Call a plumbing company in Surrey at 7 PM on a Tuesday. If you get voicemail, you’ll probably hang up and try the next one on Google. Most people do. Research from Aira suggests roughly 62% of calls to small businesses go unanswered, and according to Dialzara, about 85% of those callers never try again. A significant number just call a competitor instead.

Now imagine calling that same company and a friendly voice answers on the first ring. It asks what you need, checks tomorrow’s availability, and books you a morning appointment. It even sends a confirmation text. You’d assume it was a receptionist. It might not be.

AI phone answering has quietly become a real business tool over the past year or so. And if you haven’t looked into it yet, there’s a decent chance one of your competitors already has.

This Isn’t the “Press 1 for Sales” You Remember

When most business owners hear “AI answering the phone,” they picture those clunky automated menus from the early 2000s. The ones where you’d say “representative” four times before the system understood you. Modern AI voice agents are a completely different category of technology.

Today’s AI receptionists carry on natural conversations. A caller can say “I need to get my furnace looked at sometime next week, probably Wednesday or Thursday” and the system will parse that, check a calendar, and suggest a specific time. It can answer frequently asked questions about your business, collect caller information, qualify leads based on criteria you define, and transfer calls to a live person when the situation calls for it.

The voice quality has gotten surprisingly natural. Most callers genuinely can’t tell the difference, at least for routine interactions. That said, transparency seems to matter here. Research from COPC found that customers who are told upfront they’re speaking with AI report 34 points higher satisfaction than those who find out later. Being honest about it isn’t just ethical, it actually works better.

What a Typical Setup Looks Like

The mechanics are simpler than most people expect. You sign up with a provider, connect it to your existing phone system (usually through call forwarding or a VoIP integration VOIP phone systems), and then “train” the AI on your business. That training usually means uploading your FAQ, service list, hours, pricing guidelines, and any workflows you want it to follow.

Most platforms are no-code. You’re not writing software. You’re filling out forms and toggling settings. A straightforward setup can be live in under an hour, though getting it properly dialed in (with the right escalation rules, the right tone, the right answers to edge-case questions) typically takes a bit more ongoing effort.

There are a growing number of providers in this space. A few worth knowing about:

• Dialbox is purpose-built for Canadian businesses, supports 26 languages with real-time switching, and is PIPEDA-compliant out of the box.
• Upfirst starts at $24.95/month, making it one of the lowest-cost entry points available.
• Smith.ai runs a hybrid model with AI handling routine calls and real humans stepping in for complex ones, though pricing starts around $600/month.
• RingCentral now offers an AI Receptionist at $35/month for 100 minutes, with enterprise-grade integrations behind it.

For most small businesses, you’re looking at somewhere between $50 and $300 per month for a solid AI answering setup. Compare that to a full-time human receptionist at roughly $3,750 to $4,000 per month including benefits, and the math gets interesting. On a per-call basis, industry estimates put AI at around $0.40 per call versus $7 to $12 for a human-handled call.

What Callers Actually Experience

The best way to understand AI phone answering is to think about what happens on the caller’s end. Here’s a realistic scenario for, say, a dental clinic in Langley using an AI receptionist.

A patient calls at 6:45 PM, after the office has closed. The AI answers with the clinic’s name, asks how it can help. The patient says they want to reschedule their cleaning. The AI pulls up available slots, offers two options, confirms the new time, and sends a text confirmation. The whole call takes about 90 seconds.

For the patient, it felt like talking to a helpful front desk person. For the clinic, that’s a call that would have gone to voicemail (and probably been forgotten about) turned into a kept appointment.

Now here’s where honesty matters. AI handles straightforward, routine interactions well. Booking, rescheduling, answering common questions, taking messages, routing calls to the right person. Current estimates suggest AI can manage 85 to 95% of routine business inquiries accurately when it’s been properly trained on your specific business information.

But it has real limitations. A caller with a heavy accent, someone who’s upset and talking fast, a question that requires judgment or context the AI wasn’t trained on… these are situations where AI can stumble. And when it stumbles, the caller experience can go downhill fast. This isn’t a technology you deploy and forget about. It needs monitoring and tuning, especially in the first few weeks.

This is why most of the businesses we talk to end up preferring a hybrid approach. AI handles after-hours calls, overflow during busy periods, and the routine stuff. Humans handle the complex, sensitive, or high-value conversations. It’s typically not an either/or decision.

How We’ve Seen This Play Out in Dealerships

Kelly, our founder, spent over 20 years in the automotive industry before starting Raxxos, including managing IT for several major Lower Mainland dealership groups like Wolfe Auto Group, Trotman Auto Group, and Preston. One thing he’ll tell you about dealerships: the phone is everything. A missed call on a Saturday afternoon could be a $60,000 truck sale that walks into the competitor’s showroom instead.

Dealerships typically have service advisors, parts counters, sales floors, and F&I offices all fielding calls at the same time. During peak hours, calls get dropped constantly. We’ve seen this across every dealership group we’ve worked with along the Fraser Highway corridor and out to Chilliwack. An AI receptionist that can answer overflow calls, route them to the right department, and take a proper message when someone’s busy could genuinely change the math on lost revenue for a business like that.

And dealerships aren’t unique here. The same dynamic plays out at medical clinics, law firms, real estate offices, and trades contractors across Surrey and the Fraser Valley. Any business where a missed call often means a missed customer is a natural fit for this technology.

Compliance in BC: What You Actually Need to Know

If you’re a BC business, there are two privacy frameworks to think about when implementing AI phone answering: PIPEDA (the federal law) and PIPA BC (the provincial one). BC is one of the few provinces with its own private-sector privacy legislation, and it can be stricter than the federal rules in some areas.

The practical requirements aren’t as complicated as they might sound:

• If calls are being recorded, you need to disclose that upfront. A simple “This call may be recorded for quality purposes” in the greeting typically satisfies this requirement.
• Your organization remains accountable for personal information even when it’s processed by a third-party AI provider. Choose a provider that stores and handles data in compliance with Canadian privacy law.
• Under PIPA BC, the collection of voice data needs to pass the “reasonable person” test: would a reasonable person consider it appropriate for a business to record and process their call in this context? For a receptionist function, this is generally straightforward.
• Penalties can reach up to $100,000 for organizations under PIPA, so it’s worth getting this right from the start.

Several providers (Dialbox and Voxs, for example) are specifically built for the Canadian market with PIPEDA compliance baked in. If you’re working with an IT provider to set this up AI automation, compliance evaluation should be part of that conversation from day one.

BC Is Behind on Adoption (Which Might Be Your Window)

Here’s an interesting data point. According to a recent report from the Greater Vancouver Board of Trade, only about 39% of BC businesses have even considered adopting AI, and just 15% say they’re actively using it or planning to. That puts BC behind both Ontario and Quebec.

Nationally, roughly 73% of Canadian SMEs haven’t adopted AI at all. The most common reason, cited by 69% of non-adopters, is that they can’t identify a clear business case.

AI phone answering might be one of the clearest business cases there is. The ROI calculation is relatively simple: how many calls are you missing, and what’s each one worth? For a contractor, a missed call could easily be a $5,000 job. For a law firm, it could be a $15,000 file. An AI receptionist that catches even a few of those calls each month can pay for itself many times over.

The BC government’s Regional Artificial Intelligence Initiative also offers grants to help local businesses adopt AI tools, which could offset some of the initial costs. Worth looking into if you’re on the fence.

If You’re Considering This, Here’s Where to Start

You don’t need to overhaul your phone system overnight. A reasonable approach for most small businesses looks something like this:

1. Figure out your actual missed call volume. Most phone systems and VoIP platforms can surface this data in their call analytics. If you’re missing more than a handful of calls per week, especially after hours, the case for AI answering gets strong quickly.
2. Decide what you want the AI to handle. After-hours only? Overflow during busy periods? All inbound calls with human escalation for complex issues? Start narrow and expand from there.
3. Choose a provider with Canadian data handling. For BC businesses, PIPEDA and PIPA compliance isn’t optional. A Canadian-focused provider simplifies this significantly.
4. Train it properly on your business. The difference between an AI receptionist that impresses callers and one that frustrates them usually comes down to how well it’s been configured with your specific services, pricing, hours, and common questions.
5. Monitor and tune. Listen to call recordings, check for patterns in what the AI handles well and where it struggles, and adjust. Plan to spend some time on this in the first month especially.

We’ve been helping Lower Mainland businesses with their phone systems and AI integrations AI automation for years, and this is one of the areas where we’re seeing the most interest right now. If you’re curious whether AI phone answering makes sense for your business, or you just want to hear what it actually sounds like in action, we’re happy to walk you through it. Reach out for a free consultation Book a free consultation and we can take a look at your current setup together.

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It’s called Google NotebookLM. And while it’s gotten some attention in tech circles, most small and mid-sized businesses in the Lower Mainland and across BC still haven’t come across it. That gap is worth closing, but with some important caveats for Canadian businesses, especially those of you who’ve made a deliberate choice to keep your data inside Microsoft 365.

What NotebookLM actually does

NotebookLM lets you upload documents (PDFs, Google Docs, meeting transcripts, web pages, audio files) and then have a conversation with them. You ask questions, it answers from the material you’ve provided. It summarizes, cross-references, pulls specific clauses, surfaces key points.

What makes it different from just pasting something into ChatGPT is that it’s grounded entirely in what you’ve uploaded. When it gives you an answer, it cites the specific source and page. You can click through and verify. It’s not mixing in outside information or making things up from its training data. It’s working from your documents and only your documents.

That grounding is the whole reason it works for business use. “Does our service agreement include a limitation of liability clause?” gets you a real answer tied to the actual text, not a generic explanation of what limitation of liability clauses usually say.

What businesses are using it for

The use cases tend to cluster around a few areas:

• Contract and document review. Upload a vendor agreement, lease, or service contract. Ask it to summarize key terms, flag unusual clauses, or explain a section in plain English. Not a replacement for a lawyer on anything consequential, but useful for getting oriented before you spend lawyer time on it.
• Meeting notes and project follow-up. Upload transcripts or notes from a series of client meetings. Ask it to pull all open action items, summarize what was decided on a topic, or identify themes across the last six months of conversations.
• Staff onboarding and training. Upload your operations manual, policies, or technical documentation. New employees can ask questions and get answers from your actual materials, rather than interrupting someone senior for every small thing.
• Research and competitive analysis. Upload industry reports, competitor materials, or market analyses and ask it to compare, summarize, or extract specific data points.

There’s also a feature called Audio Overview that surprises most people the first time they try it. NotebookLM can generate a podcast-style audio conversation between two AI hosts discussing the contents of your notebook. Upload a dense report, click one button, and a few minutes later you have a natural-sounding audio summary you can listen to on your commute. It’s more useful than it sounds.

The question Canadian businesses should ask first

Before you start uploading client files and internal documents to any AI tool, there’s a question worth asking: where does your data go, and what happens to it?

For Canadian businesses, this has real implications under PIPEDA (Canada’s federal private sector privacy law) and under provincial equivalents in BC, Alberta, and Quebec. If you’re uploading documents that contain client personal information, you have obligations around how that information is handled and where it’s processed.

The good news on NotebookLM: Google has stated that your uploaded documents are never used to train its AI models. If you’re accessing it through a Google Workspace account (a paid work account, not a personal Gmail), your uploads and queries are shielded from human review entirely. That’s a meaningful distinction from using a personal account.

The less good news: your data is processed on Google’s servers, which for some regulated industries or clients with strict data handling requirements may not be appropriate. If you’re handling health information, legal files, or anything covered by a client confidentiality agreement, read the terms carefully before uploading.

Which brings us to the bigger question we get from a lot of the businesses we work with.

We’re a Microsoft shop. Is there a NotebookLM for us?

This is one of the principles we follow with the businesses we advise at Raxxos: pick one vendor ecosystem and stay in it. For most BC businesses, that means either Google Workspace or Microsoft 365. And for the majority of our clients, it’s Microsoft.

The argument for staying in your ecosystem isn’t just about simplicity. It’s about security and data governance. Every time you introduce a third-party AI tool that sits outside your primary vendor’s environment, you’re creating a new surface area for data to leave that environment. That’s a manageable risk in some contexts. In others, it’s not worth it.

So if you’re a Microsoft 365 shop and you want what NotebookLM offers, the answer is Microsoft Copilot Notebooks.

Copilot Notebooks is Microsoft’s direct equivalent. It’s an AI-powered workspace where you bring together files, meeting notes, chat history from Teams, links, and other content, and then use Copilot to query, summarize, and work with all of it. Because it lives inside Microsoft 365, your data stays inside your existing Microsoft tenant. The same data governance policies, the same compliance boundaries, the same security controls you’ve already set up apply automatically.

The tradeoff is cost. NotebookLM is free. Copilot Notebooks requires a Microsoft 365 Copilot license, which runs around $30 USD per user per month on top of your existing Microsoft 365 subscription. For a team of ten, that’s a real number to weigh.

But for businesses that have made Microsoft their platform and want to keep all their data there (which is the right call for most businesses from a security and simplicity standpoint) Copilot Notebooks is the better choice. You’re not introducing a new vendor, a new set of terms, or a new place for data to land.

Our own team uses it for exactly this kind of work. Georgy Johnson, one of our technicians, recently used Copilot to set up a structured Microsoft OneNote runbook for IT operations, something that would have taken hours to research and design manually. “It gave me a great template to follow,” he notes. That’s the kind of practical, in-workflow use case that makes Copilot worth the investment for a Microsoft-first business.

“The common concern I hear regarding AI tool use is data security, whether what you share is ever used externally. I tell our clients, who are all on Microsoft, to stick to using Copilot instead of other tools, as your data stays within the organization. Of course you need to put some guardrails in place, but overall Microsoft and Copilot have been great for troubleshooting, writing scripts, and assisting with other tasks.”

Georgy Johnson, Raxxos Technology Inc.

How to think about this decision

Here’s a simple framework:

• You’re on Google Workspace and handle relatively low-sensitivity documents: NotebookLM is free, works well, and is worth trying today. Start with notebooklm.google.com.
• You’re on Google Workspace and handle sensitive client data: Use NotebookLM through your Workspace account (not personal Gmail), review Google’s data handling terms, and consider whether specific document types should stay out of it entirely.
• You’re on Microsoft 365 and want to keep everything in your ecosystem: Look at Microsoft Copilot Notebooks. Factor the licensing cost into your decision, but take seriously the security benefit of staying inside a single vendor environment.
• You’re not sure what ecosystem you’re in or whether your current setup is actually secure: That’s a conversation worth having before you add any AI tools to the mix.

The broader point is that AI tools for document work are genuinely useful and not particularly complicated to start using. The question isn’t whether they’re worth trying. It’s whether you’re trying them in a way that’s appropriate for the kind of data your business handles.

If you want a straight answer on what makes sense for your specific setup, what tools are appropriate, what the actual security and privacy implications are, and how to introduce AI capabilities without creating new problems, that’s exactly the kind of conversation we have with businesses across the Lower Mainland every day. Book a free call with Raxxos and we’ll give you a straight answer.

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Important Notice from Raxxos

We do not recommend the use of OpenClaw for most businesses. While it is an impressive and capable piece of technology, OpenClaw is a complex, high-risk software platform that should not be operated without extensive cybersecurity experience and careful planning. There are documented cases of OpenClaw instances being compromised due to misconfiguration and security vulnerabilities. If you choose to deploy it, you do so at your own risk. We strongly encourage consulting with a qualified IT security professional before proceeding.

OpenClaw has attracted significant attention in tech circles — 60,000 GitHub stars in 72 hours, comparisons to JARVIS, and a wave of developers ordering Mac Minis specifically to run it. Most of the coverage has been aimed at developers and early adopters. This article is aimed at business owners who want to understand what it actually is, how it works, and — critically — why Raxxos advises most businesses to approach it with caution.

Georgy Johnson, a technician here at Raxxos, says that as of right now he hasn’t had clients come to him directly with questions about OpenClaw — but he’s noticed how fast it’s moving through conversations. “News about it is spreading like wildfire,” he told us. “It’s the hottest topic right now.” That tracks with what we’ve seen: the buzz is real, the questions are coming, and most of the information out there isn’t aimed at helping business owners make a clear-headed decision.

The Hype Machine Nobody Is Talking About

If you’ve been on YouTube recently, you’ve seen the videos. “Everyone should be using OpenClaw.” “Here’s how easy it is to set up.” “I built an AI employee in 20 minutes.” The thumbnails are bold, the energy is high, and the message is consistent: this is the future and you’re already behind.

What most of those videos don’t tell you is who’s paying for them.

A significant number of the most-watched OpenClaw tutorials are sponsored by VPS hosting companies — Hostinger being the most prominent example. The business model is straightforward: get as many people as possible to sign up for a VPS through an affiliate link, earn a commission on each signup. The more people who run OpenClaw, the more VPS subscriptions get sold. The incentive is volume, not accuracy.

One well-known AI YouTuber has publicly stated he was offered $30,000 to promote Hostinger on his channel — and turned it down specifically because he didn’t feel comfortable with the promotional angle. That’s a meaningful data point. For every creator who turned it down, plenty accepted.

The result is a YouTube landscape where the vast majority of OpenClaw content is financially incentivized to make the setup look easy, the risks look manageable, and the audience feel like they’re missing out if they don’t act now. Almost none of it addresses the real security questions in any depth.

We’re not saying OpenClaw is a scam or that everyone covering it is acting in bad faith. We’re saying the information environment around it is heavily distorted by financial incentives, and business owners making decisions based on that content are working with an incomplete picture.

One More Thing Worth Knowing: It Was Vibe-Coded

OpenClaw is open-source software, which means anyone can read the code. People who have done so have noted that significant portions of it appear to have been written with heavy AI assistance — what the developer community has started calling “vibe-coded” software. Code generated quickly with AI tools, iterated fast, shipped fast.

That’s not inherently a disqualifier. A lot of software is built this way now and works fine. But vibe-coded software that is being deployed with broad access to your business systems, your email, your files, and your network — and that has not been through the kind of rigorous security audit that enterprise software typically undergoes — is a different category of risk. The people qualified to evaluate whether a piece of software like this is safe to run in a business environment are cybersecurity professionals who can read the code, understand the architecture, and assess whether it’s been sandboxed correctly. That is not most business owners, and it is not most YouTube tutorial watchers.

What OpenClaw Actually Is

OpenClaw is an open-source AI agent that you install on your own hardware. Unlike ChatGPT or Microsoft Copilot, which are cloud services you log into through a browser, OpenClaw runs locally — on a computer in your office, on your home network, or on a private server you control.

Once it’s running, it acts as an always-on AI coordinator. It connects to the apps and data you already use, takes instructions through messaging apps, executes tasks autonomously, and builds up a memory of your preferences and context over time.

The key distinction from most other AI tools is that OpenClaw doesn’t just answer questions — it takes actions. It can read and write files, send messages, search the web, run scripts, draft and send emails, monitor things on a schedule, and coordinate tasks in the background without you manually prompting it each time.

One important note: OpenClaw is the agent layer, not the AI itself. It connects to AI models separately — either cloud-based ones like Claude or GPT-4, or models running locally on your own hardware. That distinction matters a lot when it comes to privacy and security.

How People Interact With It

You don’t interact with OpenClaw through a dedicated app or dashboard. You talk to it through messaging apps you’re already using — WhatsApp, Telegram, iMessage, Slack, Discord. You send a message the way you’d send one to a colleague, and OpenClaw responds and acts on it.

That interface simplicity is part of what makes it appealing. It’s also part of what makes it dangerous — because the ease of use can obscure how much access the agent actually has to your systems.

What Some Businesses Are Using It For

The OpenClaw community has shared a range of real-world use cases — inbox management, daily briefings, document drafting, research tasks, lead monitoring, and meeting prep. In the right hands, with the right configuration, some of these applications are genuinely useful.

We’re not going to walk through each one in detail here, because we don’t want to get ahead of the more important conversation: whether any business should be running OpenClaw at all without proper security infrastructure in place.

Why Raxxos Advises Most Businesses to Stay Away — For Now

We work with businesses across the Lower Mainland on IT and cybersecurity every day. When we look at OpenClaw through that lens, we see a tool with real capability and real risk — and the risk profile is serious enough that we want to be direct about it.

Georgy described the pattern he sees when businesses move quickly on new technology without thinking through the downstream effects: “A common oversight is failing to consider the broader implications. While the intended outcome may be highly beneficial, the process can introduce unforeseen security, compliance, or reputational risks. In some cases, those challenges only become apparent after significant progress has been made — which makes them much harder to address.” That’s a good description of what we’ve seen with OpenClaw deployments that went wrong.

There are documented cases of OpenClaw instances being compromised. Because OpenClaw is open-source and self-hosted, the security of any given deployment depends entirely on how it’s configured and maintained. Misconfigured instances have been exploited. This isn’t theoretical — it’s happened to real deployments run by people who thought they had it set up correctly.

An agent with broad access is a significant attack surface. OpenClaw is designed to have access to a lot — your files, email, calendar, browser, and the ability to run scripts and execute commands on your systems. That access is where its usefulness comes from. It’s also where a security failure becomes catastrophic. A compromised or misconfigured agent could expose sensitive client data, send communications you didn’t authorize, or make system changes that are difficult or impossible to reverse.

Prompt injection is a real and underappreciated threat. Because OpenClaw acts on instructions it receives and processes content from the web, email, and documents, malicious content in those sources can potentially be crafted to hijack the agent’s behaviour. This attack vector is active and not fully solved in any current AI agent framework, including OpenClaw.

Most setups transmit data to US servers. OpenClaw runs locally, but the AI models it connects to usually don’t. If you’re using cloud-based models like GPT-4 or Claude — which most people do — the content of your tasks, conversations, and file contents is transmitted to servers in the United States. For businesses handling sensitive client data under PIPEDA or BC’s PIPA, this is a compliance issue that needs a clear answer before deployment, not after.

Nobody is monitoring it by default. Like any software running on your infrastructure, OpenClaw needs to be kept updated, monitored, and troubleshot. Logs should be reviewed. Permissions should be audited. If something behaves unexpectedly, someone needs to notice and respond. Most small businesses don’t have the internal IT capacity to manage that ongoing vigilance — and without it, problems tend to compound quietly until they become serious.

There is no vendor accountability. OpenClaw is open-source software maintained by a community. There is no company standing behind it with a support line, an SLA, or liability if something goes wrong. You are on your own in a way that is fundamentally different from using an enterprise software product.

Who Should Be Running OpenClaw

Developers, security professionals, and technically sophisticated early adopters who understand what they’re taking on and have the skills to configure it safely. Specifically: people who can read and audit the source code, understand how to properly sandbox an application with broad system access, and have the expertise to evaluate whether a rapidly-developed, AI-assisted codebase meets the security standards required for their environment.

That is a small group. It does not describe most business owners, and it does not describe most people watching YouTube tutorials about how to set this up in an afternoon.

The way Georgy thinks about new technology decisions reflects how we approach this with clients generally: “My mindset is that you only need just enough tech to get the job done — nothing less, nothing more.” OpenClaw may well be the right tool for certain businesses eventually, as the ecosystem matures and security tooling improves around it. Right now, for most businesses, it’s more tech than the job requires — and more risk than the benefit justifies.

If that’s not you or someone on your team, we’d encourage waiting. The AI agent space is moving fast. Tools that deliver similar capabilities with better security guardrails, vendor accountability, and easier configuration are coming — and some already exist within platforms like Microsoft 365 Copilot that are built with enterprise security from the ground up.

If You’re Still Interested

We’re not here to tell you what to do. If you’re curious about OpenClaw and want to understand whether there’s a version of this that could work securely for your business, we’re happy to have that conversation. We can help you evaluate whether the use case makes sense, what a responsible deployment would actually require, and whether the risk-benefit calculation adds up for your situation.

What we won’t do is set it up for a client without that conversation happening first. The exposure is too significant for us to treat it as a routine implementation.

Book a free conversation with Raxxos if you want a straight answer on whether this is right for your business — and if not, what alternatives might get you where you want to go.

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