AI consulting case studies by Joel & Nanz Inc.
An illustrative AI implementation case study for a Canadian real estate brokerage group.
A Canadian real estate brokerage group adopted AI automation to handle high-volume routine work (intake, scheduling, follow-up and reporting) while keeping staff focused on judgement-based tasks. Reported annual savings: CAD 142,000. Return on investment: 2,482%. Payback period: 0.4 mo. The recommended Maple product for a real estate brokerage group is MapleConcierge (an AI front-office and operations assistant for admin-heavy teams), part of the MapleWorkSuite AI platform by Maple AI Consultants (Joel & Nanz Inc.).
For many Canadian real estate brokerage group operations, growth quietly turns into a staffing problem. The work that wins customers - answering enquiries, processing requests, keeping records accurate - is the same work that eats every available hour. This case study looks at how one real estate brokerage group used practical AI automation to break that ceiling.
The brokerage automated lead qualification, showing coordination, and client follow-ups, increasing agent productivity while reducing admin staffing needs.
Agents and assistants spent excessive time responding to inquiries, scheduling showings, and managing follow-ups.
We started where every responsible AI project should: mapping the actual workflow. Rather than bolting AI onto a broken process, we documented how requests really moved through the real estate brokerage group, found the steps that were repetitive and rule-based, and ranked them by time saved versus effort to automate.
We deployed a focused set of capabilities, each targeting one of the bottlenecks identified during discovery. For this real estate brokerage group, the build emphasised the following.
Once live, the automation produced measurable change across the real estate brokerage group's day-to-day operations.
“The difference was obvious within weeks. Our team stopped drowning in routine requests and started spending time where it actually matters - with our clients.”
Lead flow mapping
Bot and workflow configuration
Training and rollout
Real estate brokerages use CRM and MLS tools with workflows and secure signatures for client service.
AI delivers the most value in a real estate brokerage group when it is pointed at the repetitive 80% of the work, not the exceptional 20%. That boundary is where many projects fail. Here, careful scoping meant the automation earned trust quickly, because it never overreached into decisions it was not equipped to make.
SMBs can choose a Maple SaaS deployment, a hybrid integration with existing tools, or a fully custom build. The ranges below reflect realistic first-year figures for each path.
| Approach | First-Year Cost | Annual Savings | ROI | Payback |
|---|---|---|---|---|
| Maple SaaS | CAD 5,500 | CAD 142,000 | 2,482% | 0.4 mo |
| Hybrid | CAD 65,000 | CAD 142,000 | 118% | 5.6 mo |
| Custom Build | CAD 190,000 | CAD 142,000 | -25% | 16.1 mo |
For a real estate brokerage group, AI is most effective at absorbing high-volume, repetitive work - intake and enquiries, scheduling and follow-up, data entry between systems, and routine reporting. That frees skilled staff to focus on the judgement-based work that actually differentiates the business, while customers get faster, more consistent responses.
In this engagement the real estate brokerage group reported annual savings of CAD 142,000, an ROI of 2,482%, a payback period of 0.4 mo. These figures are illustrative of the kind of outcome a comparable operation can target; actual results depend on volume, current processes, and how much routine work can be safely automated.
A focused project typically runs around six to eight weeks: discovery and workflow mapping first, then a staged build and secure integration, a live pilot alongside the team, and a final rollout with training and dashboards. Starting with one high-impact workflow keeps the timeline short and the results measurable.
No. The goal is to remove repetitive load, not people. We keep a human in the loop for anything involving judgement, and route unusual cases to staff before the automation acts. In practice the technology lets a real estate brokerage group handle growth without burning out the team or hiring through every peak.
Rarely. Most real estate brokerage group projects layer AI automation on top of the tools you already use, connecting to them rather than replacing them. That keeps disruption low and lets staff keep working in familiar systems while the repetitive work happens behind the scenes.
Explore more in our full case study library, or read about our AI services for Canadian SMBs and the benefits of AI for small business.
The capabilities in this case study are delivered through MapleConcierge — an AI front-office and operations assistant for admin-heavy teams — part of the MapleWorkSuite AI platform. It is the closest off-the-shelf fit for a real estate brokerage group like the one above, and it deploys far faster than a custom build.
Most real estate brokerage group teams combine MapleConcierge with these complementary tools from the Maple suite:
An AI front-office and operations assistant for admin-heavy teams for a real estate brokerage group.
An AI phone receptionist that answers, books and routes calls 24/7 for a real estate brokerage group.
AI workflow and process automation for a real estate brokerage group.
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