and batching crucial.
SEO & AI‑search visibility by design. Copy should be designed for discoverability, producing machine-readable outputs that surface in AI Overviews, answer engines, and beyond traditional blue links.
Localisation. 76% of shoppers prefer product info in their native language; 40% will not buy otherwise. Your AI copywriter must deliver culturally nuanced, compliant, and accurate outputs, handling terminology, units, and regulations, at scale.
Integration. Connectivity is a must: your AI copywriter should integrate tightly with your PIM/DAM/CMS platforms, merchandising workflows, and inventory and pricing systems.
Observability & governance. Robust evaluation harnesses, red-team testing, audit logs, and risk controls should be aligned with frameworks like NIST’s Generative AI Profile and ISO/IEC 42001, the first certifiable AI management-system standard.
So, should you build?
If your goal is to win in the new world of search – Google AI Overviews, answer engines, and evolving SEO – what matters most is your AI copywriter’s speed to value and staying power. Building a best‑in‑class agentic AI copywriter is a multi‑year R&D commitment with ongoing computation, evaluation, and governance costs. For most retailers, the smartest move is to buy a platform that already handles grounded, brand-aligned AI copy at scale.
That said, if you’ve got ample time, resources, and AI expertise under your belt, building now could be a high-return move over the long-run. Ultimately, of course, deciding whether to ‘build’ or ‘buy’ is a decision that is highly specific to your brand, team, and circumstances. If you do take this course, make sure to do your homework, keep a clear head, and beware the hidden costs.
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