AI consulting case studies by Joel & Nanz Inc.
An illustrative AI implementation case study for a Canadian engineering consulting firm.
A Canadian engineering consulting firm 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 126,000. Return on investment: 2,420%. Payback period: 0.5 mo. The recommended Maple product for an engineering consulting firm is MapleProjects (AI project and job management), part of the MapleWorkSuite AI platform by Maple AI Consultants (Joel & Nanz Inc.).
For many Canadian engineering consulting firm 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 engineering consulting firm used practical AI automation to break that ceiling.
The firm automated proposal drafting, document retrieval, and internal Q&A, improving billable utilization.
Engineers spent excessive non-billable time searching past work and drafting proposals.
Our approach was deliberately incremental. We sat with the engineering consulting firm's team, traced a request from first contact to resolution, and isolated the handoffs that caused the most friction. Only then did we scope where AI could remove work safely, keeping a human in the loop for anything involving judgement.
The implementation centred on a small number of high-leverage automations rather than a sprawling platform. For the engineering consulting firm, the core pieces were as follows.
Within the first operating cycles, the impact was visible in the numbers the engineering consulting firm already tracked.
“The difference was obvious within weeks. Our team stopped drowning in routine requests and started spending time where it actually matters - with our clients.”
Knowledge mapping
Workflow setup
Deployment
Engineering firms rely on project platforms and CRM to manage deliverables and client work.
The lasting lesson from this engineering consulting firm engagement is that adoption beats sophistication. A modest automation that staff actually use every day outperforms an ambitious one they route around. Designing for the real workflow - and for the people in it - is what turned the technology into a result.
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 5,000 | CAD 126,000 | 2,420% | 0.5 mo |
| Hybrid | CAD 55,000 | CAD 126,000 | 129% | 5.3 mo |
| Custom Build | CAD 170,000 | CAD 126,000 | -26% | 16.2 mo |
For an engineering consulting firm, 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 engineering consulting firm reported annual savings of CAD 126,000, an ROI of 2,420%, a payback period of 0.5 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 an engineering consulting firm handle growth without burning out the team or hiring through every peak.
The first step for any engineering consulting firm is a short discovery conversation to map your current workflow and find the highest-impact place to automate. From there we scope a realistic first project with a clear ROI estimate before any build begins.
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 MapleProjects — AI project and job management — part of the MapleWorkSuite AI platform. It is the closest off-the-shelf fit for an engineering consulting firm like the one above, and it deploys far faster than a custom build.
Most engineering consulting firm teams combine MapleProjects with these complementary tools from the Maple suite:
AI project and job management for a engineering consulting firm.
AI workflow and process automation for a engineering consulting firm.
An AI front-office and operations assistant for admin-heavy teams for a engineering consulting firm.
Get started with MapleProjects on MapleWorkSuite, or book a free consult and we will scope the right configuration and ROI for your team.
Get Started on MapleWorkSuite Book a Free ConsultSend a note straight to the team. We read every one and fix bugs fast.
🏆 Find a real bug? We pay a bounty — credit toward Maple products or services for any valid bug.