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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.

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Projects are scope-dependent. Free discovery call.
workflows.example.com
workflows/triage-ticket.ts ts 
    
      
          
          // workflows/triage-ticket.ts
        
          
          import { Workflow } from '@/runtime';
        
          
           
        
          
          export const triageTicket = new Workflow('triage-ticket', async (ctx, ticketId) => {
        
          
            const ticket = await ctx.run(fetchTicket, ticketId);
        
          
            const classification = await ctx.run(classifyWithClaude, ticket);
        
          
           
        
          
            if (classification.urgency === 'high') {
        
          
              await ctx.run(pageOnCall, classification);
        
          
            }
        
          
           
        
          
            await ctx.run(addLabels, ticket.id, classification.labels);
        
          
            await ctx.run(routeToTeam, ticket.id, classification.team);
        
          
            return classification;
        
          
          });

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.

What we build

Workflows your operations team can trust at 3am.

Durable execution, AI as one step among many, human approval where it matters, observability across every run. We build automation you do not have to babysit.

01

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.

02

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.

Workflow completion rate above 99 percent.

03

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.

04

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.

05

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.

06

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.

99%+

workflow completion rate across our production AI automation deployments

Measured continuously. Includes runs that survive provider outages and deploys.

The runtime

Workflows as code, durable by default.

Temporal for durability when state matters. n8n for visual editing when operators own the workflow. Custom Node services when neither fits. The runtime gets out of the way so the workflow logic is what you read.

workflows/onboard-customer.ts ts 
    
      
          
          // workflows/onboard-customer.ts
        
          
          import { defineWorkflow, retry } from '@temporalio/workflow';
        
          
          import * as activities from '../activities';
        
          
           
        
          
          const { createWorkspace, generateOnboardingPlan, sendWelcomeEmail, scheduleFollowUp } =
        
          
            retry(activities, { maximumAttempts: 3 });
        
          
           
        
          
          export const onboardCustomer = defineWorkflow(async (customerId: string) => {
        
          
            const workspace = await createWorkspace(customerId);
        
          
            const plan = await generateOnboardingPlan(customerId);
        
          
            await sendWelcomeEmail(customerId, plan);
        
          
           
        
          
            for (const milestone of plan.milestones) {
        
          
              await scheduleFollowUp(customerId, milestone);
        
          
            }
        
          
           
        
          
            return { workspaceId: workspace.id, milestones: plan.milestones.length };
        
          
          });

Process

How a workflow project runs.

01

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.

02

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.

03

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.

Common questions

Frequently asked

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. What does it cost?

    AI workflow projects are scope-dependent for a single workflow with three to five steps. Multi-workflow systems with custom evals and observability are scoped after discovery. Discovery call is free.

Ready to automate the work that drains your team?

Tell us what you want to build.

Discovery call is free. Fixed-price quote within 48 hours. NDA on request.

Start a project See our work