AI Agent Development Services We Offer in Vancouver
The agent work Vancouver actually asks for splits along a content-security line. On one side are studios, biotech companies and enterprises whose material cannot reach a hosted endpoint, and for them we build fully self-hosted stacks: open-weight models on GPU hardware inside the client's own network, retrieval against internal asset management and lab systems, orchestration that never opens an outbound connection. Typical work includes shot-lookup and dailies-triage agents for visual effects pipelines, literature and assay-protocol agents for antibody discovery teams, and internal engineering agents that read private repositories. On the other side are agents that touch customers or public data, where a hosted frontier model is the right call and the work becomes governance: consent scoping under BC PIPA, disclosure design, and evaluation suites that catch regressions before a release ships. We build customer-service and member-service agents for credit unions and telecom operators with hard caps enforced in the tool layer rather than the prompt, document-intelligence agents for law firms and practice-management platforms, operations agents for port and marine logistics, and clinical-workflow agents for BC health authorities inside FIPPA controls. Every engagement produces an agent scope-of-authority document, a tool inventory with least-privilege scoping, and a written escalation protocol.
Our AI Agent Development Development Process
Vancouver runs on Pacific Time and our Edmonton team runs on Mountain Time, one hour ahead, so a 9:00 AM standup in Vancouver is 10:00 AM in Edmonton and both sides are inside normal working hours for the entire day. Our Chandigarh team sits twelve and a half hours ahead of Vancouver during daylight time, which we use for overnight builds and evaluation runs rather than for decisions. Discovery starts with a scope-of-authority workshop that answers one question in writing: what is this agent allowed to change, and what must it never change without a human. From there we run a privacy assessment against BC PIPA for private-sector clients or BC FIPPA for public bodies and health authorities, a content-security review when the client is under a Trusted Partner Network or studio audit obligation, and a residency decision that names the exact cloud region. Tools are inventoried and allow-listed before the first agent loop is written. Build sprints run two weeks with a Thursday demo at 2:00 PM Pacific. Each sprint produces an evaluation run against a client-specific gold set plus adversarial prompt-injection cases, and regressions block promotion in CI. Launch includes shadow mode, a kill-switch tabletop exercise, and action logs wired into whatever SIEM the client already runs.
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
Region choice is the first technical decision and it matters more from Vancouver than from Toronto. AWS ca-west-1 in Calgary is the closest in-Canada region to Vancouver and is our default for Bedrock-hosted Anthropic Claude and for agent runtimes where round-trip latency shows up in a chat interface; ca-central-1 in Montreal is the fallback and the pairing region for multi-region durability. Azure Canada Central in Toronto carries Azure OpenAI Service for clients already inside a Microsoft enterprise agreement, with Canada East in Quebec City for failover. Google Cloud offers northamerica-northeast1 in Montreal and northamerica-northeast2 in Toronto for Vertex AI and Gemini when co-location with BigQuery decides it. Cohere is on the table when a client wants a Canadian model vendor. For content-locked studios and biotech labs the entire stack is self-hosted: Llama, Mistral or Qwen weights served through vLLM on on-premise GPUs, with LoRA or QLoRA fine-tuning on internal data and zero egress. Orchestration is LangGraph when the workflow needs an auditable state machine, CrewAI for role-specialised teams, and Microsoft AutoGen for Microsoft-standardised shops. Tool interfaces use Model Context Protocol where the client accepts it. Retrieval sits on pgvector, Qdrant or Weaviate. Tracing runs on Langfuse or LangSmith, guardrails on NeMo Guardrails and Llama Guard.
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