AI Agent Development Company in Ottawa

Ranges, not a fixed number, because scope drives everything and an honest quote follows a scoping exercise. A contained proof of concept covering one agent, three to five tools, a grounded retrieval layer, an evaluation suite and a hosted demo behind single sign-on typically runs CAD 45,000 to 95,000 over six to ten weeks. A production agent in a real workflow, with guardrails, an approval console, observability, an action log and the Canadian compliance documentation an internal review will demand, typically runs CAD 150,000 to 425,000. A multi-agent platform with shared memory, role-specialised agents, custom evaluators and integration into systems of record runs CAD 425,000 to 1,100,000 and up. Senior AI engineering rates in the Canadian market sit under Toronto and well under the United States coastal hubs for equivalent seniority, and a buyer budgeting in USD picks that difference up again on the exchange rate. Federal work carries its own cost driver that has nothing to do with engineering: security assessment and authorization, privacy impact assessment and Algorithmic Impact Assessment effort can add twenty to thirty percent to a first engagement and much less on the second, because the artefacts are reusable. We quote fixed fee against a signed statement of work.

AIDA is not law. The Artificial Intelligence and Data Act was the third part of Bill C-27, the Digital Charter Implementation Act, introduced in the 44th Parliament. It never advanced past study at the House Standing Committee on Industry and Technology, and it died on the order paper when Parliament was prorogued on January 6 2025. Treat any vendor claim of AIDA compliance as a warning sign. What actually governs an Ottawa agent build today is a stack of instruments. PIPEDA governs personal information handled in commercial activity, and because Ontario has no general private-sector privacy statute of its own, PIPEDA applies directly to Ottawa businesses rather than being displaced the way it is in Alberta, British Columbia and Quebec. The Treasury Board Directive on Automated Decision-Making binds federal institutions and requires a published Algorithmic Impact Assessment. Ontario's Personal Health Information Protection Act, 2004 governs clinical data. OSFI expectations apply to federally regulated financial institutions. Ontario employment standards have required disclosure when artificial intelligence is used to screen, assess or select applicants in publicly advertised job postings since January 1 2026, under amendments made by the Working for Workers Four Act, 2024. Sectoral rules, not a single AI act.

The Algorithmic Impact Assessment is a mandatory questionnaire under the Treasury Board Directive on Automated Decision-Making. It applies to federal institutions when an automated system is used to make or assist an administrative decision about a client, meaning a decision about entitlement, eligibility, benefits, permissions or obligations. It produces an impact level on a four-point scale, and the level determines the obligations: peer review, notice to affected people, explanation of the decision, human intervention requirements, recourse channels and publication of the completed assessment. A retrieval agent that only summarises internal policy for a public servant generally sits low on the scale. An agent that ranks or triages applications, even as a recommendation, climbs quickly, because assisting a decision counts. We draft the assessment during discovery rather than after the build, for a practical reason: the impact level dictates whether the architecture needs a mandatory human-in-the-loop gate, a reason-code output the department can put in a decision letter, and a recourse path. Retrofitting those into a finished agent is far more expensive than designing for them. Provincial and municipal bodies are not bound by the federal directive, but many Ottawa-area public bodies adopt the same assessment as internal practice.

Yes, and this is the most common Ottawa constraint. Two patterns cover almost every case. The first keeps the whole stack in a Canadian cloud region: Amazon Bedrock in ca-central-1 or Azure OpenAI in Canada Central for inference, embeddings and vector storage in the same region, with ca-west-1 in Calgary or Canada East in Quebec City for in-country failover so a disaster recovery plan does not quietly break the residency clause. That satisfies most commercial and Crown corporation requirements, provided the contract pins region and the architecture blocks cross-region model routing. The second pattern removes hosted models entirely, which is what Protected B document estates and defence-adjacent Kanata customers usually require: open-weight models on private GPU capacity inside the customer's own network or datacentre, local embedding models, a self-hosted vector store, self-hosted tracing and guardrail classifiers running locally. Nothing leaves the perimeter. The cost is capability headroom against frontier models, which we manage with tighter retrieval grounding, task decomposition into steps a mid-size open model handles reliably, and fine-tuning on the customer's own data. We document the residency decision so it survives an audit.

Every tool the agent can call gets classified before a line of orchestration code is written. Read-only tools need no gate. Reversible writes, such as creating a draft or a private note, get post-hoc review and alerting. Irreversible writes get a mandatory pre-call human approval gate, and high-impact actions get two approvers. In Ottawa the high-impact list is specific: anything that becomes an administrative decision about a member of the public, any outbound communication to a citizen or customer, any network configuration change in a carrier environment, any financial movement, any write to a health record, and any hiring or screening action, which now carries an Ontario disclosure obligation of its own. The approver sees a structured payload showing the agent's intent, the evidence it retrieved, the predicted effect and the policy it is invoking, and approves or rejects in one click. Separately, we treat prompt injection as a live threat rather than a theoretical one, because an agent that reads untrusted documents can be instructed by them. Retrieved content is fenced and never granted tool-calling authority, tool arguments are validated against schemas and allow-lists, and the action log records approver identity, timestamp and outcome.

Not geography, and it is worth being precise about what does. Agent work is integration work: connecting to document stores, ticketing systems, case systems and APIs, writing orchestration and guardrails, then evaluating and red-teaming the result. None of that improves by being done in the same building, and the evaluation work in particular is better when it runs continuously against your real corpus rather than in a workshop. Three things genuinely do constrain a remote engagement, and we raise all three during scoping rather than after contract. First, network access: if the systems the agent must read sit behind a departmental or carrier perimeter with no external path, the build needs either a jump host arrangement your security team approves or an on-premises development environment, and that decision changes the schedule. Second, personnel security screening: a federal engagement that requires Reliability or Secret status for anyone touching production data is a screening problem, not a travel problem, and where it applies we structure delivery around a cleared partner. Third, data classification: Protected B and above usually means the environment comes to us as a controlled enclave, not the reverse. Codazz is a Canadian company headquartered in Edmonton with a delivery team in Chandigarh, contracting in Canadian dollars under Canadian law.