A domain-tuned AI assistant that quietly removes friction.
A domain-tuned AI assistant embedded into an existing operational workflow. The assistant answers questions, drafts responses and automates repetitive steps — surfacing the right context at the right moment.
Reduce the time operators spend on repetitive, low-leverage work — so they can focus on decisions humans should actually make.
The team's daily work involved copy-pasting between tools, re-typing similar messages, and hunting for information across systems. Every hire had to re-learn the same tribal knowledge.
Mapped the top 20 repetitive tasks and estimated time spent per week.
Interviewed operators to understand tone, edge cases and the risk of automation.
Compared LLM providers on cost, latency and grounding quality for the target use cases.
Designed an assistant interface that lives inside existing tools, not next to them.
Prototyped prompts, guardrails and fallback behavior with real operator conversations.
Shaped the automation surface — triggers, actions, review — with a human in the loop.
A carefully scoped AI layer: an assistant panel with grounded answers, one-click draft generation for common messages, and a small library of automations operators can trigger explicitly. The system defaults to transparency — it always shows sources and what it's about to do.
Responses cite the underlying documents and records.
Common replies drafted with the right context injected.
Every automation step is reviewable before execution.
Scoped access, redaction, and safe defaults for sensitive data.
Automating too aggressively erodes trust — pacing matters.
Balancing quality with a budget operators would use daily.
A working assistant embedded into daily operations.
Measurable time saved on repetitive drafting and lookup tasks.
A pattern the team can extend to new domains without rewriting the foundations.