AI implementation for organisations_

AI implementation, not another AI app

Most organisations do not need one more subscription. They need AI to do real work inside the systems their people already open every morning. That is what AI implementation means at DevDino: we start from the work, not the technology, and we finish when your team is using it and the numbers show it.

We are an engineering company first. The same team that built ibon, a food-industry management platform used in over 80 Aroma branches, and a real-time production performance application for Netafim, builds your AI integrations. That matters, because the hard part of AI adoption is rarely the model. It is the connection to an ERP older than some of your staff, the permissions on a shared drive, and the edge case nobody wrote down.

It also matters who is on the other side of the table. The AI consulting market, in Toronto and across Canada, is full of small-ticket shops selling one chatbot template to everyone. We build for organisations where an outage, a privacy slip or a confused customer has a real cost: broadcasters like Keshet, manufacturers like Netafim, restaurant chains like Aroma, institutions like the Jewish Agency.

What AI can take over in your organisation

Four kinds of work are usually the best place to start.

Front desk and lead intake

An AI assistant answers calls, web chat, SMS and email the moment they arrive. It qualifies the enquiry, books the appointment or routes it to the right person, and calls back the people you missed. Nothing waits for Monday morning. We run a CRM with an AI assistant on the lead pipelines of our own businesses, so we know where an assistant helps and where a human should take over.

Back-office workflows

Intake forms that turn into records. Reminders and follow-ups that send themselves. No-show rebooking, review requests, billing nudges, and weekly reports that assemble overnight. Each one is small. Together they free skilled people for the work you hired them to do.

Document handling

Invoices, applications, contracts and forms are read, classified and entered into the right system, with a person approving anything the AI is not sure about.

Internal knowledge assistants

A private assistant that answers staff questions from your policies, procedures and past work, cites the source document, and only shows each person what they are allowed to see. Our AI development page explains how we build these with retrieval over your own documents.

Privacy, security and governance

In Canada, organisations work under PIPEDA, and those in Quebec also under Law 25, which adds duties around automated decisions and privacy impact assessments. In Israel, the Privacy Protection Law applies. We design every implementation for the rules that apply to you:

• Data mapping before build: which personal information is touched, where it travels and who sees it.
• Minimum data to the model: redaction and filtering, so a model only receives what the task needs.
• Hosting and providers chosen on purpose: matched to your data residency and contract requirements.
• Logs, access controls and retention rules your privacy officer can review.
• A human in the loop for decisions that affect people.

We design for these laws; we do not claim a certification, and your counsel signs off. What we give them is clear documentation to work from, instead of a vendor’s marketing page.

Change management and measuring results

An AI rollout rarely fails in the demo. It fails a few weeks later, when the team quietly goes back to the old way because nobody showed them the new one. So training is part of the project, not an add-on: short sessions per role, written playbooks, and a champion in each team who collects feedback and tells us what to fix.

Measurement starts before we write any code. We record a baseline for the workflow we are changing: response time, hours per task, error rate, leads answered after hours. After go-live we track the same numbers, report them to you, and use them to decide what goes next on the roadmap. If a use case is not paying its way, you will hear it from us first.

Choosing an AI implementation partner

Whoever you hire, ask these questions:

1. Will the people who scope the project also build it?
2. Can they integrate with your actual systems, or only with their own platform?
3. How will they handle personal data, and will they put it in writing?
4. What will they measure, and from what baseline?
5. Who owns the prompts, workflows and code at the end? (With us, you do.)

We have built software for clients in Canada, Israel, the USA, Europe and Australia, from offices in Toronto and Tel Aviv. Our name might be Dino, but we would rather your processes were not the fossils. For a step-by-step view, read how to implement AI in your organisation. If AI leads you to rethink how you manage customers, see custom CRM development, and if you need someone to own the whole technical picture, meet our technology partner service. Hebrew speakers can read this page in Hebrew.

Tell us which part of your organisation feels slowest, and we will show you what AI can realistically do about it.