Close-up overhead shot of a terminal screen displaying a live AI agent output log, green text on dark background, clean studio light from the left, a mechanical keyboard partially visible at frame bottom
Close-up overhead shot of a terminal screen displaying a live AI agent output log, green text on dark background, clean studio light from the left, a mechanical keyboard partially visible at frame bottom
/ Case Studies

Shipped agents. Specific problems. Measured results.

Every project below documents the friction point, the agent architecture, and what runs now. No vague outcomes.

Three completed builds

What broke. What was built. What runs.

74%

11 min

3.1×

Workflow automation
Decision-support agent
Customer-facing system

Invoice routing agent

Deal-risk scoring agent

Support triage agent

Accounts payable team processed 400+ invoices weekly by hand. Agent now classifies, routes, and flags exceptions without human triage — 74% reduction in manual touches.

Sales team spent hours pulling CRM signals before forecast calls. Agent surfaces a scored risk summary per deal in 11 minutes, replacing a half-day analyst workflow.

Tier-1 support queue averaged 48-hour first response. Agent resolves 61% of tickets autonomously; escalations carry full context. Response throughput increased 3.1×.

Across all builds

What the work produces

100%

< 6 wks

0 hand-offs

Every agent deployed to production, not handed off as a prototype or spec document.

Average time from problem definition to a running agent in the client's stack.

Problem scoping through deployment is owned end-to-end. No third-party build teams, no spec documents passed on.

Bring a specific problem. We scope from there.

If your team is doing repetitive work that shouldn't require a person, that's a scoping conversation worth having.