Software development • Montreal

Chatbot and AI Agent Development in Montreal

A chatbot that answers the same forty questions at 2 a.m. is useful. An AI agent that reads every incoming request, classifies it and routes it to the right person is more useful. We build both for Montreal businesses that want to answer faster without pretending the AI is infallible.

DaniMaster is a Montreal software team that runs its own AI automation stack daily: content, email, lead flow and agents that answer and route requests. We build chatbots and agents on Claude and OpenAI models, grounded in your own documents, with human review wherever money or legal consequences are involved.

What we build

• Customer support chatbots. Chat widgets that answer from your documentation, hand off to a human when confidence is low, and log every conversation.
• AI agents for lead flow. Agents that read incoming forms, emails and chats, classify each request and route it to the right team or your CRM with fields filled.
• Internal assistants. Tools your staff use to search contracts, draft replies or query a database in plain language.
• AI software development. Full applications with AI at the core: audit generators, content tools, document pipelines and workflow automation, built in Node.js, Python and TypeScript.
• Machine learning solutions. Classification, scoring and prediction models trained on your own data when a general model is not precise enough.
• Integrations. Connections to your CRM, helpdesk, calendar, Odoo, ERPNext and Stripe, so the agent can act, not just talk.

We choose the model per task: small models for simple work, stronger ones for hard reasoning.

How we work

Discovery. We collect real examples: the last 200 support emails, chat logs, the questions your front desk hears. We sort them into what the AI handles, what it drafts for a human, and what it never touches.

Design. We write the agent's instructions, its knowledge sources, the tools it may call and the escalation rules. We define success in measurable terms, such as the share of test questions answered correctly.

Build. We connect the model to your data and systems, build the widget or back-end agent, and log every decision.

Test. We run the real examples through it and score answers with your team. We test hostile inputs too: prompt injection, off-topic requests, attempts to extract data.

Launch. We go live in stages, often with the agent drafting and a human approving at first.

Support. We review transcripts monthly, fix wrong answers at the source and update the knowledge base as your business changes.

AI we run in production

We run AI agents for our own businesses, which taught us what to automate and what to keep human.

Our automation stack handles content, email, social posting, web scraping and lead flow across several brands. Agents read incoming requests, answer the simple ones and route the rest to the right person with a summary.

On BizLocal, our Quebec and Canada business directory, a classification filter scores every quote request as real, test or spam before it reaches Odoo CRM. Sales staff see only genuine requests.

We also built a chat support widget that answers from a knowledge base and hands off to email when stuck, an audit generator that assesses a website from its URL, and a content generator we use daily.

None of these systems move money or make legal commitments on their own. Payments, refunds and contract terms always pass through a person, and we apply that rule to every Montreal client.

Costs and timelines

Costs depend on channels, how much knowledge must be prepared and how many systems the agent acts on. Ranges in Canadian dollars:

• Support chatbot answering from your existing documentation: $4,000 to $12,000, three to five weeks.
• Lead intake and routing agent connected to your CRM: $8,000 to $25,000, four to eight weeks.
• Internal assistant over your documents or database: $10,000 to $30,000.
• Custom AI application or machine learning model trained on your data: $25,000 and up, scoped after a data review.
• Model usage: typically $20 to $300 per month for a small business, billed through your own API account.
• Monthly support, transcript review and knowledge updates: $300 to $1,500.

The cheapest project is usually the one with the best source material. If your FAQ and policies are written and current, the chatbot is fast. If they live in people's heads, budget time to extract them first.

Why a Montreal AI partner

Bilingual agents. Montreal customers switch between French and English mid-conversation. Our agents detect the language, answer in it and keep the knowledge base consistent in both. We test French answers as carefully as English ones, because models slip in French if nobody checks.

Law 25 and personal data. A chatbot collects names, emails and sometimes sensitive details. We decide with you what is stored, for how long and who can read transcripts, and we are clear about which providers see what.

Same time zone. When an agent starts giving odd answers after a provider update, we notice during your business day and fix it.

Canadian hosting on request. The application, logs and knowledge base can run on Canadian servers.

Honest scope. We will tell you when a simple form, a rule-based flow or a well-written FAQ page beats an AI agent. Not every problem needs a model, and we would rather keep your trust.

Who this is for

A property manager in Montreal receives hundreds of tenant messages a month about repairs, payments and keys. An agent classifies each one, answers routine ones from the lease rules, and creates work orders for the rest.

A professional services firm gets inquiries through its website, email and a directory. A lead agent extracts service, urgency and location, scores the lead and creates it in the CRM with a draft reply for a human.

A software company answers the same onboarding questions daily. A support widget grounded in the documentation handles them and flags gaps in the docs.

A retailer has years of orders and returns and wants to predict returns. A machine learning model trained on that history flags risky orders before shipping.

If you have a stream of repetitive requests and know what should stay human, we can build the rest. Start with a free 30-minute call and bring a sample of real messages.

Frequently asked questions

Will the chatbot make things up?

Any language model can produce a confident wrong answer. We reduce the risk by grounding answers in your documents, instructing the model to say when it does not know, and scoring answers against a test set before launch. For money, legal terms or health, the agent drafts and a human decides. We do not promise zero errors.

Which AI models do you use, and who sees our data?

We build on Claude and OpenAI models through their business APIs. Under those business terms, your prompts are not used to train the models. We tell you which data goes to which provider, minimize personal data in prompts, and can keep logs and knowledge bases on Canadian servers.

Can the agent do things, or only answer questions?

It can act when we give it tools: create a CRM lead, book an appointment, open a ticket, look up an order. Each tool has limits we define, and risky actions require confirmation. Agents that act need more testing, so we launch them in stages with a human in the loop.

Do we need machine learning or is a chatbot enough?

Most businesses need a well-grounded chatbot or agent on an existing model, which is faster and cheaper. Machine learning solutions trained on your own data make sense when you have a specific prediction or classification task, enough historical examples, and a cost or precision reason not to use a general model.