Are you an automation agency, or a WordPress agency?

Both, and the honest version is that the automation work grew out of running our own operations. We build the workflows that sit between systems: lead routing, data sync, reporting that assembles itself, approvals and handoffs. Most automation agencies do not know WordPress internals, and most WordPress agencies do not run production workflow engines. If your automation has to touch a WordPress install, that overlap is the reason to talk to us rather than either one alone.

When should we use AI automation versus a normal workflow?

AI fits when the input is unstructured (text, images, audio) or the decision needs judgment that does not fit a rules engine. Everything else is faster and cheaper as deterministic code. We are aggressive about keeping AI to the steps that actually need it.

n8n versus Temporal versus custom code?

n8n when the operations team needs to read and edit the workflow. Temporal when durability and long-running state matter. Custom Node services when neither fits and you want full control. We mix all three based on the actual workflow.

How do you handle AI failures inside a workflow?

Retries with exponential backoff. Fallback to a smaller model or cached response. Human approval for steps where the AI confidence is low. Hard fail with a Slack alert when nothing else works. The workflow never silently corrupts data.

Can you replace our existing Zapier or Make scenarios?

Yes, and we usually do. Zapier and Make are great for simple flows. They get expensive and brittle past 10 steps or when AI judgment enters the picture. We migrate them to n8n or Temporal with full state recovery.

How do you handle data privacy in a workflow?

PII filtering before AI calls, configurable data residency, no training opt-in, audit log on every step that touches customer data. Your security team reviews the workflow the same way they review any service.

Automation work is estimated against scope rather than sold at a list price. A single trigger with one action and a pipeline across five systems with retries and alerting are very different projects. The section above sets out what moves the number.

Service AI Engineering

Workflows that let humans do what only humans can.

We build durable AI workflows on n8n, Temporal, and custom Node services. Your operations team gets back the hours they spend on classification, routing, and triage.

Start a project See our work

Projects are scope-dependent. Free discovery call.

Why this matters

Most AI automation projects ship as a fragile demo and stay there.

The Zapier flow runs for two weeks then breaks at 3am. The intern wires up ChatGPT and leaves the company. The workflow that should free 10 hours a week instead burns 10 hours debugging. We build automation that survives the first quarter, with the durability, observability, and human-in-the-loop controls that make it safe to actually trust.

One workflow running

A 3-step automation from one human ask.

Workflows we ship usually look like this: one webhook fires, the agent does the boring middle steps, and a human sees the outcome they care about. Below is a real workflow we deploy for SaaS clients. Scroll up and back down to replay.

You

When a new WooCommerce order comes in, enrich the customer in our CRM and notify the right rep in Slack.

workflow_create trigger:"woocommerce.order.created", steps:4 running

workflow_id wf_acme_orders_v3

steps 4 (3 auto, 1 human)

avg_cost_per_run $0.012

avg_duration 2.4s

Workflow Agent

4 step types sync · agent · approval · webhook · all observable from day one
Idempotent replays do not double-charge or double-notify · production-safe by default
Cost per run auditable to the cent · finance sees the dashboard, not a surprise bill

n8n for visual orchestration

Operations teams own the workflow graph. Engineers own the custom nodes. Both can read what the workflow does at 3am without paging the other.

Non-engineers safely edit workflows after launch.

Temporal for durability

Workflows that run for hours or days survive deploys, restarts, and AI outages. State is checkpointed. Retries are deterministic. No "lost in the middle" runs.

Workflows that finish, and say so plainly when they do not.

AI as one step, not the whole workflow

AI handles the judgment calls. Deterministic code handles the data plumbing. The workflow does not collapse when the model returns nonsense.

Workflows survive model regressions and outages.

Human-in-the-loop where it matters

Approval steps for high-stakes actions. Slack and email notifications wired in. The AI never sends the email, files the refund, or merges the PR without your operator seeing it first.

Audit trail covers every consequential action.

Observability across the workflow

Every step traced, every AI call costed, every retry logged. Dashboards by workflow, by tenant, by step. Debug is grep, not asking ChatGPT what went wrong.

Mean time to debug a failed run under 15 minutes.

Versioning + rollback by default

Workflow definitions are code, kept in git, deployed with the same pipeline as the rest of your services. Roll back a workflow change in 30 seconds, not 30 minutes.

Rollback time matches the rest of your stack.

Resumable

steps, so a provider outage pauses a run instead of losing it

Idempotent steps, a dead-letter queue for what cannot complete, and an alert to a person rather than a silent failure.

workflows/onboard-customer.ts ts

    
      
          1
          // workflows/onboard-customer.ts
        
          2
          import { defineWorkflow, retry } from '@temporalio/workflow';
        
          3
          import * as activities from '../activities';
        
          4
           
        
          5
          const { createWorkspace, generateOnboardingPlan, sendWelcomeEmail, scheduleFollowUp } =
        
          6
            retry(activities, { maximumAttempts: 3 });
        
          7
           
        
          8
          export const onboardCustomer = defineWorkflow(async (customerId: string) => {
        
          9
            const workspace = await createWorkspace(customerId);
        
          10
            const plan = await generateOnboardingPlan(customerId);
        
          11
            await sendWelcomeEmail(customerId, plan);
        
          12
           
        
          13
            for (const milestone of plan.milestones) {
        
          14
              await scheduleFollowUp(customerId, milestone);
        
          15
            }
        
          16
           
        
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            return { workspaceId: workspace.id, milestones: plan.milestones.length };
        
          18
          });

Discovery

Two weeks. We shadow the existing manual process, identify the steps that need AI versus deterministic code, design the workflow graph, and lock the success metric.

Fixed scope, fixed price.

Build

Three to six weeks. Workflow ships behind a feature flag. AI calls cached and observable from day one. Staging runs end-to-end by week three.

Operators can run the workflow in week three.

Launch + monitor

Two weeks. Canary rollout, completion-rate dashboards live, on-call coverage during the first 30 days. Handoff docs and team training before we step back.

Your team owns the workflow at the end.

What it costs

Workflow pricing follows the number of systems and the failure paths.

One trigger and one action, and a pipeline spanning five systems with retries and alerting, are the same sentence and very different projects. Here is what moves the number.

What moves the number

How many systems Each system in the chain is another integration, another set of credentials and another way the run can fail.
How often it runs Something firing weekly and something firing on every order have different reliability requirements and different costs.
What happens on failure Retries, dead-letter handling and someone being told. Workflows without this quietly stop working and nobody notices for a month.
Whether a model is in the loop An LLM step adds judgement and unpredictability. It needs a deterministic fallback for when the answer comes back wrong.
Who maintains it A workflow your ops team can read and edit is worth more than a clever one only we understand.

Small, well-defined work is usually better handled as an hourly block than scoped as a project, and we will say so rather than inflate it into one.

Rough idea to delivery

You send the details What you want built, roughly, plus budget range and timing. The form asks for all of it
Within 4 business hours We read it and reply. Nothing is scheduled before we know what it is about
Then the call Free discovery, booked once there is enough on the table to make it worth your hour
Within 48 hours of the call A written fixed-price quote you approve before anything starts

Get an estimate

Quick enquiry

Something manual that should not be?

Tell us the process and roughly how often it runs. If it currently lives in somebody's calendar reminders, that is a good sign it is worth automating.

Prefer the full form? Start a project

Are you an automation agency, or a WordPress agency?

Both, and the honest version is that the automation work grew out of running our own operations. We build the workflows that sit between systems: lead routing, data sync, reporting that assembles itself, approvals and handoffs. Most automation agencies do not know WordPress internals, and most WordPress agencies do not run production workflow engines. If your automation has to touch a WordPress install, that overlap is the reason to talk to us rather than either one alone.
When should we use AI automation versus a normal workflow?

AI fits when the input is unstructured (text, images, audio) or the decision needs judgment that does not fit a rules engine. Everything else is faster and cheaper as deterministic code. We are aggressive about keeping AI to the steps that actually need it.
n8n versus Temporal versus custom code?

n8n when the operations team needs to read and edit the workflow. Temporal when durability and long-running state matter. Custom Node services when neither fits and you want full control. We mix all three based on the actual workflow.
How do you handle AI failures inside a workflow?

Retries with exponential backoff. Fallback to a smaller model or cached response. Human approval for steps where the AI confidence is low. Hard fail with a Slack alert when nothing else works. The workflow never silently corrupts data.
Can you replace our existing Zapier or Make scenarios?

Yes, and we usually do. Zapier and Make are great for simple flows. They get expensive and brittle past 10 steps or when AI judgment enters the picture. We migrate them to n8n or Temporal with full state recovery.
How do you handle data privacy in a workflow?

PII filtering before AI calls, configurable data residency, no training opt-in, audit log on every step that touches customer data. Your security team reviews the workflow the same way they review any service.
What does it cost?

Automation work is estimated against scope rather than sold at a list price. A single trigger with one action and a pipeline across five systems with retries and alerting are very different projects. The section above sets out what moves the number.