Practical AI: where LLMs actually earn their keep

AI is our newest service line, and we're honest about that. That also means we've watched a lot of AI projects up close, and the ones that deliver share a pattern.

Where LLMs pull their weight

• Extraction: turning messy documents and emails into structured data.
• Summarization and search over content people already own.
• Drafting inside a human-in-the-loop workflow, not fully autonomous action.

Where teams get burned

Trouble shows up when an LLM is put in a position where being confidently wrong is expensive and there's no verification step. The fix is rarely a bigger model; it's a tighter scope, real evaluation, and a fallback path.

Deployable and operationally sound beats impressive-in-a-demo every time.

Our approach

We treat AI features like any other production system: measurable success criteria, evaluation harnesses, and observability. If we can't measure whether it's working, we don't ship it.