Field Notes
These posts capture the playbooks, instrumentation, and rituals we rely on when standing up AI copilots, voice agents, and finance automations.
Last month, a 40-person recruitment agency asked us why their best people spent half their day hunting for information. Six weeks later, anyone in the company could ask a question and get an instant answer pulled from every system they use. This is how we built it.
Takeaway:You do not need a data warehouse or a team of analysts. You need an AI layer that speaks human and knows where all your stuff lives.
How we architect an AI voice agent that screens inbound calls, qualifies insurance leads, and routes warm prospects to sales without losing the human touch.
Takeaway:Voice assistants excel at top-of-funnel screening when you define qualification criteria upfront, instrument every handoff, and keep humans in the loop for complex cases.
How we scope, budget, and ship a pilot that proves automation value without torching credibility with the teams who live in the workflow.
Takeaway:Treat new copilots like production change by instrumenting the business process before you write the first prompt.
Voice AI sounds impressive until the first handoff goes sideways. This is how we keep call quality, compliance, and customer sentiment trending up as agents learn.
Takeaway:Voice agents perform like junior employees; they need clear guardrails, coaching rituals, and instrumentation to stay on brand.
LLM copilots are only as trustworthy as the ledger they read from. We set lightweight data contracts so finance leaders know what changed and why.
Takeaway:Short, explicit data contracts align CFO trust with builder speed by clarifying which tables, refresh cadences, and owners support AI forecasts.
