AI automation that runs the work you keep redoing.
Most operations teams do not have an AI problem. They have twenty small handoffs a day that each take four minutes, never get written down, and quietly consume a full-time role. AI automation is worth doing where those handoffs are — not where the demo looks best.
We start by watching the actual workflow: who touches what, where a request waits, and which step people dread. That produces a short list of candidates ranked by hours returned per week, not by how impressive they are to describe. Some of what comes back is not AI at all — a webhook, a rule, a better form. We will tell you when that is the answer, because a deterministic fix you can reason about beats a model you have to babysit.
What is left is the work that genuinely needs judgment: reading an unstructured email and deciding what it is, summarising a thread for the person picking it up, drafting the reply that a human approves. That is where we put the models, wired into the tools your team already opens every morning.
What we build
Intake and triage
Inbound email, forms, and messages get read, classified, and routed with the context already attached — so the person who picks it up is not re-reading the thread to work out what it is.
Document and data handling
Invoices, contracts, applications and reports parsed into structured fields your systems can act on, with a confidence threshold that escalates anything ambiguous instead of guessing.
Reporting that assembles itself
Recurring client or internal reports pulled from source systems, written up, and delivered on a schedule — with the underlying numbers linked so anyone can check the work.
Follow-up sequences
The nudges that get forgotten when someone is busy: quote follow-ups, renewal reminders, stalled-deal check-ins, each aware of what already happened in the thread.
Human approval gates
Anything that sends externally, spends money, or changes a record can require a person to approve it. You decide which steps those are, and you can move the line later.
How the engagement runs
- 01
Map
We sit with the workflow and time it. You get the bottleneck list whether or not you hire us for the build.
- 02
Scope
One workflow, one measurable target, a fixed scope. Not a platform rollout.
- 03
Build
Weekly demos against real data. You see it working on your cases, not a sandbox.
- 04
Hand over
Runbooks, an escalation path, and the ability to change thresholds without calling us.
This is a good fit if
- A workflow that runs at least daily and has an owner who can describe it end to end
- Operations, support, or finance teams drowning in repetitive triage
- You have a metric you already care about — response time, cost per ticket, hours per week
- Systems with an API, or at least a stable export
We’d turn this down
- A workflow nobody can describe consistently — automating an undefined process just makes the confusion faster
- One-off data cleanups, which are usually a script and a week, not an engagement
- Anything requiring a regulated clinical, legal, or financial decision to be made without a human in the loop
Questions we get about this
- How long before an AI automation is actually running?
- Typically about six weeks from kickoff to a live workflow, with weekly demos throughout so you are not waiting until the end to see it. Simple single-step automations land sooner; anything crossing several systems with strict approval rules takes longer.
- What happens when the automation gets something wrong?
- It escalates rather than proceeding. Every workflow has a confidence threshold and a fallback path to a person, and we instrument the handoffs so you can see how often that happens and why. Silent failure is the thing that kills trust in automation, so we design against it first.
- Do you replace our existing tools?
- Almost never. Automation works best on top of the CRM, helpdesk and spreadsheets your team already uses. Replacing tooling is a separate decision with its own change-management cost, and bundling it into an automation project is how both fail.
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