cashcrown // ai.agents
watches metrics, price changes or mentions and pings only when it actually matters.
You need “monitoring & alerting agent”, but building it in-house gets stuck on integrations, maintenance and lack of time — and the result tends to be fragile and hard to scale.
watches metrics, price changes or mentions and pings only when it actually matters. We deliver it as part of “Autonomous agents & automation”: a working system with observability, safety gates and documentation. Models are always reached through the router — we mask PII before it leaves for the cloud.
We break the real flow down into steps, data and decision points.
We define the scope, tools and gates; we wire in the LLM router.
Plan → execution → verification (log/test), with rollback.
Observability, alerts, a gradual widening of autonomy.
Only within the scope you set — through API contracts and permission gates. Every action is logged and reversible.
Model access goes through the router; we mask PII before anything leaves for the cloud, and we handle sensitive paths locally (self-hosted LLM + BGE-M3).
With an audit of a single process and a pilot. We show a working agent before we ask for your trust.
We work in ranges that depend on scope — the entry point is a fixed-cost pilot with one measurable KPI. If a process eats a dozen to several dozen hours a month, the deployment usually pays back in 2–4 months. Calculate the return in our ROI calculator.
Yes — we design compliance in from the start: the agent introduces itself as AI (transparency), irreversible actions pass through a confirmation (human-gate, human oversight), and every step is logged. We mask PII before the cloud; profiling or decisions about people add a DPIA.