Your team is paying people to do work a machine could do tonight. We find that work, automate it against the systems you already run, and hand you the hours back — measured, not promised.
Anonymized case study — a top-5 US bank, capacity planning organization.
"They were struggling with prompt writing and drowning in manual reporting. In 45 days the automation systems were live — the team said they were lost before."
AI automations plugged into SSRS, Jira and Splunk generate capacity documents with deterministic recommendations. The flagship app saves an estimated 3,000 hours a year — about 1.5 person-years — plus standardization gains and avoided audit findings.
Client work is confidential. Engagements are clean-room builds on your systems and data — never reused code, never your data leaving your walls.
Most clients start at the top and work down. Every rung pays for the next.
Hands-on training in prompt craft and automation patterns, using your real work — not toy examples. This is also where we find your 3,000-hour problem.
A fixed-scope, fixed-price sprint — typically 2–4 weeks — replacing one painful manual workflow with an automation on your systems. Priced on the hours it returns, not the days it takes.
A monthly retainer for new automations and tooling guidance from someone who's seen your systems from the inside. Cancel anytime — the automations are yours either way.
Twenty-plus years across the exact stack this work touches.
Infrastructure capacity planning and telemetry at enterprise scale: BMC TrueSight/TSCO across 6,000+ endpoints, Prophet and scikit-learn forecasting models predicting bottlenecks months out, PySpark pipelines, AWS serverless data platforms.
Started in C/C++ and Java development, moved through performance testing into APM tools administration — CA Wily/APM rollouts of 4,000–6,000 agents, then Dynatrace AppMon and Synthetics. Knows what "slow" costs.
Builds GenAI agents and production automations daily — routinely the only person on the team to run out of AI dev tokens. This isn't a sideline.