Case studies

AI automation case studies and results.

A look at recent True North Agentics engagements across billing automation, AI product development, and team training. Each figure below is a conservative estimate with the assumptions shown — not a guarantee — so you can judge the scale for yourself.

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Case study 01

Billing operations automation for a finance and payments team

Finance / payments operations Workflow automation Billing QA Reporting automation

The challenge

A finance operations team was losing significant time every month preparing billing packages, validating partner data, checking exceptions, and moving information between spreadsheets, reports, and delivery folders. The process worked — but it leaned heavily on manual review and the same repeated effort on every billing cycle.

What we built

We automated the billing workflow end to end. The system ingests billing files, refreshes the workbook, runs QA checks, prepares reporting outputs, organizes archive folders, and builds a more reliable review path before delivery — so the monthly cycle runs to a standard instead of from memory.

Result

An estimated 35–50 hours of manual billing work removed per month.

At a conservative loaded cost of $75/hour, that is roughly $2,625–$3,750 per month — about $31,500–$45,000 per year in recovered time. The larger value came from reduced billing risk, repeatable QA, and a cleaner audit trail.

Case study 02

AI product suite for a global online library platform

Digital education / online library AI product expansion Internal tools User-facing AI

The challenge

A global online library platform wanted to grow beyond its digital reading experience and use AI to strengthen its product — not as a marketing label, but as genuinely useful tools sitting naturally alongside its existing library content.

What we built

We designed and set up a full AI suite around the platform's current offerings: AI-assisted learning support, content-discovery concepts, teaching and classroom-support workflows, cost modeling, branded product positioning, and an implementation plan for bringing AI into the existing product ecosystem.

Result

Replaced weeks of research and planning — an estimated 80–120 hours of product, strategy, and implementation time.

At a conservative blended rate of $100/hour, that is approximately $8,000–$12,000 in planning and development acceleration — before the larger revenue upside of a new AI-enabled product line.

Case study 03

AI Power Lab training for a professional services team

Professional services / SMB operations AI training Team enablement Practical adoption

The challenge

A company wanted to introduce AI to its staff — without a vague presentation or a complicated software rollout. The team needed to understand how AI could help in their actual work: writing, research, meetings, client communication, admin follow-up, reports, and everyday decision support.

What we delivered

We created and delivered an AI Power Lab: a structured, hands-on training session built around the company's real workflows. Staff learned how to use AI safely, where it saves time, what not to put into AI tools, and how to build practical prompts for repeat work.

Result

Employees left with usable AI habits the same day. If 15 employees save just 2 hours a week, that is about 120 hours per month.

At a conservative loaded cost of $50/hour, that is roughly $6,000 per month — about $72,000 per year in potential productivity gains.

Case study 04

Process automation for a construction and project-management firm

Construction / project management Process mapping Workflow automation AI agents & tools

The challenge

A growing firm ran its operations on detailed process maps and manual workflows — reliable, but heavy on repeated hands-on effort. Every recurring task meant the same steps by hand, and the knowledge lived in people's heads rather than in repeatable, easy-to-run tools.

What we built

We converted the firm's existing process maps and workflows into automated, easy-to-operate AI agents and tools — matched to how the team actually works, with people kept in control of every output. The goal was a smooth transition the staff could run themselves, not a black box.

Result

A smooth team transition and an estimated ~40% of cumulative process-labour hours saved.

The team adopted the tools without disruption, freeing recurring hours back to higher-value work — and the engagement is in discussion for additional scope.

Case study 05

Routine-inquiry reduction for a provincial government team

Government / public sector Citizen service Intake & inquiry automation Responsible AI

The challenge

A provincial government team handled a high volume of incoming inquiries through a manual intake process. Much of the workload was the same handful of repeat questions, handled one at a time — slow for the public and a heavy, recurring load for staff.

What we built

We identified the repeat-inquiry patterns, mapped the common-request steps, and designed a structured routing, drafting, and review workflow with responsible-AI controls throughout — staff review and approve, and nothing goes out unchecked. Data stayed governed and Canadian.

Result

An estimated ~40% reduction in routine-inquiry handling, plus much faster process mapping.

Staff time shifted from repetitive triage to the cases that actually needed judgment, and the intake process became documented and repeatable rather than tribal knowledge. Client anonymized at their request.

Savings figures are conservative estimates based on the stated hourly rates and hours, provided to illustrate scale. Actual results depend on your processes, team, and scope. We anonymize client details unless we have permission to share them.

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