FAQ
Applied AI implementation — common questions
Straight answers on scope, process, and how we engage. For a tailored view of your situation, start a discovery conversation.
Do you only build proofs of concept?
No. Our focus is production-ready systems: monitoring, evaluation, rollback paths, and operational ownership. Pilots are used to de-risk specific unknowns, not as the end state.
What AI capabilities do you implement?
Typical work includes recommendation and ranking, NLP and document workflows, computer vision on edge or server, predictive models for operations, and RAG-style knowledge systems — always tied to measurable decisions.
How do you approach governance and responsible AI?
We document intended use, failure modes, human review points, and data handling early. Technical controls align to your risk and regulatory context rather than generic checklists.
Can models run on our infrastructure?
Yes. We design for your deployment constraints — cloud, VPC, or on-prem — and factor latency, cost, and maintenance into the architecture from the start.
What do you need from us to start?
Access to stakeholders, representative data or environments where available, and clarity on success metrics. If metrics are unclear, discovery focuses on defining them before large build commitments.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context — we will identify the highest-leverage intervention areas before any scope is agreed.