Workflow mapping
We find the handoffs worth automating and preserve human judgment where it earns its place.
Automation / Operations
Make the repetitive work disappear.
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Workflow automation
Reliable workflows for the repetitive decisions, handoffs, and updates that slow teams down.
What we build
Useful from
day one.
n8nAPIsPythonLLMs
Durable integrations
Retries, idempotency, alerts, and dead-letter handling keep failures visible and recoverable.
AI where it helps
Models classify, extract, summarise, and draft while deterministic steps remain deterministic.
The route to production
Clear steps.
Visible progress.
Discover
We map the real workflow, risks, users, and success measure before choosing an implementation.
Build
Working software lands in visible increments, with the hardest assumptions tested first.
Prove
We validate against production-shaped data, failure paths, performance, and real use.
Ship
Deployment, monitoring, documentation, and a clean handover are part of the build.
Verified client reviews
In their words.
“Everything was set up perfectly and worked exactly as we needed. Super professional, easy to communicate with, and delivered right on time.”
“Very knowledgeable. Deployed my project successfully and was kind and patient throughout.”
Before you ask
Questions,
answered.
Which parts of a process are actually worth automating?
The repetitive handoffs, lookups and status updates where a human adds no judgment — and only those. We start by mapping the workflow and counting the exception rate, because a process that breaks its own rules a fifth of the time will not produce the savings anyone quoted. Judgment calls stay with people; the work around them stops needing a person to move it.
Do you build on n8n, Make, or Zapier?
It depends on the workflow. Zapier has the largest integration catalogue and the gentlest learning curve, which suits simple low-volume automations. Make is the middle ground on multi-step work. We build most production work on self-hosted n8n: flat cost as volume grows, data that stays on infrastructure you control, and real code nodes for workflows that are closer to a small program than a straight line of blocks.
What happens when a workflow fails at three in the morning?
It should recover, and if it cannot, it should say so. We build retries with backoff rather than retries alone, a dedicated error workflow that catches what the happy path drops, a dead-letter store for items that cannot be processed, and alerts written on the assumption that nobody is watching the dashboard. Silent failure is the expensive one.
How do you stop retries corrupting data?
By making the external calls idempotent. A retry is only safe if repeating it is harmless, so writes carry an idempotency key or a deduplication check and the workflow can tell the difference between a call that failed and a call that succeeded before the response came back. Without that, a reliability feature quietly becomes a data-integrity problem.
Should a workflow run unattended or with a person in the loop?
Work it out from what an exception costs. Attended automation keeps a human present for the decision and the credentials, which is right where a mistake is expensive or an approval is required. Unattended automation is cheaper and faster, and it fails quietly, which is why it needs monitoring proportional to what it touches. Most real processes end up split: unattended on the predictable path, attended on the exceptions.
Where do the exceptions go?
Into a queue that somebody owns, with the evidence attached — the input, the step that failed, and what the workflow already tried. A status column and a cron job is not state, and an exception discovered by a customer is not an exception you handled. The queue is part of the design, not a fallback we add after the first incident.
Where do you use an LLM inside a workflow?
For the parts that are genuinely fuzzy: classifying a request, extracting fields from an unstructured document, summarising a thread, drafting a reply for a human to approve. Everything deterministic stays deterministic. Putting a model in the middle of a step that a rule already handles correctly adds cost, latency and a new failure mode for nothing.
How do you handle rate limits and bursts of volume?
With back-pressure rather than hope. Work is queued and paced against the limits of the systems downstream, long-running pipelines keep their state outside the run so a restart resumes instead of replaying, and throughput is observable so a backlog shows up as a number before it shows up as a missed SLA.
Can you work inside the automations we already have?
Yes. We join existing delivery teams, take over a workstream, or come in to rescue a build that is misbehaving before launch. Inheriting a live workflow usually starts the same way: instrument it, find where it loses items, and fix the failure handling before adding anything new on top.
Who maintains it once it is live?
That has to be answered before you build. Self-hosted automation with nobody maintaining it is worse than the managed tool you left, so where we run the instance we keep it healthy, patched and monitored. Where your team takes it, the handover includes the documentation, the alerting and the runbook for the failure paths we designed.
From the journal
Further reading.
Workflow Automation as a Service: Why Most Deployments Crash and How to Fix Them8 min readThe n8n vs Zapier pricing math nobody does before the bill arrives5 min readAttended vs unattended automation comes down to who owns the exception path5 min readProcess automation dies on the exception path6 min readHiring an AI automation engineer: costs and how to vet one9 min readWhat AI automation services cost (and when to hire an agency)9 min read
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