AI Workflow Automation

Get back the timeyou're already paying for

The repetitive work your team does every day, copy-paste between systems, file conversions, chasing follow-ups, is salary you already spend. We automate it, one process at a time, at a fixed price, with the saving measured from day one.

Calculate your time savings Book an assessment

Built on n8n. Automation first, AI only where it pays. You decide with your own numbers.

One workflow

TriggerA file lands

RulesConvert, check

AI stepRead, classify

Write backInto your system

AI is a node, not the workflow

01 / The platform

Enterprise-grade capability, at SMB scale, on n8n.

Between no-code tools that stop short of real integrations and enterprise platforms that cost tens of thousands a year, there is an uncovered middle. n8n sits exactly there: open and self-hostable, billed per execution rather than per seat, and able to orchestrate APIs, AI models and human approval steps in the same workflow. Choosing it is not a bet on a startup, either. The numbers below show a platform that is funded, adopted at scale and independently rated.

$5.2B
valuation in 2026, with a strategic investment from SAP
SAP
invests in n8n and embeds it natively in Joule Studio
150k+
stars on GitHub
400+
ready integrations

Sources: n8n, SAP investment and $5.2bn valuation (blog.n8n.io, 2026); GitHub n8n-io/n8n.

Independent benchmark

Among the top tools for building AI agents

A 2026 technical evaluation of 14 platforms scored n8n 72/100 on capability and 54/100 on enterprise-readiness, ahead of iPaaS incumbents like Make (41/46) and Workato (42/54) at actually building agents.

Source: n8n, "AI agent development tools 2026", analyst A. Green.

The same platform, at any size

Mercedes-Benz runs n8n company-wide

Around 164,000 people, from R&D to production, sales, HR and IT, on the same platform an SMB can start from for about $20 a month.

Source: blog.n8n.io/mercedes-benz-n8n/, 2026. Deployment by the customer, not by FlorenceNext.

02 / The problem

Inefficiency is not a future cost. It is money already leaving.

22%

of working time in SMBs goes to repetitive tasks

Source: Osservatorio Intelligent Business Process Automation, Politecnico di Milano, 2026

It never appears on a budget line, yet you pay it in full every month, in hours your team spends moving data by hand instead of doing the work only they can do. The encouraging part: most of this is exactly the kind of structured, rule-based work that software does faster and without mistakes. The six categories below are where it usually hides.

Data entry
Re-typing what a system already has
Documents and invoices
Converting, renaming, filing
Reports
Stitching data from several sources
Follow-ups
Remembering who to chase, and when
Email triage
Sorting, routing, replying
Scheduling
Confirmations and reminders

03 / Try it

1Answer five quick questions
2See time and value saved
3Get the report by email
4We reach out

How much time can you get back?

Five quick steps: pick your sector, tick the repetitive tasks you recognise, say how long they take and how often, and see the hours, and the euros, you could recover. Ask for the full report and we will send it over, then help you turn the top candidate into a working automation.

FlorenceNext · Automation ROI

Sector
Tasks
Time
Team
Result

Step 1 of 5

How much time can you recover?

In a few steps, estimate how many hours and how much money your company can free up by automating repetitive tasks. No technical detail needed, just answer as best you can.

What's your sector?

Average gross hourly cost

The average gross hourly pay of the people who do these tasks (salary plus contributions). Not sure? Just leave the default.

04 / Success stories

Automations already in production, and what they gave back.

Four real projects, anonymised by sector. Different industries, different problems, one method: start from the highest-value repetitive task, use AI only where it earns its place, and put a working system into production, not a demo. The figures are measured, not projected.

Insurance claims, automotiveNo AI

Claims document pipeline

For every hail-damage claim, an operator unzipped documents and photos, converted everything to size-capped PDFs, created a correctly named folder on Drive and uploaded the files one by one. Slow, mechanical, easy to get wrong. We replaced it with a single authenticated n8n form: the operator enters the plate and drops two zip files; the workflow converts the files, creates the dated, correctly named Drive folder, uploads everything and returns a direct link. An operation that took half an hour is now one upload. No AI needed here. The value is a robust document pipeline, and that is the point.

hours / month recovered: 18 to 20hours / month recovered
time per claim, now under 3 min: −90%time per claim, now under 3 min

B2B manufacturingAI where it pays

Lead qualification pipeline

The sales team researched every inbound lead by hand: who the company is, whether it is in target, how much the contact is worth. We built an n8n pipeline that enriches each lead automatically, with real-time web research on the company, then classification and scoring against the criteria the client sets. Sales receives the lead already qualified, with context and priority. The AI runs on a small, fast model on purpose. For structured classification it is cheaper and just as reliable, so the cost per lead stays in the order of a few cents.

hours / month recovered: 15 to 18hours / month recovered
time per lead, now under 1 min: −95%time per lead, now under 1 min

Luxury hospitalityAI on a leash

Pre-arrival check-in agent

Check-in at a five-star property meant repetitive pre-arrival exchanges with guests about documents, arrival times and special requests, where tone and reliability matter as much as speed: a luxury guest must never feel they are talking to a bot that has gone off the rails. We built a conversational agent on n8n as a state machine: every conversation is always in a defined state, and transitions are explicit and controlled, so the AI is natural where that helps and never leaves its assigned perimeter. Deterministic where it must be, conversational where it adds value.

conversations handled end to end: ~8 in 10conversations handled end to end
hours / month freed at the front desk: ~20hours / month freed at the front desk

Logistics and transportNo AI

Weekly follow-up report

Follow-up contacts were slipping through the cracks. Nobody had a clear view of who to chase, or when. We built an n8n workflow that queries the contacts database, finds every company stalled in follow-up for more than seven days, and emails a complete HTML report with company, contact and status straight to the inbox each Monday. No dashboard to remember to open; the information arrives where the salesperson already works. Not everything needs AI. Sometimes the right automation is a well-written query and a punctual report.

forgotten follow-ups since go-live: Zeroforgotten follow-ups since go-live
hours / month of manual checks saved: 3 to 4hours / month of manual checks saved

Real projects delivered with the n8n method, anonymised by sector. Figures are measured on each system in production.

Let's talk

Let's find your first automation.

Half a day with the people who do the work. You leave with a prioritised roadmap and the first automation already chosen, and if you like, already measured.

Book an assessment

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