Every team has a process that eats up hours because someone has to look at each item one by one and decide what to do with it — flag this order as suspicious, escalate this support ticket, approve this record, reject that one. It's repetitive, it's judgment-heavy, and it doesn't scale as volume grows.

I build AI agents that take the first pass at exactly this kind of work. Instead of a rigid rules engine (if X then Y), the agent actually reasons about each case using an LLM — reading the context, applying the same logic a trained team member would, and explaining its decision in plain English so nothing feels like a black box.

A live dashboard connected to your real data (not a mockup)

Each item reviewed with a clear, human-readable explanation of why it was flagged, approved, or needs attention

One-click action buttons (approve / flag / escalate / dismiss) so your team acts immediately, without switching tools

Full visibility into every decision the agent made, so it stays auditable

Proof of work — Anomaly Review & Action Console: I built exactly this pattern for fraud/anomaly detection: incoming records get scored using Isolation Forest (a statistical anomaly-detection model), then Groq/Llama 3.3 generates a plain-English explanation for each flagged case, and reviewers act on it directly from the dashboard — no spreadsheet digging, no separate tools. That's the live example shown here.

Fraud or anomaly review (transactions, orders, records)

Support ticket triage and prioritization

Content or listing moderation queues

Data quality checks before it hits your main system

Lead scoring and qualification

How we'd work together: You tell me the process you're currently doing manually and what "good" looks like for a decision. I scope the exact fields/data involved, build the agent and dashboard around your real data (or a representative sample), and deliver something your team can start using — with the reasoning visible, not hidden.

If your team is still eyeballing spreadsheets or clicking through records one at a time to make the same kind of call over and over, this replaces that first pass — so your team spends time on the judgment calls that actually need a human, not the repetitive ones.