AI automation that survives contact with real data

It finds the repetitive work inside your operation and builds systems that do it. In practice that's three things: workflow automation connecting the tools you already use, AI agents that handle judgement-based tasks like triage and drafting, and data pipelines that keep everything in sync. The output is hours returned and errors removed.

Zapier for simple, low-volume connections your ops team maintains alone. n8n once you need branching logic, custom code, high volume or data that can't leave your infrastructure. It self-hosts, versions in git and costs far less at scale. We build on n8n and host it in your accounts.

By constraining it. Agents answer from your retrieved documents rather than model memory, tools are scoped to specific permitted actions, and anything low-confidence or high-stakes routes to a human. Before launch we run an eval set of real cases and set a pass threshold. Every response is logged and reviewable.

Not on the paid API tiers we build on. OpenAI and Anthropic both state that API inputs and outputs aren't used to train their models, retention is a short abuse-monitoring window, and zero-retention agreements are available for qualifying accounts. Where data can't leave your infrastructure at all, we design around self-hosted n8n and open models instead.

We plan for it, because retirement is routine rather than rare. Anthropic commits to at least 60 days' notice for a publicly released model and OpenAI to at least six months for generally available ones, after which requests to that model simply fail. So we pin versions, keep prompts and evaluation sets in your repository, and re-run the evals on the replacement before switching.

We baseline the metric before building (hours spent, tickets handled, meetings booked, error rate) and instrument the automation to report against it. You get a dashboard, not a status update. If a workflow silently stops firing because something upstream changed, an alert tells you the same day.