AI workflow that replaced three roles
A multi-step AI workflow now handles work that previously required three full-time staff — freeing the team for higher-value work.
Timeline: ~6 weeks · Discovery → shadow run → cutover
The problem
Three full-time staff spent the majority of every week on the same loop: receive document → read → classify → route → log.
Volumes were growing faster than the team could scale, but hiring more people for repetitive work was not the answer the leadership team wanted.
The work was not strictly low-skill — context mattered — but the patterns were learnable from existing examples.
Our approach
- Mined the team's historical decisions to build a labeled dataset from their past routing choices.
- Designed a multi-step LLM pipeline: extract → classify → route → human-review-when-uncertain. Confidence threshold tuned with the team so edge cases still escalate to a human.
- Wired the pipeline into existing tools (no new dashboards to learn) and shipped behind a feature flag for a two-week shadow run before going live.
- Built a small review console for the team to spot-check decisions and feed corrections back into the system.
The result
The pipeline now handles the bulk of the volume autonomously, with a small human-review queue for edge cases.
The team was redeployed to client-facing work — the headcount cost on the function dropped sharply without anyone losing their job.
New document types onboard in days, not months, because the model picks up patterns from a handful of examples.
They built an AI workflow that now does what three people used to do manually. We re-deployed the team to higher-value work and our monthly headcount cost on that function is a fraction of what it was.
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