The work your team shouldn't be doing.
Fettler is an AI-services studio. We build custom agents, skills, automations and integrations for mid-market businesses, then run and monitor them inside your systems, under your approval.
Skilled people spend their day re-typing between systems that do not speak to each other. We build agents that do that work instead.
Working with 40+ clients across sizes and industries.
Production: shift production and downtime capture
- The shift ends and the log is submitted as a sheet, photo or chat message.
- Read output and downtime entries against the line and shipment plans.
- Compare output to the plan and check downtime reason codes. Downtime without a reason code is held for the shift supervisor to name.
- Prepare the shift record and update the shipment plan.
- The plant manager signs off the shift record before it is sent and posted.
- Log every input, rule, decision and output.
The trigger changes and the approver changes. The anatomy does not.
- a system, a file or a message
- work the agent does on its own
- a decision against your rules
- a named person has to release it
Demonstration data. Not a client system.
The problem
Most mid-sized businesses do not have a technology problem. They have too much work going through too few hands.
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The hours don't go into making things. They go into re-typing things.
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Buying another module moves the typing, it does not remove it.
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The exceptions never make it into any system.
How this differs from the usual programme.
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Scope
- The usual way
- A platform rollout that touches every department at once.
- With us
- One workflow at a time, each paying for itself before the next.
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Time to real work
- The usual way
- A programme measured in quarters, with a pilot at the end.
- With us
- The first agent doing real work by week six.
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Proof
- The usual way
- A demo on sample data and a steering committee sign-off.
- With us
- Parity with your manual process, reconciled to zero difference, before anything is switched off.
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Pricing
- The usual way
- Seats, licences and time-and-materials meters.
- With us
- A fixed price per workflow and a monthly fee to run it.
What we deliver
Custom AI agents, skills, automations and integrations.
You buy one workflow at a time, not a platform to grow into. Our agentic AI services cover the workflow, the connections and the work of keeping it running.
- Agents
- A digital worker that owns a whole workflow end to end.
- builds the full export documentation pack and stops on any mismatch
- Skills
- One reusable competence that any agent or any person can call.
- one "read a supplier invoice" skill, every vendor, every layout
- Automations
- Scheduled unattended runs, with monitoring and retries.
- the daily close pack is on the desk before the desk is occupied
- Integrations
- Getting data out of systems never meant to share it: a legacy ERP, a supplier portal, bank mail, PDFs.
- platform integration where no API was ever published
The machinery
Every AI agent we build has the same six parts.
Name the trigger, the rules and the person who signs.
- Trigger: it starts itself
- Read: ERP, PDFs, portals, mail, sheets, photos
- Decide: your rules, your tolerances, your definitions
- Act: posts, files, drafts, emails, updates
- Escalate: ambiguity goes to a person, never a guess
- Log: every input, rule and output, auditable
Not a chatbot
A chatbot answers. An agent finishes the task: the document is filed, the entry is posted.
Not recorded clicks
A recorded click sequence breaks the week a screen moves. An agent reads meaning.
Not a dashboard
A dashboard waits to be opened. An agent acts on the number and tells the person who needs to know.
Already running
AI workflows already running in business operations.
We describe them by what they do rather than by who they belong to.
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Finance
Settlement reconciliation
Reads dense remittance workbooks by meaning rather than cell position and rebuilds every line; anything it cannot account for is held with the reason stated.
- trigger
- remittance file lands in the mailbox
- approver
- finance lead
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Leadership
An AI colleague on chat
Answers plain-language questions against the live database using one recorded formula per metric, in the chat leadership already uses.
- trigger
- a question is asked
- approver
- none, read only
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Operations
The watchdog
Sweeps every scheduled process and alerts the owner, not the room, with the fix in the message. Silence means healthy.
- trigger
- continuous
- approver
- process owner
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Finance
Bank to invoice to filing
Matches credit alerts to open invoices, names every deduction, raises the tax invoice and produces a return-ready workbook.
- trigger
- bank credit alert
- approver
- finance controller
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Customer service
Inbound at scale
Classifies and routes thousands of messages a week to an owner, escalates critical language within minutes, drafts replies a person releases.
- trigger
- message received
- approver
- named responder
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Integration
The collection layer
Scheduled jobs that sign in to portals with no published interface, with session management, retries and a per-run audit trail.
- trigger
- schedule
- approver
- none, read only
Scroll sideways to read the table.
| time | agent | outcome | state |
|---|---|---|---|
| 06:00 | settlement recon | 4 remittance files read, every line rebuilt | logged |
| 06:14 | settlement recon | one deduction unnamed, held for the finance lead | escalated |
| 07:30 | supplier invoice | 61 invoices matched to purchase orders | logged |
| 08:00 | compliance sweep | 12 checks due today, evidence attached | logged |
| 09:12 | payment posting | credit matched to invoice, tax invoice drafted | awaiting approval |
| 09:40 | inbound triage | messages classified, 3 critical routed to owners | logged |
Control
The agent prepares. A person releases.
Anything that leaves your company or moves your money has a named approver.
Scroll sideways to read the table.
| raised | action | approver | state |
|---|---|---|---|
| 09:12 | release tax invoice to customer | finance lead | held for approval |
| 09:31 | post credit note, ledger write | finance controller | held for approval |
| 10:58 | send documentation pack to buyer | export desk | released 11:04 |
| nothing in this queue moves without a named human | |||
- Data
- Your cloud, your network, or your own hardware.
- Authority
- A human holds the pen.
- Evidence
- What it read, which rule it applied, what it produced, and who approved it.
- Integrity
- No fallback logic. If a field is missing it stops and asks.
- Continuity
- Documented rules and integrations, exportable data, and a handover pack.
- Reversibility
- Parity is proven before anything is switched off.
We enter your systems only with your written authorisation and your own credentials.
Engagement
Four phases. The first agent is live on real work in six weeks.
| # | Phase | What happens | What you have at the end |
|---|---|---|---|
| 01 | Diagnostic, 2 weeks | We sit beside the people doing the work and map eight to twelve workflows, measuring what each one costs in hours. | A ranked automation backlog with an expected payback against every workflow. |
| 02 | Build, 4 weeks | The first agent is built against the real process, in your environment, on your data. Weekly demonstrations with the people who will use it, not with a steering committee. | One agent in production, doing real work. |
| 03 | Prove, 3 weeks | The agent runs alongside the manual process and the two are reconciled to zero difference. | Evidence, and then the switchover. |
| 04 | Run and extend, ongoing | We host it, monitor it, maintain it, and absorb the format and portal changes that break automation. The next workflow comes off the backlog every four to six weeks. | Compounding coverage rather than a finished project. |
- Numbers are set after the diagnostic, because quoting before we have seen your process goes badly.
Fit
If your work is rules based, repeated and spread across systems that do not talk, we can automate it.
- Skilled people spend more than half their week moving information between systems.
- The number everyone actually trusts lives in one person's spreadsheet.
- The deadline is met by someone staying late.
- The last automation attempt produced a pilot, and the pilot produced a meeting.
- Manufacturing
- Shipping and logistics
- Hospitality
- Multi site consumer operations
- Business services
What that looks like in a manufacturing business
A first diagnostic in a contract manufacturing business produced this backlog in two weeks.
- Export documentation packs
- Purchase to pay matching
- Customer purchase order to sales order
- Production against shipment plan
- Shift production and downtime capture
- Inventory replenishment triggers
- Inline quality checks with photo evidence
- Complaint to corrective action
- Tool and die shot count maintenance
- Request for quotation to quotation
- Payables and receivables follow up
- The compliance calendar
We would expect at least three of these to be wrong.
Method
Four rules we do not break, and the reason each one exists.
MIT's Project NANDA reported in 2025, in The GenAI Divide, that about 95 per cent of the organisations it studied saw no measurable return from generative AI. We read that as a discipline problem more than a technology problem. These are the four disciplines we hold ourselves to.
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No fallback logic
An agent never quietly substitutes one data source for another when a field is missing.
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A percentage is not an explanation
A difference is closed when we can say what every unit of it is, not when the percentage looks small. Anything left over is reported as unexplained.
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Parity before switchover
The agent runs beside the manual process until the two reconcile to zero difference. If it does not tie out, it does not go live.
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The done-ness test
After the agent goes live, does a person still open that file? If yes, we have not finished.
Pick one workflow. We'll have it running in six weeks.
Two weeks beside your team, eight to twelve workflows mapped and costed, and a ranked backlog with an expected payback against each one.
Or write to us and describe the workflow that annoys you most. That is usually the right one.
Questions
Eight questions, answered the way we would answer them on a call.
Where does our data live?
Wherever you tell us: your cloud account, your network, or your own hardware. We do not pool it with anyone else's and we do not use it to train a model.
What do you get access to?
The least we need for the workflow you asked for, granted in writing, using credentials you issue and can revoke. We tell you what an agent touches before it touches it.
Who owns the code?
You do, for everything built specifically for you: the workflow logic, the rules, the integrations and the documentation. This is written down before the first build, not after.
What happens if we stop working with you?
You get a handover pack: documented rules and integrations, credentials returned, data exported in a format you can read, and a walkthrough. We build for that day from the beginning.
How do you price this?
A fixed fee for the diagnostic, credited against the first build. A fixed price per agent and a monthly fee to run it. We do not sell seats or bill by the hour.
What if it gets something wrong?
If it is uncertain it does not proceed: it stops and escalates to the person you named. If it acts wrongly, the log tells you what it read and which rule it applied.
Do our people have to learn a new tool?
No, and we treat that as a design constraint. The work shows up where your people already work: the ERP screen they use, their mailbox, the chat they already have open.
How long before something is live?
Two weeks for the diagnostic, and the first agent is on real work by week six. Parity is proven between weeks six and nine before anything manual is switched off.