AI Automation

Operational AI, Not Demos

Most teams have a backlog of work that's well-defined, repetitive, and exactly what current AI models are good at — categorizing tickets, summarizing documents, normalizing data, drafting first-pass responses, watching feeds for what matters. We build the pipelines, queues, and retry logic to run that work reliably, 24/7, against your real systems.

This is distinct from AI Integration, which focuses on user-facing AI inside your product. AI Automation is the back office: scheduled jobs, webhook handlers, and queue workers that quietly do the work nobody wants to do manually.

What We Build

• Document and data extraction pipelines (PDFs, emails, scraped pages, scanned files)
• Classification, tagging, and routing of inbound tickets, leads, or content
• Summarization, briefing, and report generation jobs that run on a schedule
• System-to-system glue: AI that bridges tools that don't talk to each other
• Quality and review pipelines with human-in-the-loop checkpoints
• Prompt caching, batching, and cost optimization so usage scales without runaway bills
• Monitoring, alerting, and observability so you know when a job fails before your users do

Our Approach

We start with one workflow, measure it end to end, and only scale once it's genuinely working. Every automation includes evaluation against real examples, version control for prompts, and clear logs of what the model did and why — so when something goes wrong, you can find and fix it.

We default to Claude via the Anthropic SDK with prompt caching enabled, but we'll use whatever model fits the cost, latency, and quality requirements of the job.