The automation agencythat routes, retries, scores, monitors, compoundsprocesses that move revenue.
An automation agency that ships no-code workflows on Make, n8n and Zapier — with LLM steps inside when they make the process smarter, not when they make the demo cooler. Ops-grade: retries, idempotency, structured logs, kill switch on every external write.
ActiveCampaign
Adalo
AdCreative.ai
Ahref
Airtable
Allo (The Mobile First Company)
Anthropic
Apify
Apollo.io
Attio
Base44
Baserow
Brevo
Bright Data
Browse AI
Bubble
CaptainData
ChatGPT
Claude
Claude Code
Claude Cowork
Clay
Clickup
Cursor
Deepseek
Dust
ElevenLabs
Fillout
Flutterflow
Folk CRM
Freepik Spaces
Gamma
Gemini
Glide
Grok
HiggsfieldAutomation that actually runs in prod stands on 4 pillars.
Most automation projects die in maintenance: the orchestrator broke after an upgrade, the API changed, an edge case piled up partial failures nobody saw. The stack we deploy in 2026 closes the four gaps that kill workflows before they pay back.
- Audit
Process scoring before building
We score every candidate process on volume (how often it runs), variability (how stable the input is) and value (how much time or money it costs you today). Most teams have 5 to 10 obvious automation wins they never spotted, plus a list of pet ideas where automation would cost more than the manual work. Audit before code, always.
How we pick what to automate - Build
No-code on Make, n8n, Zapier
We pick the orchestrator per use case, never per fashion. Make for visual flows with deep app catalog, n8n when ops needs self-hosting or branching logic, Zapier when speed-to-deploy matters more than power, Workato or Tray when SOC2 and enterprise scale enter the room. The runtime serves the process, not the other way around.
See the orchestration stack - AI inside
LLM steps in the workflow
Modern automation has LLM steps inside: Claude or GPT-4o classifying an inbound ticket, drafting a reply, extracting fields from a PDF, scoring a lead. The model runs as a node, costs are tracked per call, evals validate the output before downstream actions fire. Automation that thinks, not automation that just routes data.
See AI-augmented automation - Monitoring
Logs, alerts + SLA
Every workflow ships with structured logs (success / partial / failure), a Slack alert on N consecutive failures, a daily volume + cost dashboard, and an SLA target you signed off on. A scenario that breaks at 3am surfaces in your inbox at 8am with the failing payload — not a month later when sales realizes nobody got the lead.
What we monitor
What a workflow in production actually moves.
- −75%Manual time on the process
Across the 3-5 workflows we typically ship on a mission: CRM hygiene, lead routing, invoice extraction, report generation, ticket triage. The team handles only the edge cases the automation defers.
- $80Avg cost per workflow / month
Make + Claude on a medium-traffic scenario (1-3k runs/month, 1-2 AI calls per run). We benchmark every deploy. If unit cost drifts above $0.10/run the dashboard alerts us before the invoice does.
- 3-5 wkFirst workflow in prod
From audit to a workflow running unattended in your stack. Week 1 audit + scoring. Week 2-3 build + integration. Week 4-5 internal beta + eval + prod deploy with monitoring wired.
Our 4-step build, from process to production.
Every workflow gets the same treatment regardless of whether it lives in Make, n8n, Zapier or a custom service. Discover, design, build, deploy — with monitoring wired into the build, not bolted on after.
- Discover · score every candidate process on volume, variability and value
- Design · scenario blueprint, error handling, AI eval set if relevant — all signed before code
- Build · workflow wired in the right orchestrator with retries, idempotency, structured logs
- Deploy · monitoring + alerts + SLA, runbook handed to ops, kill switch on every external write
Workflows that survive month 3, not POCs that die in month 2.
Half the automation work we see in audits is a graveyard of scenarios that broke months ago and nobody noticed. We treat every workflow as production software: retries, idempotency, monitoring, alerts, runbook, SLA. The handover ends with your ops team owning the workflow on paper, not us being needed to restart it.
- Every workflow has retries, idempotency keys and a kill switch on external writes
- LLM steps versioned and cost-tracked separately from the orchestration cost
- Slack alerts on N consecutive failures, daily volume + cost dashboard, monthly SLA report
- Runbook handed to your ops team — they own the workflow after the handover, not us
We score your candidate processes, you leave with a plan.
Before quoting anything, we spend 60 minutes mapping the processes that deserve automation and ranking them on volume, variability and value. You walk away with a ranked candidate list and the design draft of the first workflow — yours to ship in-house or with us. Zero pitch, just an outside look at what to automate first.
- Process scoring on every repetitive process you flag
- Top 3 candidates with rough cost-to-build and expected ROI
- Scenario blueprint for the first workflow (trigger, steps, error handling)
- Honest take on the processes where automation would be worse than manual
How we run an automation engagement.
Five steps, in order, no skip. We don't open an editor before the scenario blueprint is signed, we don't deploy without monitoring wired, and we don't bill a retainer before the first workflow is running unattended. Every step has a DOD and you approve before we move to the next.
- Step 1 · Process audit
Audit which processes deserve automation (and which don't)
We sit down with the teams that actually run the work — sales ops, support, ops, finance, marketing — and score every repetitive process on three axes: volume (how often it runs), variability (how much the input shape changes), value (how much time or money it costs you today). Most teams have 5 to 10 obvious automation candidates they were too close to spot. We also flag the processes where automation would be a worse-than-manual solution. You walk away with a ranked candidate list and three quick wins to ship within 30 days.
- Step 2 · Scenario design
Design the workflow before you build it
Scenario blueprint drafted in plain English: the trigger, the steps in order, the data shape at each stage, the external systems touched, the error handling for each failure mode. If an LLM step is involved, we add the prompt, the schema constraints, the eval set (30-80 input/output pairs) and the cost ceiling per run. None of this is code yet. The doc is signed off by an operator on your side before we open an editor.
- Step 3 · Build with retries
Build the workflow with retries, idempotency and structured logs
Orchestrator picked per use case: Make for visual flows with deep app catalog, n8n when ops needs self-hosting or branching logic, Zapier when speed matters more than power. Every external write gets an idempotency key. Every API call gets a retry policy with backoff. Every step logs success / partial / failure into a queryable store (Supabase, BigQuery, or the orchestrator's native logs). If an LLM step is in the loop, we wire eval-on-prompt-change and cost-per-call tracking from day one.
- Step 4 · Deploy with monitoring
Deploy with monitoring wired, not as an afterthought
Slack alerts on N consecutive failures (configurable per workflow), Pagerduty or Opsgenie escalation if the workflow is critical, a daily volume + cost dashboard you can check whenever. Kill switch on every external write (a single flag we can flip to pause the workflow if it starts misbehaving). Runbook with the failure modes, the rollback procedure, and the on-call contact handed to your ops team in writing.
- Step 5 · SLA + monthly review
Run the SLA, watch the cost, iterate every month
Volume tracked per workflow per day, error rate per workflow per week, cost per run per month, SLA pass rate per workflow per month. Monthly review with us: which workflows to extend (new edge cases handled), which to retire (volume dropped, ROI no longer there), which to migrate (a better orchestrator or model just shipped). Workflows get sharper with the months, they don't decay silently in the background.
The same engine, across multiple client workflows.
The frames below come from real monthly review calls with clients running workflow fleets in production: SLA pass rate, volume trends, cost-per-run, queue of new processes to automate next, and the workflows we're retiring because the ROI dropped. Same operational rigor across industries — B2B SaaS, services, e-commerce ops, finance back-office.
- Monthly SLA review with every client running 5+ workflows in prod
- Cost-per-run dashboard updated daily, no quarterly slide deck
- A failing workflow triggers an alert in Slack within 30 minutes, not the next morning
- Trustpilot reviews come from the ops teams running the workflows, not from us
The 10 questions we get asked on every call.
Make, n8n, Zapier — which orchestrator should we use?
Depends on the constraint. Make for teams that want a visual interface and the broadest app catalog, with reasonable pricing at medium volume. n8n for teams that need self-hosting (data residency, on-VPC), advanced branching logic, or want to avoid vendor lock-in. Zapier for teams that want the fastest deploy and don't mind paying per task at scale. Workato or Tray when SOC2, audit logs and enterprise governance are non-negotiable. We pick per use case, often deploying a single team's workflows across two runtimes if that's what makes sense.How much does an automation agency cost in 2026?
Depends on scope. A focused mission (3-5 workflows audited, designed, built and deployed) runs $8,000 to $25,000 depending on integration complexity. A monthly retainer covering 8-15 workflows in production (extensions, evals, monitoring, model migration if AI is involved) starts around $4,000-$8,000/month. Watch out for agencies that charge by workflow count without auditing whether the workflows actually move the needle. Our approach: free audit first, then price per workflow shipped, not per hour talked.Should we automate everything that's technically automatable?
No. Automating a process that runs 10 times a month with 200 different input shapes costs more in maintenance than doing it by hand. We score every candidate on three axes (volume, variability, value) and only automate where the math says yes. The flagging of pet ideas where automation is worse than manual is part of the audit deliverable. Saying no to bad automation candidates is part of the job.What's the difference between an automation agency and an AI agency?
An automation agency wires processes together so data flows between your tools without manual touch. An AI agency builds LLM features that read, generate or decide things in your product. The two overlap in 2026: modern automation has LLM steps inside (a Claude call to classify a ticket inside a Make scenario), and modern AI features run on top of automation infrastructure (an MCP server fronting your tools). We do both because they share the same DNA: scoring, design, build, monitoring.How long to ship a first workflow in production?
Honest answer: 3 to 5 weeks for a first workflow on a well-scoped use case. Week 1 audit + scoring. Week 2-3 design (blueprint, error handling, eval set if LLM is involved). Week 4-5 build + integration + internal beta + prod deploy with monitoring. If an agency promises a workflow in 1 week, they're skipping the design doc and the eval — fine for a demo, dangerous when the workflow starts writing to your CRM.Will automation replace our team?
Augments. Every workflow we ship has an escalation path back to a human operator — for the edge cases, the angry customers, the high-value deals. What changes: the team stops doing the 80% of repetitive work the workflow handles and refocuses on the 20% that needs judgment. On the cohorts we've shipped: sales ops moves from CRM data entry to building the playbook, support L1 moves from copy-paste replies to fixing the root cause that generated the ticket.What happens when a workflow breaks?
Three layers of catch. (1) Retry policy with exponential backoff on every external API call. (2) Structured logs into a queryable store, so a partial failure is visible in the dashboard the same day. (3) Slack alert on N consecutive failures (default 3), Pagerduty or Opsgenie escalation if the workflow is critical. Runbook with the failure modes and rollback procedure. We do not deploy a workflow without a kill switch on every external write — we can pause the workflow in 30 seconds if it starts misbehaving.Can we run automation workflows on our own infrastructure?
Yes. n8n self-hosted on your VPC or on-premise is the most common pattern for teams with data residency or compliance constraints. Make and Zapier are SaaS-only but offer EU data residency and zero-data-retention modes on their enterprise tiers. For workloads where data legally can't leave your perimeter (finance, defense, healthcare), n8n self-hosted + on-premise LLM inference via vLLM is the standard. We size cost and operational overhead honestly before recommending self-hosting.What tools and systems do you wire workflows to?
Tool-agnostic. We've shipped workflows wired to HubSpot, Pipedrive, Salesforce, Attio, Folk, Airtable, Notion, Zendesk, Intercom, Slack, Gmail, Outlook, Stripe, Linear, GitHub, Webflow, Shopify, Postgres, BigQuery, Supabase, and custom internal systems via REST APIs or webhooks. If you have a documented API and webhooks, we can wire automation to it. Custom integrations get built behind a thin wrapper so swapping the orchestrator later is a 1-day job, not a 1-month migration.How long do we commit for?
Three formats. (1) Audit only: flat fee, 2 weeks, deliverable is the ranked workflow candidate list and the design doc for the first 1-2 workflows. (2) Build sprint: 4 to 8 weeks per batch of 3-5 workflows shipped, fixed scope, fixed price. (3) Ongoing retainer: 6-month minimum for teams running 8+ workflows in production who want continuous monitoring, extensions, and model migration when AI is involved. No forced annual contract, no convoluted exit clauses. If we don't ship, you stop.
Stop pitching the automation roadmap. Ship the first workflow.
A 60-minute audit, three candidate processes scored, one workflow designed. If your team should build it in-house, we'll say so and hand over the design. If we're a better fit, we ship in 3 to 5 weeks.