Common questions before you scope a pilot

• Is Cogni Circuit a cognitive-wellness / brain-training brand, or do you guarantee the AI will be error-free?

  No. We are an applied-AI automation studio that designs and builds AI assistants, agents and workflow automations for client organizations — the ".life" TLD is branding only and this is NOT a wellness, brain-training or health product, and NOT an AGI project. AI systems can make mistakes and require human oversight; we do not guarantee accuracy, uptime, cost savings or specific business outcomes — results depend on data, scope, integration and use.

• What kind of company is Cogni Circuit?

  Cogni Circuit Labs is an applied-AI engineering studio based in Vancouver, British Columbia. We design, build and deploy AI assistants, autonomous agents, retrieval systems and workflow automations for client organizations — typically B2B engagements with IT, operations or product teams who need production-grade systems rather than internal experiments.

  We are consultants and engineers, not a software product you log into. Deliverables are custom to your environment: integrations, infrastructure, runbooks and training. We work under statements of work with defined milestones.

• Which industries do you serve?

  Most of our work sits in professional services, construction and property, municipal and regional government, financial operations and mid-market SaaS companies. The common thread is document-heavy workflows, multiple backend systems and a need for auditability — not consumer mobile apps or e-commerce storefronts.

  If your use case is a strong fit, we will say so in discovery. If a simpler automation would serve you better, we will recommend that instead of upselling a large language model.

• How long does a typical pilot take?

  Discovery sprints run two weeks. Build phases for a focused pilot usually span six to ten weeks depending on integration count, data cleansing requirements and security review cycles. Complex enterprise deployments with multiple environments can extend beyond that; we quote timelines after discovery rather than before we have seen your stack.

• Do you work remotely or on-site?

  Remote delivery is our default and works well for most build phases. We offer on-site workshops in Vancouver and the Lower Mainland for kickoffs, threat-modelling sessions, operator training and executive readouts. Travel outside BC is available by arrangement.

• Where is our data processed and stored?

  We follow your data residency requirements. Many Canadian clients prefer Azure Canada Central or AWS ca-central-1. Model API calls may transit to provider regions documented in your architecture decision record; we surface this explicitly during discovery so privacy and InfoSec teams can sign off before build starts.

  Personal information handling aligns with PIPEDA and, where applicable, BC PIPA. See our Privacy Policy for website-specific practices.

• Can you integrate with our existing tools?

  Yes — that is most of the job. We routinely connect to Microsoft 365, Google Workspace, Salesforce, ServiceNow, SharePoint, Snowflake, Postgres, REST and GraphQL APIs, message queues and custom internal services. If an API is undocumented or brittle, we factor integration risk into the statement of work.

• What does a pilot cost?

  Discovery sprints are fixed-fee. Build phases are quoted after discovery based on scope, seniority mix and timeline. We do not publish a rate card because integration surface area varies too widely — a single-source RAG assistant is a different shape of work than a multi-system agent with write access to production records.

  Contact us with a short description of your workflow and we will suggest a realistic entry point.

• Do you offer ongoing support after launch?

  Yes. Retainers cover monitoring, prompt and index updates, incident response and quarterly reviews. We also deliver full handoff packages so your team can operate without us. Many clients start with a retainer for six months post-launch, then transition to internal ownership.

• How do you handle AI accuracy and hallucinations?

  We combine retrieval grounding, confidence thresholds, human review gates and automated evaluation suites. No generative system is suitable for every decision type — we scope human-in-the-loop steps where mistakes would be costly. We document known failure modes in runbooks rather than implying the model is infallible.

• Who owns the intellectual property?

  Custom code, prompts, indexes and documentation created specifically for your engagement are assigned to you under terms in the statement of work. We retain rights to general-purpose libraries and patterns developed prior to or independently of your project. Third-party model and cloud licences remain subject to their respective providers.

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