We design and ship the unglamorous infrastructure that quietly saves your operators hours every week — workflow automation, ETL pipelines, API integrations, and the observability layer that tells you when something drifts before customers notice. Not every problem is an AI problem. A lot of the highest-ROI work we do is a well-designed pipeline and a few hundred lines of glue code.

What we build

Our automation practice covers the spectrum from deterministic workflow plumbing to AI-augmented exception handling. We pick the right tool for each layer — and we’ll tell you when an AI agent is the wrong answer.

  • Workflow automation. Multi-system processes that today require a human to copy data between tabs. Order intake, invoice processing, expense routing, internal request triage, onboarding sequences. Built to run unattended with audit trails and exception paths.
  • ETL and data pipelines. Scheduled extracts, transformations, and loads across your CRM, ERP, billing platform, support tools, and warehouse. Idempotent, monitored, and instrumented so a failed run doesn’t silently corrupt downstream reports.
  • API integrations. Bidirectional sync between systems that were never designed to talk to each other. Webhook plumbing, retry budgets, dead-letter queues, schema reconciliation. The integration work that vendors quote in weeks and we ship in days.
  • Observability. Logs, dashboards, alerts, and SLO tracking for every workflow we ship. The single most underbuilt layer in business automation — and the one that determines whether you find out about a broken integration from a metric or from an angry customer.
  • Hybrid agent + RPA architectures. When a workflow has both deterministic and judgment-heavy stages, we layer AI agents on top of rule-based execution. The decision framework is laid out in our breakdown of AI agents vs. RPA — and the answer is almost always “use both, in the right places.”

The cost driver in this category is integration depth, not workflow count. A single integration to a well-documented modern API takes days. A multi-system reconciliation pipeline against legacy ERPs with inconsistent schemas takes weeks. Discovery surfaces the real number before you commit.

Who it’s for

This service fits operators who have an obvious efficiency problem and a willingness to fix the underlying process — not just paper over it with software.

  • You have repetitive multi-system work that’s costing real labor hours and producing real error rates. The four-question framework in AI agents vs. RPA is a good filter: structured input, no judgment required, stable process, high volume = pure automation territory.
  • You’re running a workflow on email and spreadsheets that should be running on APIs. Most early-stage businesses cross this threshold somewhere between Series A and Series B. The signals are usually a manual reconciliation that someone does every Friday, or a Slack thread that exists only to coordinate handoffs.
  • You have an existing RPA or Zapier footprint that’s hitting its ceiling. Brittle bots that break monthly, exception rates north of 25%, integration costs that don’t amortize. We diagnose the portfolio and rebuild the highest-pain workflows on durable foundations. The migration logic is covered in our pilot-to-production breakdown.
  • You need an agent and pipelines — most production AI deployments are 30% agent and 70% surrounding automation. We build both.

If your only goal is “do something with AI” without an attached operational problem, we’re the wrong team. We don’t run innovation theaters.

How we engage

Same four phases as our other practices — discovery, build, ship, operate — calibrated to the lower variance of deterministic work. Most automation engagements run four to ten weeks end-to-end.

  1. Discovery (1 week). Workflow mapping, system inventory, integration audit, exception-rate baseline, success metrics. We produce a written architecture document and a fixed-price build estimate. The output of discovery is also useful even if you never build with us — several clients have used the audit alone to fix process problems internally.
  2. Build (2-6 weeks). Pipeline development, integration plumbing, observability instrumentation, exception handling. We ship in working increments with weekly demos against real data, not synthetic test cases.
  3. Ship (1 week). Shadow-mode deployment, comparison against the existing manual or RPA process, cutover when the error rate clears the threshold. Rollback plan written before the cutover, not after.
  4. Operate (ongoing, optional). Monitoring, schema-drift handling, integration updates when vendor APIs change. Many clients run this themselves after handoff; others keep us on a quarterly retainer to handle the long tail.

We don’t take engagements where the underlying process is fundamentally broken. Automating a broken process produces a broken process running faster. Discovery surfaces this — the scaling AI agents post covers the systemic version of the same trap.

Our automation work draws on the same operational discipline we publish on. Start here if you want to evaluate how we think about workflow design: