Artificial Intelligence Solutions
Put AI agents to work on research, documents and routine tasks. We build and deploy them within your existing systems, with clear permissions and human review where judgment matters.
Our AI Services
Comprehensive AI solutions designed to transform your business operations and unlock new levels of efficiency.
AI Integration Solutions
Deploy AI agents that research information, process documents and use your business tools, with defined permissions and human review.
- AI Agent Deployment
- Workflow Automation
- Human Review & Permissions
- System Integration
Reduce repeated effort with a workflow designed around your team and systems
AI Training & Education
Learn how to maximize AI tools like ChatGPT, Gemini, and Copilot for Founders, Owners, and Staff/Employees.
- Executive Training
- Team Workshops
- Best Practices
- Tool Optimization
Empower your team to leverage AI effectively and confidently
AI Business Analysis
We analyze your business to find opportunities to implement AI-powered solutions for maximum impact.
- Process Assessment
- ROI Analysis
- Implementation Roadmap
- Risk Evaluation
Identify high-impact AI opportunities with clear business value
AI Model Training & Fine-tuning
Custom AI model development, training, fine-tuning, and inference for your specific business needs.
- Custom Models
- Data Processing
- Model Training
- Performance Optimization
Get AI solutions tailored specifically to your industry and use cases
AI delivery in a real workflow
Our published M&A advisory case describes document intake, buyer research and matching with explainable recommendations and human review. Read the project context and what was built before deciding how it relates to your business.
Read the AI-assisted buyer research caseMost AI advice is written for companies that don't exist
The prevailing advice assumes an organization with a data team, a greenfield process, and appetite for a multi-year programme. Almost none of the Ontario businesses we work with look like that. They have twenty to three hundred people, software bought over fifteen years, a process that works well enough, and no tolerance for a project that might not land.
For that kind of business the useful question isn't “what is our AI strategy”. It's narrower and much more answerable: which specific, repetitive, expensive task in this company could a model do acceptably well, and what would that be worth? Answer that once, correctly, and you have a real project. Answer it wrong and you have an expensive pilot nobody uses.
The one place AI reliably earns its keep
After two decades of building software and several years of applying AI across our own business, the pattern we keep returning to is this: models are good at turning unstructured input into structured data. Reading a document and pulling out the fields. Classifying an inbound message. Matching two records that refer to the same thing but don't look alike. Summarising a long thread into something actionable.
That's a narrower claim than most of this industry makes, and it's deliberate. It's also where the measurable money is, because that work is currently being done by people, at a cost you can calculate, at a speed you can compare against.
What we include, and what we insist on
An AI engagement covers the model selection, the prompting or fine-tuning work, the integration into whatever system the output needs to reach, the error and exception paths, and a measurement approach so you can see whether it's working. It also includes a human review step anywhere a wrong answer would cost you — and we push hard on this, even when a client would rather have full automation.
The reason is simple. Models are confidently wrong in ways that traditional software isn't. Conventional code fails loudly; a model produces a plausible incorrect answer and moves on. A design that assumes the output is always right will eventually put a wrong number in front of a customer, and by then the trust is gone. Routing uncertain cases to a person costs a little throughput and buys the whole thing credibility.
We run this on ourselves first
Since 2022 we've operated HeitechSoft as a working experiment in applying AI across our own business — not as a marketing line, but because it's the only way to know which tools hold up under real use rather than in a demo. We've abandoned plenty that didn't. That experience is why our recommendations tend to be narrower than what clients expect, and why we're comfortable telling you a use case isn't worth pursuing.
If you're not sure where to start, a AI Discovery & Roadmap helps produce the shortlist. This paid engagement is scoped and quoted before work begins. If you have a specific idea and nobody can tell you whether it will work, a proof of concept settles it in weeks. And if the need is ongoing judgment rather than a project, fractional AI leadership is usually the better arrangement.
Where AI is worth the money
Three business shapes account for most of the AI work that actually returns something. If your operation looks like one of them, there's probably a case here.
High-volume, rules-heavy operations
Order processing, claims, applications, compliance records, service requests. Work that follows patterns, happens hundreds of times a month, and currently occupies people who could be doing something harder.
Businesses drowning in unstructured input
Documents, emails, forms, and PDFs carrying a handful of fields someone has to read out and type in. This is the clearest AI win we see, and the least discussed.
Teams already using AI badly
Licences bought, nobody trained, everyone quietly pasting customer data into a consumer chatbot. Getting this under control is often more urgent than any new project.
Where we'd tell you not to use AI
- Problems where the rules can be written down completely. If a decision follows a definite procedure, code is cheaper, faster, and auditable — a model adds cost and uncertainty for nothing.
- Processes still changing every few weeks. Automating a moving target means rebuilding repeatedly.
- Cases where the data needed simply doesn't exist. No model compensates for information that was never captured, and we'd rather establish that in a call than in month three.
How an AI engagement runs
Assessment before build, and a proof of concept before anything expensive. The sequence is the point — it's what keeps a failed idea cheap.
- 1
Find the candidates
1-2 weeksWe look at how the work actually gets done and identify where AI would change the economics — versus where an integration, a rule, or a process change does the job better and cheaper. You get a shortlist with the reasoning attached.
- 2
Prove the risky one
2-6 weeksFor anything genuinely unproven on your data, we build a narrow proof of concept with success criteria agreed in advance. Run against your real records, not a clean sample, because clean samples are how AI projects produce results that don't survive production.
- 3
Build and integrate
Scoped per projectThe working system, connected to the software you already run, with a human review step wherever a wrong answer would matter. Accuracy is measured against your current process rather than against a benchmark, because that's the comparison that decides whether it was worth doing.
- 4
Train the team and hand over
OngoingSessions with the people who'll use it, on your data and your processes, plus clear guardrails for customer and employee information. Tools nobody was trained on are the single most common form of AI waste we encounter.
Common questions
- Where does AI actually pay off in a business like ours?
- Almost always in one place: turning unstructured input into structured data. Reading a supplier invoice, classifying an inbound request, pulling specifications out of a document, matching records where the rules resist being written down. It's unglamorous and it's where the measurable return lives. Most of the rest of what gets marketed as AI is either automation that predates AI or a demo.
- Is our data safe? Can we keep it in Canada?
- Both are design decisions we make at the start rather than afterwards. Depending on sensitivity that can mean self-hosted or private models, Canadian data residency, or restricting the model to a narrow task with no retention. For regulated clients we map where data is permitted to go before proposing an architecture, because a design that ignores the constraint has to be thrown away later.
- What accuracy should we expect?
- The honest answer is that it depends on the task and your data, and any firm quoting a number before seeing either is guessing. What matters more is the comparison: your current manual process also has an error rate, and it's usually higher than people assume. We measure against that baseline, and we design a human review step wherever being wrong would be costly.
- Do we need to replace our existing systems?
- Rarely. Most of what we build sits alongside your ERP, CRM, or accounting package and feeds it. Replacing working software to accommodate an AI project is an expensive way to increase risk, and we'd argue against it in most cases.
- Will this replace people on our team?
- In our experience it redirects them. The Ontario businesses we work with are generally short-staffed rather than over-staffed, and the recovered hours go to work that was already being deferred. We'd rather set that expectation than promise headcount savings we can't stand behind.
- How do we start?
- Start with a free introductory call. If you need help deciding what to do first, AI Discovery & Roadmap costs CA$2,000–$4,000, with scope and price agreed before work begins. Discovery includes a basic proof-of-concept app for at least one opportunity identified during the engagement. Production implementation is quoted separately. If you already have a clear specification, we can discuss the appropriate delivery engagement.
Have a workflow an AI agent could help with?
AI Discovery & Roadmap: CA$2,000–$4,000. Scope and price agreed before work begins. Discovery includes a basic proof-of-concept app for at least one opportunity identified during the engagement. Production implementation is quoted separately.