Case studies
Seven agents. All in production.
Each of these is written the way I would want to read it: the manual process it replaced, the architecture, the thing that made it hard, the guardrail that made it safe to deploy, and what actually changed afterwards.
Incident Copilot
A ticket-triggered triage agent that pulls the right resolution procedure, the relevant logs and comparable prior cases the moment a ticket is created — then posts a recommended fix onto the ticket before an engineer has opened it.
Anonymized — Fortune 500 pharmaceutical enterprise
~50%
reduction in mean-time-to-acknowledge
Observability Copilot
Natural-language access to logs, metrics, traces and live endpoint health across a multi-application Kubernetes estate — built on a custom Grafana-stack MCP server I wrote because the one I needed did not exist.
Anonymized — Fortune 500 pharmaceutical enterprise
0
lines of PromQL an engineer has to write
Autonomous SDLC Pipeline
Three cooperating agents — Coder, Reviewer, Deployment — that let one consultant carry a client's entire production computer vision service without hand-running every fix, test and deploy.
Client engagement — computer vision application
hours → minutes
feature-to-deploy turnaround
Company Knowledge Base Agent
An enterprise-wide agent that answers questions against Confluence through MCP — no vector store, no separate index, no sync job to go stale.
Anonymized — Fortune 500 pharmaceutical enterprise
30 → 5 min
to find internal knowledge
DevOps Diagnostics Agent
A GitHub-issue-triggered agent with SSH into a production EC2 host, a hard allowlist of non-destructive commands, and no authority to fix anything it finds.
Client engagement — live production instance
Multimodal Invoice & Document Extractor
Turns invoices, PDFs, scans and photographs into schema-validated JSON using multimodal extraction — replacing template parsers that shatter the moment a layout shifts. Manual effort per document fell from about ten minutes to under one.
Anonymized — delivered engagement
10 → <1 min
manual effort per document
Content Automation Pipeline
Idea in, fact-checked post and on-brand carousel out. I built it to run my own LinkedIn presence, and it has been running daily ever since.
Built for myself — running daily
2h → 15 min
per day of content production
Limited availability
Tell me the problem.
I’ll tell you if an agent is the answer.
A 30-minute call, no pitch. Describe the manual loop that hurts and I’ll give you a straight read on whether this is worth building — including when the honest answer is a script, not an agent.
30 minutes · video call · no pitch