AI & RAG adoption: when it actually works
We don’t sell “AI for AI”. We start from process reality: where automation saves time, what data exists, and what guardrails you need — then we pilot and scale.
Discuss an AI initiativeWhere AI tends to pay off
Knowledge search, support automation, document workflows, sales/ops assistance — high-volume repetitive work with structured inputs.
Where AI is a bad bet
No stable process, tiny datasets, or requirements for perfect accuracy without human oversight — we’ll say so before you spend budget.
Our approach
Process audit → economics → pilot → scale. If the first two steps don’t clear the bar, we don’t start building.
RAG: knowledge search
RAG connects an LLM to your documents so answers cite sources instead of relying on memory. Typical time savings: 40–60% on internal search workloads — when data quality is there.
Related Cases
FAQ
What should be ready before implementation?
Access to documents, a described workflow, and baseline metrics for the current state.
How do you control answer quality?
Metrics, logging, data contour boundaries, and periodic evaluation — see also the controlled AI contour pillar page.
Ready to discuss?
Leave a request — we'll audit your process, calculate ROI, and propose a pilot scenario.
Discuss an AI initiative