People searching for AI and ML development services are not looking for a slide about the future. They want to know who will build the thing, what data they need, how long it takes, and whether it will run in production after the demo.
That is the job. PositionMySite builds custom AI and machine learning systems for operators who already have a workflow that is too slow, too manual, or too guessy. The work lives on our AI and ML solutions page. This article is the plain version of what that engagement actually looks like.
If a vendor only talks models and never talks integration, support, or retraining, you are buying a pilot. We do not sell pilots as the product.
What “AI and ML development services” should include
A real build has five parts. Skip one and the project dies in a shared drive.
- A business problem with a number. Not “add AI.” Something like: cut document handling time in half, flag the claims that will fail review, or draft the first response so a human only edits.
- A data verdict. You have enough, you have some and it is messy, or you need a collection plan. We tell you which one it is in discovery, not after you have paid for a model.
- A model that is allowed to be boring. Classical ML, retrieval-augmented generation, computer vision, or a small classifier. The cheapest thing that hits the number.
- A place it lives. API, queue, dashboard, or inside the tool your team already opens. If it is not in the path of work, nobody will use it.
- A way to keep it honest. Monitoring, retraining, and a human who owns the misses. Models drift. That is not optional maintenance. That is the product.
What we actually build
The services page lists the catalog. In practice most clients land in one of these:
- Custom AI on your workflow. A system that reads your documents, tickets, or events and does the next step, with a human in the loop where it matters.
- NLP and chat that know your business. Not a generic chatbot. Retrieval over your policies, product docs, or case history, with citations so staff can check it.
- Predictive analytics. Demand, churn, risk, or “which row needs a person today.” Useful when you already have history and a decision you make every week.
- Computer vision. Inspect, count, or flag from images or video when a human eye is the bottleneck.
- Recommendations. Next product, next article, next action, based on behavior you already log.
- MLOps. The unsexy part: deploy, watch, retrain. If you already have a notebook that works on one laptop, this is often the real project.
Mining and geology, adaptive learning, and predictive planning have their own pages when the domain is that specific. Do not start there if you just need a production assistant on your existing stack.
How a project runs
Discovery. We look at the workflow, the data, and the number you care about. You get a yes, a not yet, or a smaller first slice. We would rather shrink the first ship than promise a platform.
Data. We clean what you have or say what is missing. Structured tables, PDFs, tickets, images, or sensor streams are all fair. Volume matters less than whether the label exists for the decision you want.
Build. Model, evaluation set, and the integration. You should be able to point at a URL or an API and see it fail in a way you understand.
Deploy and support. We put it on your cloud, ours, or a hybrid. Then we watch it. Post-deploy is not a helpdesk add-on. Models change when your customers change.
Simple assistants and recommendation slices often land in 6 to 12 weeks. Vision and heavier forecasting often take 3 to 6 months. Those are ranges, not quotes. Timeline comes after we have seen the data.
What it costs, without the dance
Focused builds often start in the five-figure range. Larger systems with multiple models, strict compliance, or on-prem work reach six figures. We do fixed price when the slice is clear, time and materials when it is not, and a dedicated team when you want people embedded. If someone quotes a number before they have asked about data and integration, they are guessing.
What we will not do
We will not wrap a public chatbot, change the logo, and call it custom AI. We will not train on data you cannot legally use. We will not hide the failure cases. If the honest answer is “you need better logging for 60 days before a model will help,” that is the deliverable.
A first project that is small enough to ship
Pick one queue your team already hates. Invoices, support macros, lead scoring, inspection photos. Write the success number on a line. If we cannot name the input, the output, and who overrides the model, it is not a first project. It is a research wish.
Toronto and remote teams both work. We integrate with the stack you have: REST, webhooks, your CRM, your warehouse. Cloud, on-prem, or hybrid. The constraint is usually data access and a person who can say yes to the workflow change, not the model library.
How to start
Go to AI and ML solutions and start the project form, or book the consultation. Bring one workflow, one number, and a sentence about the data you already have. We reply with a scoped path, not a deck.
If you already know it is predictive-only or geology-specific, use those pages instead. Everyone else starts here.