Business process automation and AI agents
Wellsy FLOW designs, hosts and operates the automations that keep your business running. We start with what is deterministic and reliable, we add artificial intelligence only in the places where it genuinely earns its place, and we tell you plainly where it loses.
Systems that run in production, not pilots that stall.
Deterministic automation first
AI behind a confidence threshold
Your workflows belong to you
What we orchestrate
What can be automated in your business?
Any repetitive process that follows rules: syncing between tools, sales follow-ups, order processing, accounting entries, catalogue updates. We map your steps, we measure the time they cost, and we automate the ones with an immediate payoff before touching the ambiguous cases.
Deterministic automation
Explicit rules, testable, returning the same result every time.
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AI where it wins
Voice, free text, document extraction — with a human behind it.
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Voice and conversational agents
They call, they answer, and they book in your calendar.
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Why start with deterministic automation?
Because it produces most of the gain and it never gets things wrong. An explicit rule always returns the same result, can be tested, replayed and audited. We keep artificial intelligence for the places where no rule is enough, rather than installing it everywhere on principle.
We connect your tools to each other: APIs, CRM, online store, accounting, telephony, messaging. An incoming order triggers its delivery, a won opportunity writes its invoice, a bank statement lands in the right account. Each chain is written once, then it runs.
Two patterns run through everything we build, and they are what makes the difference over time. Deduplication on a stable key, so that the same item handled twice still counts once. Idempotence, so that a replay after an incident never creates a second entry.
We run this machinery on our own systems, including the chain that takes payment: a payment triggers domain registration, DNS setup, the certificate, delivery, then moving the opportunity to won. Nobody steps into that sequence.
Where does artificial intelligence genuinely add something?
Wherever the input is ambiguous: voice, free text, uncertain classification, extraction from a poorly structured document. Every model decision carries a confidence threshold; below it, the case goes to a human review queue. AI proposes, the rule decides, and nobody discovers the error weeks later.
On financial entries, categorisation happens at three levels: a learned mapping table handles what is already known, a model takes over above a confidence threshold, and whatever stays uncertain goes to a review queue. The share handled without intervention rises, the margin for error does not.
On product data, we import a catalogue into several platforms, we move orders between them, and we detect duplicates. Product descriptions are generated by a model, then reviewed before publication: generation is automatic, going live is not.
On documents, extraction replaces typing. A document arrives, the useful fields come out, and anything not read with enough certainty is flagged rather than guessed. That last sentence is what separates a usable tool from a source of silent errors.
What does an AI agent do during a call?
It announces that it is an artificial intelligence, leads the conversation, checks your real calendar and books the appointment itself. After the call, the transcript goes into your CRM, the opportunity is updated, and an opt-out request is honoured automatically, with no intervention.
Outbound calls respect calling hours and a follow-up cadence: the agent does not call at any hour, and it does not call a contact back more often than agreed. Inbound calls follow the same logic, with the same announcement at the start.
In writing, a conversational agent answers on your site. For a luxury real estate operator, we built a multilingual concierge whose model only ever receives the properties actually listed: it cannot invent a property, not because it is forbidden to, but because it knows nothing else.
That same agent captures enquiries while keeping the full context of the search: what was asked, in what order, under which constraints. The sales team picks up a conversation rather than an empty form, and the handover happens without making the person repeat themselves.
Who owns your workflows?
You do. We build on n8n, an open source foundation that we host and monitor for you: your automations stay readable, exportable and workable by another team. You can change provider without rebuilding everything. An agency that hides its tooling cannot promise you that.
Hosting and operations are part of the service: we watch the runs, we alert on a processing failure, and we also alert when an API quota approaches its limit. An automation that stops without anyone knowing costs more than it returns.
Maintenance and changes follow the same contract. A process changes, a platform alters its interface, a new need appears: the change happens on a foundation you own, without starting over and without negotiating access to your own business logic.
We train an internal owner whenever you want one, and we are straightforward about where their scope ends. They handle inputs, outputs, and frequencies if needed. The core of the workflows stays with us: you are autonomous in use, never exposed on the mechanics.
Do the people you deal with know they are talking to an AI?
Yes, and it is wired into the system rather than left to good intentions. A voice agent says so in the first seconds of the call. Your data stays on the hosting we operate for you, and opt-out is honoured automatically as soon as it is expressed.
The announcement is not a line in a script that can be skipped: it sits in the agent configuration, before the first useful sentence. An agent that does not announce itself does not go to production, and that constraint is not negotiable case by case.
Opt-out works the same way. Someone who asks not to be contacted again is removed from the cadences, immediately, without a human having to remember. The request is recorded with its timestamp, which lets you show when it was received and honoured.
We see voice agents being sold that do not announce themselves, because it is easier to sell. We do not ship those. The day a director is asked the question, the answer fits in one sentence and one record, or it does not hold at all.
How does an automation project actually run?
A free audit first: we look at your processes and we tell you what is worth automating, and what is not worth the trouble. Then comes a fixed scope, an acceptance run with you, and monitored operations: supervision, failure alerts, and changes.
Free audit
We walk through your processes with you, we spot the repetitions and the re-entry, and we put a figure on the time they cost. You leave that conversation with an ordered list, including what we advise you not to automate. This step commits you to nothing.
Scoping
We cut the work into scopes that each produce a visible gain on their own. You choose where to start, and we set the fixed price for that scope. A project that delivers nothing for months is a project that stops before it has served.
Build
We write the chains, we connect your tools, and we handle the edge cases at the same time as the normal path. This is where deduplication, idempotence and confidence thresholds are put in place, because they cannot be added afterwards.
Acceptance with you
You run your real cases through it, including the ones you know are awkward. We fix what fails. Nothing goes to production on the strength of one successful demonstration, under conditions we chose ourselves.
Going live
The system moves to production on the hosting we operate, with its supervision: run tracking, failure alerts, API quota alerts. You are told when something stops, and you do not have to find out yourself.
Ongoing changes
Your processes change, your tools change, and the automations follow. We train an internal owner if you want one, and we stay on the mechanics. You own the foundation, which makes every change a modification rather than a rebuild.
What separates a real automation from a demo?
Many AI pilots never reach production, and the model is almost never the reason: the edge cases are. A duplicate, a replay after an incident, an interface quota closing, an uncertain decision applied with no check. Here is what changes, point by point.
A pilot that stalls
What a pilot produces
- Cases handled: the happy path
- Replay after an incident: duplicates
- Model decision: applied as is
- Error: found later, by chance
- Outage: nobody is told
- Ownership: with the provider
What a system produces
- Cases handled: the edge cases too
- Replay after an incident: idempotent, no double entry
- Model decision: confidence threshold, human behind it
- Error: flagged the moment it happens
- Outage: failure and quota alerts
- Ownership: open source foundation, exportable workflows
A system that holds
Frequently asked questions
Do we need to know what we want to automate first?
No, and it is usually the opposite. The audit exists to spot the repetitions you no longer see because they are part of the routine. You describe how you work, we identify the re-entry, the copying between tools and the follow-ups done by hand.
What do you not do?
We do not drive software without a programming interface by simulating screen clicks: that is fragile and it breaks at the first update. We do not build a full business application in this scope, we do not act as your data protection officer, and we do not take on a large data migration as a standalone project.
How does pricing work?
In three stages. The audit is free and commits you to nothing. The build is a fixed price per scope, known before we start, which avoids open-ended budgets. Operations are a monthly subscription covering hosting, supervision, alerting and maintenance. Figures are given in a meeting.
What happens if we want to take over or change provider?
Your workflows are exportable and readable by another team, because they are built on an open source foundation rather than a proprietary platform. You leave with your business logic. It is a promise an agency that will not say which tool it uses cannot make.
Does our data go to a third party?
Your automations run on the hosting we operate for you, not on a shared platform whose rules you do not know. When a step calls a language model, it sends what that step needs, and not the whole of your database.
Do we need someone internally to look after it?
It is not required: both models exist, depending on whether you would rather be autonomous or leave it with us. If you want an internal owner, we train them — they handle inputs, outputs and frequencies. The core of the workflows stays with us, so a misplaced change does not cost an outage.
What happens when an automation fails?
It stops and it says so. A processing failure raises an alert, and a programming interface quota nearing its limit raises another one before the cut-off. A recovery after an incident does not recreate what had already been written: that is the point of idempotent processing.
What about our visibility in AI answers?
That is a different craft, and we do it too: automating your processes does not make your business quotable by an assistant. If your question is being found and cited when someone asks an AI, our visibility in AI answers offer is the one that answers it.
How many hours a week go into repetitive tasks?
The audit is free and commits you to nothing. We look at your processes together, and you leave with an ordered list of what is worth automating — and what is not.
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