Custom
Workflow Automation
We map the process, automate the mechanical steps deterministically, and use AI only where judgement is genuinely required — with the error handling, monitoring and documentation that decide whether it's still running next year.
30 minutes. Bring one process that eats your week. You'll leave knowing whether it's ready to automate, what it'd cost, and whether AI belongs in it at all.
Most automation projects don't fail on the tooling
The research is consistent about this and it's worth stating plainly: technical and tool-selection issues account for under 20% of workflow automation failures. The rest are process problems. The most-cited reasons automation efforts fall short are weak change management (~35%), insufficient training (~31%), automating the wrong process in the first place (~28%), and optimistic timelines (~24%).
Three patterns cause most of it:
Undefined decision points
Any step currently resolved with "it depends" is a decision without a rule. Automate it and the workflow either picks a default — often wrong — or stalls waiting for a human, which means you've automated up to the decision but not through it.
Inconsistent inputs
If the same request arrives through three channels with different fields and different quality, the automation handles the clean ones and struggles with the rest.
Uncounted exceptions
Everyone knows their standard path. Almost nobody has counted how often the exception path is actually taken — and the exception rate determines whether the whole thing is worth building.
There's a useful test: if you can't describe the process in under ten steps without saying “it depends,” it isn't ready to automate yet. Fixing that comes first, and it's most of what our audit does.
The failure that shows up in month three
The other way this goes wrong is slower and more expensive.
A flow gets built. It works. Nobody adds error handling because nothing has gone wrong yet. Then an API times out, or data arrives malformed, or a field gets renamed upstream — and the automation fails silently. Records stop syncing. Nobody notices for two weeks, because the only signal of success was that nobody complained.
Scale that up and you get something worse. Enough undocumented workflows accumulate that no single person understands which change affects what. Teams stop modifying automations — not because they're working well, but because the risk of a cascading failure is too high. New edge cases get routed to humans by default because nobody wants to be the person who broke accounts payable two days before month-end.
This is the difference between wiring two apps together and building a system. The first is genuinely cheap. The second is what you're paying for.
About the $30 workflow
You can get an n8n workflow built on a freelance marketplace starting around $30. That isn't a scam — for a simple, low-stakes, two-app connection, it's often the right purchase, and we'll say so if that's what your problem is.
Here's what that price doesn't include, and what our build fee does:
Error handling
What happens when the API times out, the payload is malformed, or the third app is down. Retries with backoff, timeouts, and a defined failure path.
Idempotency
So a retried step doesn't create the same invoice, refund or record twice. This is the single most common silent-damage bug in cheap automation.
Monitoring and alerting
You find out when something breaks, rather than finding out from a customer.
Documentation
A written record of what runs, what triggers it, and what to do when it fails — so the process survives the person who built it leaving.
Ownership
Code and workflows you hold, on infrastructure you control. No dependency on a contractor who's since moved on.
Someone accountable after launch
[FILL: state your actual post-launch commitment — support window, retainer option, or nothing. If it's nothing, say what handover covers instead. Don't imply support you don't provide.]
If none of that matters for your use case, take the $30 option with our blessing. If you're automating something that touches money, customers or compliance, it matters a great deal.
When to use AI — and when a rule is better
The current fashion is to put a language model in every step. It demos well and it's usually the wrong engineering choice.
Use a deterministic rule when…
…the logic is knowable and stable: if the amount is over £5,000, route to finance. If the form says “enterprise,” assign to the enterprise queue. If the file is a PDF, send it to the parser. Rules are cheaper, instant, free to run, testable, and they give the same answer every time.
Use AI when…
…the input is genuinely unstructured or the judgement is genuinely fuzzy: reading a supplier invoice that arrives in a different layout every time, classifying a free-text complaint, summarising a call, extracting terms from a contract, deciding whether two records describe the same company.
Use both, in the right order.
Most real workflows are mostly deterministic with two or three points that need judgement. The right design does the cheap, reliable thing wherever possible and reserves the model for where it earns its cost — with a rule-based check on its output where a wrong answer would be expensive.
We'll tell you which steps in your process are which. It's frequently the part of the audit that saves the most money, and it's why some builds come in below what clients expected.
What we automate
Data sync between systems
CRM, billing, spreadsheets, project tools, warehouse. The unglamorous work that eats the most hours.
Document processing
Invoices, purchase orders, claims, contracts, forms. Extract, validate against your system of record, route the exceptions.
Approval chains
Routing, reminders, escalation on stall, and a record of who approved what and when.
Onboarding and offboarding
Client, employee or vendor. Accounts, access, documents, notifications, checklists.
Ticket triage and routing
Inbound classified, prioritised and routed before a human touches it.
Learn moreReconciliation
Matching records across systems that disagree, resolving what's mechanical, escalating what isn't.
Fixing automation that already broke
Inherited a flow nobody understands, or one that silently failed and cost you data? We audit it, document it, and rebuild the parts that need rebuilding — often cheaper than starting again.
We'll tell you when not to automate
Some processes shouldn't be automated yet, and saying so on the audit call costs us a sale and saves you a bad project. The honest disqualifiers:
The process changes every week
Automating a moving target means paying to rebuild it repeatedly. Stabilise it first.
Volume doesn't justify it
If it takes twenty minutes a month, the build will never pay back. We'll say the number.
Nobody agrees on the steps
Two people describing the process differently means there is no process yet, only a habit.
The exception rate is high
If the "unusual" path is taken 40% of the time, you'd be automating the minority case and adding a handoff to the majority.
The real fix is upstream
Sometimes the answer is one changed form field, not an automation. That advice is free.
How we build it
Audit to production in 2–6 weeks.
Free audit (week 1)
We map the process as it actually runs — including the exceptions people handle without thinking about them. You get back: the step-by-step map, which steps are deterministic, which need AI, which should stay human, an estimate of the hours it currently consumes, and an honest recommendation. Some audits end with "don't build this."
Readiness check
Against the failure patterns above: undefined decisions, inconsistent inputs, uncounted exceptions. Anything failing gets fixed or scoped out before we build.
Design
Trigger, steps, data flow, failure paths, escalation, and where a human stays in the loop. Approved by you before anything gets built.
Build
[FILL: your actual stack — n8n, Make, Zapier, custom code, or a mix. Name it. Buyers on this SERP search by tool, and vagueness here costs you both trust and rankings.] Deterministic steps as rules, AI only where judgement is required, error handling and idempotency throughout.
Test against real cases
Including the malformed ones and the exceptions. If AI is involved, a scored evaluation set on your real inputs.
Shadow or parallel run
The automation runs alongside the manual process and you compare outputs before switching over.
Handover and monitoring
Documentation, alerting, and a walkthrough with whoever will own it. [FILL: what ongoing support you offer, if any.]
What it costs
What moves the price: number of systems, whether the process is documented, exception volume, how much AI judgement is involved, and whether you need it hosted and monitored or handed over.
What's not included: platform subscriptions (n8n, Make or Zapier), model usage, and infrastructure — billed to your own accounts so you see real running costs. [FILL: confirm and give a typical monthly running cost range.]
What you can expect
Fully automated end-to-end workflows remain rare — one compilation puts the share of companies achieving them at around 4%. Most successful projects automate the mechanical 70–80% of a process and route the rest to a person with better context than they had before. That's the outcome worth buying.
Reported success rates run higher for SMBs than large organisations — fewer stakeholders, shorter approval chains, less legacy. If you're between 10 and 200 people, that's in your favour.
[FILL: one real deployment with client-verifiable numbers. Both specialist competitors on this SERP show concrete work. Benchmarks are honest; they aren't proof.]
Security and data handling
Hosting
[FILL: self-hosted n8n in your environment, our infrastructure, or vendor cloud — state what you offer. Self-hosting is a genuine differentiator for regulated buyers if you do it.]
Access
Least-privilege credentials per system, scoped keys, secrets never stored in plain text in workflow definitions.
Audit log
Every run recorded, with inputs, outputs and failures retained. [FILL: retention period.]
Human gates
Payments, deletions, outbound customer messages and anything irreversible sit behind approval by default.
Prompt injection
Where AI reads documents, emails or form submissions, that content is treated as data, never as instructions — relevant because document-processing workflows ingest content from outside your organisation by definition.
Ownership
[FILL: confirm clients own the workflows and code, and can take them elsewhere. If true, state it plainly — it's a real differentiator against contractors who keep clients dependent.]
Frequently asked questions
Bring us the process that eats your week
Book a free AI audit. We'll map it as it actually runs, tell you which steps are automatable, which need AI, which should stay human, and what it would cost — including an honest “this isn't ready yet” if that's the answer.