Built an AI-powered ops dashboard that cut a 30-person team's manual review time by 78%
A growth-stage company had 30 ops analysts manually triaging customer transactions. We built an LLM-assisted review console with human-in-the-loop controls — same accuracy, a fraction of the time.
Client identity withheld. All figures, timelines, and tactics are from a real engagement.
What changed in 6 weeks.
Avg review time per case
-78%
Analyst-AI agreement rate
Shadow-mode
Daily throughput
+4.7x
Cases requiring escalation
-7.3 pts
What they were up against.
- 1
30 analysts spending 6+ hours/day on repetitive transaction review
- 2
Existing internal tooling was a spreadsheet plus a Retool patchwork
- 3
Needed full audit trail — every AI suggestion logged and reversible
- 4
Couldn't slow down ops during rollout; team had a daily SLA to hit
How we shipped their product.
Step 01
LLM-assisted, human-decided
Built a structured-output pipeline that pre-classifies each transaction and surfaces evidence. Analysts approve or override in one click — never blind-accept.
Step 02
Audit-first data model
Every AI call, prompt, evidence snippet, and analyst decision logged. Reversal flow built in from day one.
Step 03
Shadow-mode rollout
First two weeks ran the AI in shadow alongside the current process. Measured agreement rate before letting it touch real decisions.
Step 04
Custom-built console
Replaced the spreadsheet patchwork with a focused queue UI — keyboard-driven, sub-100ms interactions, designed around the analyst's actual day.
What it meant for the business.
Freed up ~14 analyst-hours/day for higher-judgement work
Cut ops hiring plan by 12 heads in the following quarter
Audit trail passed a third-party compliance review with no findings
Internal team now extends the console themselves using our handoff repo
"We thought AI would replace the team. Envert helped us build something better — AI that makes the team 5× faster without removing their judgement."
— Director of Operations, growth-stage fintech
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