AI automation services for work your team repeats

Traditional automation follows rules you can write down: when a form arrives, create a contact; when an invoice is approved, post it to Xero. AI automation adds a step that reads language. A model works out what an email is asking for, pulls a delivery date out of a paragraph or drafts a reply in your tone. Everything around that step is still ordinary, testable plumbing.

The split matters for cost and risk. Rules are cheap to run and never improvise. A model step adds a usage fee on every call and can be wrong, so it needs a test set, a schema check and a person for the edge cases.

Good builds are mostly rules, with 1 or 2 model steps placed exactly where the input gets messy. If your process has no messy input at all, you want business process automation, which is rules only and cheaper to run. If the work needs a system that plans its own steps and picks its own tools, read how agents differ from automations and chatbots before you scope anything.

Strong candidates share 4 traits. The input is text or documents. The volume is at least 30 to 50 cases a week. A person can say what the right output looks like. And a mistake can be caught before it costs much.

Processes that usually pass:

• A shared support inbox, where the workflow classifies each request, pulls the order number, drafts a reply from your help docs and routes it to the right queue.
• Quote requests arriving as emails or PDFs, matched against your catalog and turned into a draft quote for a salesperson to check.
• Supplier mail, sorted into invoices, statements and remittance advice, with invoices passed to an extraction step.
• Lead intake, where each form fill is scored, enriched and summarized in 3 lines for the rep.
• The Monday report, built from figures in 4 tools and written up the way a manager would.

Processes that usually fail: anything decided once a quarter, anything where a wrong answer reaches a customer or regulator unchecked, and any process still being redesigned. Automating a process that changes every month means paying to rebuild it every month.

During Map we score each candidate on volume, input type, error cost and stability, with the hours saved next to the build price.

You cannot make a model step infallible, so we design the workflow around that fact. Five controls do most of the work.

1. Structured output. The model returns JSON that must match a schema: a category from a fixed list, a date in ISO format, an amount as a number. Anything that fails validation goes to a person.
2. A confidence gate. Each decision carries a short reason and a confidence label, and low-confidence cases land in an approval queue in Slack or Teams. That is human-in-the-loop review in its simplest form.
3. Business-rule checks after the model. If the model reads an order as 400 units and the customer's largest past order was 40, the workflow pauses and asks.
4. An evaluation set. Every prompt or model change runs against your labeled examples before release, and a drop in accuracy blocks the change.
5. Shadow mode. For the first 1 to 2 weeks the automation runs beside your team without acting, and we compare its answers with theirs.

A single AI workflow costs $4,000 to $10,000 and takes 3 to 5 weeks. A system of connected workflows, for example inbox triage feeding quoting feeding CRM updates, costs $12,000 to $35,000 and takes 6 to 12 weeks.

What moves a project up the band:

• The systems it writes to. A modern CRM with a documented API is quick. A 15-year-old ERP behind a SOAP endpoint is slow.
• Input variety. A single web form is easy to test. Free-text requests in 4 languages need a larger evaluation set.
• Approval design. An approve button in Slack is cheap. A review screen with editable fields is a small app of its own.
• Compliance. Regional hosting, redaction and 7-year audit logs all add build time.

Running costs sit outside the build price. You pay your automation platform and your model provider directly. Changes after handover are billed by the hour, or come under a monthly care plan.

To judge payback, compare hours saved per month, times the loaded hourly cost of the people doing the work, with the build price plus monthly running cost. We measure the baseline during Map, so the sum starts from your own numbers. Our guide on measuring the ROI of AI automation walks through it.

When a rule does the job. If the decision can be written as a condition (the country is Canada, the amount is over $5,000, the field is empty), a filter in n8n or Make is faster, cheaper and never improvises. We build that instead and say so in the proposal, because it costs less to run.

AI automation is also a poor fit when:

• Volume is too low. At 20 cases a month, a $6,000 build rarely pays back. A checklist or a saved prompt might be enough.
• Nobody can define a correct answer. If 2 experienced staff disagree on how to classify a request, the model will disagree with both of them. Settle the policy first.
• The process is mid-redesign. Wait until it has held still for a quarter.
• Every output goes straight to a customer, a bank or a regulator with no chance to review. Some steps should stay with a person.

What an AI automation project does not include: subscription fees for your automation platform and model providers, cleanup of source systems beyond what the workflow needs, and customer-facing chatbots. If people need answers drawn from thousands of internal documents with sources cited, that is a knowledge system, scoped separately.