Workflow & Business Process Automation

The repetitive admin nobody owns — reconciliation, compliance checklists, screening, inbox triage — running as pipelines that hold up.

Discuss this See example work

Time to value: First workflow in production in 2–3 weeks.
Deployment: Can run fully on your infrastructure
Category: AI & Intelligent Automation

A person facing a wall of monitoring screens in a control room

The manual version of this

Invictt AI layer

Map

Instrument

Automate

Guard

Hand over

A man relaxed at his desk with a laptop, hands behind his head

Once it's AI-assisted

01/The problem

Every business has a handful of processes held together by one person's spreadsheet and their memory of the exceptions. Too small to justify enterprise software, too important to keep doing by hand, and a genuine risk the week that person is on holiday.

02/How it works

The operating model

What enters, what the system does with it, and what reaches you.

System

Map

We sit with whoever actually does the work and write down the real process, including the exceptions that never made it into the documented one.

Check

Instrument

Before automating, we measure: volume, time per item, error rate, where it queues. This is what the business case gets built on, and what we measure against afterwards.

System

Automate

Built as versioned n8n workflows with custom Python or FastAPI services wherever the logic outgrows a node graph — which it usually does at the interesting parts.

Check

Guard

Retries, dead-letter queues, idempotency, and alerting on silent failure. An automation that fails loudly is fine; one that fails quietly is worse than the manual process it replaced.

Human

Hand over

Documented, with runbooks, and built so your team can change a rule without calling us. We would rather you did not need a retainer.

Read the detail behind each stage

Map

We sit with whoever actually does the work and write down the real process, including the exceptions that never made it into the documented one.

Instrument

Before automating, we measure: volume, time per item, error rate, where it queues. This is what the business case gets built on, and what we measure against afterwards.

Automate

Built as versioned n8n workflows with custom Python or FastAPI services wherever the logic outgrows a node graph — which it usually does at the interesting parts.

Guard

Retries, dead-letter queues, idempotency, and alerting on silent failure. An automation that fails loudly is fine; one that fails quietly is worse than the manual process it replaced.

Hand over

Documented, with runbooks, and built so your team can change a rule without calling us. We would rather you did not need a retainer.

03/What's included

What you actually receive

Concrete deliverables, not a statement of intent.

Process mapping sessions with the people who do the work

Baseline measurement of volume, time and error rate

Versioned workflows in n8n plus custom services where needed

Retry, idempotency and dead-letter handling

Alerting on failure and on silent stalls

Runbooks and handover training for your team

Built with

n8n
Python
FastAPI
Postgres
Docker

Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.

04/Example work

All work

What this looks like in practice

Reference implementations, including what went wrong.

Reference Implementation2025

Removing manual invoice entry for a building materials distributor

Two full-time roles spent their mornings retyping supplier invoices into an ageing ERP. We replaced the typing with an extraction pipeline and a review queue — keeping the humans, moving them to the exceptions.

Read it

Reference Implementation2025

Unifying lead intake and cutting first-response time for a solar installer

Enquiries arrived across five channels into three inboxes nobody fully owned. We unified intake, enriched every lead before contact, and cut first response from many hours to minutes.

Read it

05/Also in this category