AI Business Center
We solve the problems that weigh on the result.
Autenly chooses the technology to fit the problem: we put processes in order, design the rules of oversight over what touches money and personal data, and build solutions that stay with the teams.
Services
Three services, one discipline.
Where we usually start
The questions a conversation starts from.
Each is asked in a different company, and the answer begins the same way: with what eats the time today.
Before this
We automated processes long before it was called AI.
Years of automation that had to work on Monday morning. None of it is glamorous and all of it is the reason the current work holds.
Intelligent document processing
Reading, classifying and routing the paper an organisation runs on – invoices, orders, contracts, claims. The hard part was never the extraction; it was the exception path, the confidence threshold, and who gets called when the model is unsure.
RPA at production scale
Years of building and maintaining robotic process automation across finance and back-office operations, including the part most decks omit: what happens when the underlying system changes and two hundred robots need to keep working on Monday.
Process engineering before automation
Decomposing operational chaos into rules precise enough to hand to a machine. Most failed automation projects are failed process definitions – a bad process automated is simply a bad process running faster and at greater cost.
How it runs
One function first. Then the rest.
Diagnose
We map where the time actually goes across the function, classify what touches money, personal data or production systems, and pick the first target. You end this phase with a decision, not a deck.
Prove
One function, one working result, live. Built with your people rather than delivered over the wall, so the capability stays when we step back. This is where the case for the rest of the organisation gets made – or does not.
Scale
The model extends: more teams, more tools, the same gates. Intake, risk tiers and the library are already in place, so each new function is a repetition rather than a new project.
Before you hand anything over
A small firm has to be safer, not looser.
An NDA before anything sensitive
Signed before we see a document, a dataset or a process. Nothing about your organisation reaches us on trust alone.
We work inside your environment
On your licences, in your tenant, with your tooling. Your data stays on infrastructure you already control and already audit – we do not stand up a shadow stack alongside it.
Your data does not train anyone’s model
We build on enterprise agreements – Claude Enterprise, Gemini Enterprise and their equivalents – under which the provider does not train on customer content. Where a tool cannot offer that, it does not go near your data.
Demonstrations run on synthetic data
Training decks, walkthroughs and pilots are built on labelled synthetic records. Real records are never copied into material that will be shown around the organisation.
The first stage
Diagnosis has a closed scope and ends in a decision.
Before anything is built, we map where the time actually goes in one function and classify the tasks by risk. Scope and timing are agreed up front, and the result is yours whether or not the engagement continues.
- 01A map of where the time goes in one function
- 02Tasks classified: finance, personal data, production systems
- 03The first area to build in
- 04A decision: whether, and where, to start
No case for going further is a result, not a failure.
Experience
Delivered, not proposed.
Client names are withheld under agreement. Figures come from the engagements described and are not a forecast for other organisations.
Global delivery & quick-commerce platform
Finance function · a dozen teams
An operating model for AI across an entire function
People were already using AI, but privately and without a system: the same tools were being rebuilt in parallel by teams who could not see each other. The organisation had no way to tell a harmless personal script from a tool writing to SAP – so the only options on the table were to block everything or to control nothing.
Read the caseClaude Enterprise · Gemini Enterprise · Google Workspace · Apps Script · Slack · SAP via sanctioned APIs
Global FMCG manufacturer, private-equity owned
300 licences · C-level to operations
A rollout that survives an audit and a nervous workforce
A recent carve-out wanted a second, higher-grade AI tool across 300 people – without deploying outside any governance frame, without a pilot too narrow to extend, and without the one message that would have killed it: in a company just taken over by a fund, “save time” is heard as “cut headcount”.
Read the caseClaude Enterprise · M365 · Databricks · Looker · EU AI Act risk tiering
Supplements manufacturer, multi-market
≈200 automations delivered · n8n
Label translation that answers to the regulator, not the dictionary
A supplement label is not prose. Every market sets its own rules on permitted health claims, ingredient nomenclature, allergen declaration and units – so a translation that reads beautifully can still be one that cannot legally ship. Done by hand, each new market meant weeks of specialist review before a single product moved.
Read the casen8n · LLM translation with constrained terminology · human-in-the-loop review
Built for ourselves first
The other half of this studio runs on it.
Autenly Social Studio is not a brochure for this practice – it is an instance of it. Campaign concepting, image generation, scheduling and publishing run as one agentic system we designed, built and operate ourselves, on n8n, Claude, Gemini and Supabase.
We know what it costs to keep one of these running, because we pay it. That is a different kind of knowledge from having read about it.
See what it producesCommon questions
The questions asked before a first conversation.
- Where do we start if we do not know what to automate?
- From a map of where the time actually goes in one function. We classify the tasks that touch money, personal data and production systems, then name the first area to build in. That stage ends in a decision, not a presentation.
- Do we have to replace our current systems?
- No. We work on the systems you already have and join them where a person retyping data joins them today. We advise replacing a system only when the system itself is the problem, not the way it is used.
- Does a first conversation commit us to anything?
- No. It exists to understand what eats the time and to say plainly whether this is work for us. If it is not, we will say so and point you to someone who fits.
- What if the team is not using AI yet?
- That is the most common starting point. We do not begin with tool training but with the tasks the team does today; the tool appears at the task that needs it, and training runs on the team’s real work, not on slides.
- Will our data be used to train models?
- No. We build on enterprise agreements in which the vendor does not train on customer content, on your licences and inside your environment. A tool that cannot guarantee this does not come near your data.
- Who builds it, your team or ours?
- Together. The first working solution is built in production with your team, so the competence stays in the organisation rather than leaving with us when the project ends.
Start with one function.
Tell us where the time goes. We come back within one working day with specifics, not a credentials deck.