AI Agent Development Services We Offer in Halifax
Agent work around Halifax divides cleanly into three buckets and we scope each differently. Ocean and marine operators, including firms working with the Ocean Supercluster, tenants at the Centre for Ocean Ventures and Entrepreneurship, and Dalhousie research groups, need agents that read vessel tracking feeds, observer and logbook reports and weather forecasts, then draft a routing recommendation, a compliance exception or an inspection summary for a human to approve. Insurance, benefits and financial services teams need document extraction with structured handoff, claim triage, and underwriting or broker support copilots that respect PIPEDA and the model risk expectations set out in OSFI Guideline E-23. Public sector bodies and grant-funded firms need internal copilots over policy manuals, reporting assistants and citizen-facing assistants that are aware of Freedom of Information and Protection of Privacy Act obligations, because in a public body every record an agent creates is potentially disclosable. In all three we scope against the real escalation path and the real tool inventory rather than against a generic chatbot pattern, and we say plainly when the workload is deterministic enough that a scheduled job or a workflow tool would do the same thing for a fraction of the cost.
Our AI Agent Development Development Process
Discovery, design, build and deployment all run on Atlantic Time so Halifax product owners get synchronous standups rather than overnight handoffs. Discovery opens with a use case map that separates deterministic automation, which belongs in a workflow tool or a stored procedure, from genuine agent work, where tool selection, planning and reflection earn their compute cost. Where the work touches ocean data under federal fisheries rules, defence-adjacent supply chains, or provincial health information governed by the Personal Health Information Act, we run a privacy and residency review before a single token is spent. Build sprints are two weeks and every one of them ships three artifacts alongside the code: an evaluation suite with scored test cases, prompt regression tests wired into CI, and a tool registry checked into the repository so nobody has to guess what the agent can reach. Deployment adds guardrails, rate and spend limits, and human-in-the-loop gates on any action that moves money, changes a vessel's course or affects an insurance decision. We ship a rollback plan and a trace export an internal audit team or a grant reviewer can read without a second vendor engagement, because the most common reason an agent pilot dies in this market is that nobody could explain to a risk committee what it actually did.
Process Discovery
1-2 WeeksWe sit with the people doing the work in {city} and record the real process — including the exceptions they handle by instinct, which are exactly what kill naive automations.
Tool Surface Design
1-2 WeeksEvery system the agent touches gets a typed, permission-scoped tool with its own rate limit and rollback path. The agent gets a narrow set of verbs, never raw admin access.
Build & Evaluate
3-6 WeeksThe agent is built alongside its evaluation suite from day one, using real tasks from your business with verified outcomes. Every change is scored before it ships.
Shadow Mode
2-3 WeeksThe agent runs against live traffic but commits nothing. We compare its proposed actions to what your team actually did and tune until agreement is high enough to trust.
Staged Autonomy & Run
OngoingAutonomy is released by risk band — reversible actions first, irreversible ones keeping a permanent human gate. Then we monitor completion rate, escalations, latency and spend.
Technologies We Use for AI Agent Development
Orchestration defaults to LangGraph or Pydantic AI where clients need self-hosted control, and to hosted endpoints from OpenAI, Anthropic or Cohere where the privacy review clears them. For marine and ocean workloads we integrate vessel tracking feeds, Environment and Climate Change Canada weather data, and Fisheries and Oceans Canada logbook and observer formats, which are the data sources that decide whether an ocean agent is useful or decorative. Memory and retrieval run on PostgreSQL with pgvector where residency matters and on a managed vector store where it does not. Observability runs through LangSmith, Langfuse or Arize, and every production agent ships with a trace export pipeline feeding whatever log stack the client already operates, so agent behaviour lands in the same place as the rest of their telemetry. For insurance and benefits work we pin model versions rather than tracking a moving alias, log every tool call with its arguments and result, and keep inference inside AWS ca-central-1 or Azure Canada Central so a privacy officer is not asked to approve an undocumented cross-border transfer after the fact. Evaluation runs on scored datasets built from the client's own historical cases, not on synthetic prompts.
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