Skip to content
ARANEUX

AI services

AI workflow automation

Hours of copy, paste and chase turned into flows that run on their own, with AI steps where judgement is needed.

Book a free callSend a brief

In short

AI workflow automation connects your tools so repetitive work runs by itself: lead routing, invoice entry, document intake, weekly reports. A language model handles the steps that need judgement. We build in n8n, Make, Zapier or code on Temporal, and the first flows are usually live in about four weeks, running next to the manual process until the numbers match.

Automate the work nobody wants, and keep the parts that need a person.

Most businesses have a handful of workflows that eat hours every week: leads copied from forms into the CRM, invoices keyed in from PDFs, reports stitched together on Friday afternoons, follow-ups that slip. Classic automation handled the predictable steps. Language models now handle the ones that need a bit of judgement.

We build automations in whatever fits your team: n8n or Make when you want to see and edit the flow yourself, Zapier for quick wins in SaaS-heavy teams, and Temporal, Inngest or plain queues when the workflow is business-critical and needs retries, audit trails and real error handling.

Every flow has a clear owner, logs you can read, and an alert when something fails, so automation doesn't become a new place for work to silently disappear.

Is this for you?

You'll get the most out of this if

  • Someone on your team spends hours a week moving data between tools
  • Inbound leads, orders or tickets wait in an inbox before anyone touches them
  • You already use n8n, Make or Zapier and the flows have become fragile
  • Documents arrive as PDFs and get typed into a system by hand
  • You want weekly reports that write themselves

Deliverables

What you walk away with

  1. 01

    Automations that run every day

    Flows connected to your real tools (CRM, email, Slack, accounting, spreadsheets, databases) with retries and error handling built in.

  2. 02

    AI steps that are checked

    Classification, extraction, summarisation and drafting steps with structured outputs, validation and a human review queue for low-confidence cases.

  3. 03

    Monitoring and alerts

    A failed run pings the right person with the reason, instead of failing silently for a week.

  4. 04

    Docs your team can follow

    A plain-English map of every flow, what triggers it and how to change it, so you're not dependent on us.

Timeline

How the project runs

Typical timings for a first live version. We confirm yours in writing after discovery.

  1. Week 1

    Map the work

    We list the repetitive workflows, estimate hours saved for each and pick the two or three with the best return.

  2. Weeks 2 to 3

    Build

    Flows built and tested on real data, with AI steps validated against examples from your business.

  3. Week 4

    Run in parallel

    Automations run alongside the manual process until the numbers match. Then the manual process retires.

  4. Ongoing

    Expand

    New flows each month on a retainer, or a handover so your team can build the next ones.

Usual stackn8n, Make, Zapier, Temporal, Inngest, Trigger.dev, OpenAI, Claude, Structured outputs, Webhooks, Node.js, Python, PostgreSQL

Why a small studio

The person who scopes your project is the one who builds it, and the one who picks up when it breaks at 2am.

The founder is on your project
Hussain leads every build himself. You won't brief an account manager who briefs a developer who briefs someone else.
Three people, the output of ten
We build with Claude, Codex and our own agent tooling every day. A small senior team now covers what used to need a department.
We stay after launch
Evals before every release, a log of each AI decision, cost limits and someone on call. Month six matters more to us than demo day.
Your repo, your cloud
The code sits in your GitHub and runs in your AWS, GCP or Azure account from the first commit, on tools your next hire already knows.
Projects shipped
115
since 2014
Clients served
53
US, UK, EU, Middle East, APAC
Average rating
5.0
113 of 113 rated jobs
Years shipping software
18
the founder, since 2008

Case studies

Where we've done this before

FAQ

Questions people ask

n8n, Make or Zapier: which should we use?

Zapier is fastest to start for SaaS-to-SaaS flows. Make is cheaper at volume and handles branching well. n8n can be self-hosted, which matters for privacy and cost at scale. For critical workflows we often write code on Temporal or Inngest instead.

Can you fix our existing automations?

Yes. We audit what's there, fix what's fragile, add error handling and alerts, and document it. Sometimes the right fix is moving one flow into code.

How much time will we save?

We estimate hours saved per workflow before building and measure it afterwards. Most first projects pay back within a few months; we only build the ones where the numbers work.

Is it safe to let AI make decisions in a workflow?

For low-risk steps, yes, with validation. For anything costly or customer-facing we add a confidence threshold and a human review queue, so uncertain cases go to a person.

Keep exploring

Start here

Let's scope yours.

Send a few lines about the workflow or product. You'll hear back from Hussain within a working day with questions, a rough shape and a ballpark.

Book a free callhello@araneux.com