Halloween Rush: AI Agent for Costume Stock Questions

It's the second week of October and the phone at your shop won't stop ringing. Do you have the inflatable dinosaur in an adult large? Any more fog juice? Is the witch hat in the window the same one that's online? Your staff answer the same twenty questions all day while the person at the till waits.

This is exactly the kind of work an AI agent can take off your plate, as long as you wire it up properly. Here's how to do that for a costume or party supply shop in Burnaby, what it actually costs, and where it's a bad idea.

Why Halloween is a good test case

Most of the questions you get in October are lookups. Size, colour, price, quantity, which location has it, and whether you'll get more before the 31st. The answers live in your point of sale or ecommerce system. Nobody needs judgment to answer "how many adult medium pirate coats are left," they just need to check.

That's the sweet spot. Statistics Canada found that among businesses using AI, "virtual agents or chat bots" were among the most common uses, at 24.8%, behind text analytics (35.7%) and data analytics (26.4%) (Statistics Canada, Analysis on artificial intelligence use by businesses in Canada, second quarter of 2025). The same release reported that 12.2% of Canadian businesses had used AI to produce goods or deliver services in the prior 12 months, up from 6.1% a year earlier.

Retail is behind that curve, though. In the third quarter of 2025, only 5.7% of retail trade businesses said they planned to use AI over the next year, and across all businesses with no plans, 78.1% said AI simply wasn't relevant to what they sell (Statistics Canada, expected use of AI, third quarter of 2025). For a lot of shops, that's a fair call. A seasonal inventory question line is one of the narrower cases where it can make sense.

How the agent actually answers "do you have it?"

The single most important design choice: the AI should never guess at stock. It should look it up every time.

Modern language models do this through tool use, sometimes called function calling. Anthropic's documentation describes the loop plainly. You define a tool, the model returns a structured `tool_use` request, "your code executes the operation and sends back a `tool_result`," and the model writes its answer from that result (Anthropic, Tool use with Claude).

For a costume shop, the tool is a read-only inventory lookup. If you run Shopify, the inventory model is worth understanding before you build anything. Shopify separates available stock ("the inventory that a merchant can sell") from on hand stock, which also counts units that are committed to orders, reserved, damaged or held as safety stock, and it tracks quantities per location (Shopify, Apps in inventory management). Your agent should quote *available* at the right store. Quote *on hand* and you'll promise a customer the costume that's already sitting in someone else's online order.

A practical setup in five steps

Here's the build we'd recommend for a single shop or a small chain in Burnaby, New Westminster or elsewhere in Metro Vancouver:

• Give it one read-only tool. A lookup that takes a product name or SKU, a size and a location, and returns available quantity and price. No ability to edit stock, issue refunds or change orders. OWASP lists "Excessive Agency" as a top risk for LLM applications and recommends you "limit the extensions that LLM agents are allowed to call to only the minimum necessary" (OWASP Gen AI Security Project, LLM06:2025 Excessive Agency).
• Write a short policy sheet it can read. Your return policy on costumes (including worn ones), holds, pickup hours on October 31, and whether you price match. Keep it to one page.
• Set hard handoff rules. Anything about allergies, refunds, complaints or big group orders goes to a person, with the customer's question passed along so they don't have to repeat it.
• Tell people it's AI. More on that below.
• Test it with last October's real questions before you turn it on. Pull fifty from your inbox or call notes and check every answer against the system.

Safety questions need a script, not improvisation

Customers will ask whether a costume is safe near candles or whether those coloured contact lenses are OK. Don't let the model freelance.

Point it at official guidance instead. Health Canada advises choosing fabrics "less likely to catch on fire, such as those made of nylon or heavyweight polyester," and notes that "flame resistant does not mean fire-proof." It also says decorative contact lenses should be bought "from a qualified eye care professional" (Health Canada, Halloween safety). Your agent can quote that, tell the customer what the product label says, and leave it there. If you sell novelty lenses without that kind of oversight, the agent should not be the one explaining why.

Privacy rules still apply to a chatbot

Canada's federal, provincial and territorial privacy commissioners published joint principles for generative AI in 2023. Two matter most for a shop. Openness: make sure "individuals interacting with the tool are aware that they are interacting with a generative AI tool." Accuracy: "take reasonable steps to ensure that any outputs from a generative AI tool are accurate as necessary for the purpose" (Office of the Privacy Commissioner of Canada, Principles for responsible, trustworthy and privacy-protective generative AI technologies).

In practice that means a clear "you're chatting with our AI assistant" line, not collecting names or phone numbers unless you need them for a hold, and not stuffing customer records into prompts. The live stock lookup is also your accuracy control. An agent answering from a spreadsheet you exported on October 1 will be wrong by the 20th.

What it costs to run

The model bill is usually the smallest line. As of October 2026, Anthropic's published API pricing for Claude Haiku 4.5 is US$1 per million input tokens and US$5 per million output tokens, and Claude Sonnet 5.5 is US$2 and US$10 (Anthropic, Pricing). The same page works through a support example of about 3,700 tokens per conversation that comes to roughly US$37 per 10,000 conversations on Haiku 4.5. It also notes that Claude 4.7 and later models use a tokenizer that produces about 30% more tokens for the same text, so check the page for the model you pick.

Illustrative only: if your shop handled 2,000 stock questions in October, that rate works out to about US$7.40 in model usage, a little more once tool definitions and stock lookups add tokens. The real costs are the setup, connecting your inventory system, testing, and somebody checking the transcripts each week. Those take hours, not dollars per token, and they're where most projects succeed or fail.

Where this doesn't apply

Be honest with yourself about these before you build anything:

Your stock count isn't trustworthy. If staff move costumes between stores without recording it, or online and in store stock aren't synced, the agent will confidently repeat bad data. Fix the count first. That alone may cut the calls.

You don't get many questions. If the phone rings a dozen times a day, a pinned "check stock online" link and a good FAQ page will do the job for free.

You're starting too late. Setting this up in the last week of October, with no time to test, is how you end up apologizing to a parent whose kid's costume "was in stock." If it's past mid October, run a simple FAQ this year and build the agent properly for next season.

The questions need a human eye. Fit advice, "will this look good on my partner," and custom group costumes are judgment calls. An agent can book the appointment. It shouldn't pretend to be your stylist.

You want it to take actions. Holding stock, issuing refunds or editing orders raise the stakes. OWASP recommends you "require a human to approve high-impact actions before they are taken." Start read-only and earn trust before adding more.

The evidence also isn't one-sided on payoff. In Statistics Canada's second quarter 2025 data, 89.4% of businesses using AI reported no change in employment levels, and 47.2% said it reduced tasks only to a small extent. Treat an AI agent as relief for your busiest weeks, not a replacement for your floor staff.

Sources

• Statistics Canada. "Analysis on artificial intelligence use by businesses in Canada, second quarter of 2025." 2025. https://www150.statcan.gc.ca/n1/pub/11-621-m/11-621-m2025008-eng.htm
• Statistics Canada. "Analysis on expected use of artificial intelligence by businesses in Canada, third quarter of 2025." 2025. https://www150.statcan.gc.ca/n1/pub/11-621-m/11-621-m2025011-eng.htm
• Anthropic. "Tool use with Claude." Claude Platform documentation, accessed October 2026. https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview
• Anthropic. "Pricing." Claude Platform documentation, accessed October 2026. https://platform.claude.com/docs/en/about-claude/pricing
• Shopify. "Apps in inventory management." Shopify Dev documentation, accessed October 2026. https://shopify.dev/docs/apps/build/orders-fulfillment/inventory-management-apps
• OWASP Gen AI Security Project. "LLM06:2025 Excessive Agency." 2025. https://genai.owasp.org/llmrisk/llm062025-excessive-agency/
• Office of the Privacy Commissioner of Canada. "Principles for responsible, trustworthy and privacy-protective generative AI technologies." 2023. https://www.priv.gc.ca/en/privacy-topics/technology/artificial-intelligence/gd_principles_ai/
• Health Canada. "Halloween safety." Government of Canada, accessed October 2026. https://www.canada.ca/en/health-canada/services/home-safety/halloween-safety.html

Want a second opinion on whether an AI agent makes sense for your shop this season, or whether you should wait until next year? Book a free call with Autana Solutions. We're based in Burnaby and New Westminster, and we'll tell you honestly if a FAQ page would do the job instead.