Once a month an Aivell sweeps your customer table for duplicates and drift — same company twice, three phone formats, dead email domains — writes every proposed fix to a change log, and pauses for approval. Only after a human says yes does anything change, and deletes are blocked outright.
starts when: First Sunday of the month, the sweep begins
Customer data doesn’t break; it rots. A company signed up twice under slightly different names gets two invoices and takes offense at both. Phone numbers live in five formats, mailings bounce off domains that died in 2023, and every report is a little bit wrong because “ACME GmbH” and “Acme Gmbh.” are counted as two customers.
Everyone agrees it should be cleaned. Nobody does it, because cleanup is a weekend of squinting at near-duplicates — and because the tools that offer to help want your entire customer base uploaded first. For records full of personal data, that trade is the wrong way round: keeping the processing in-house is privacy by design under GDPR Art. 25, not an inconvenience to engineer around.
That pause is the feature. The tedious part — finding, comparing, formatting — is done by the box; the judgment stays with a person. Your records get cleaner every month, and every single change has a name, a date and an undo path.
No flowcharts, no code — a chore is just a message to your Aivell, in your own words. It shapes it into a solid, guarded procedure and follows it to the letter. This one:
first sunday of the month, scan the customer table for duplicates
match on VAT number first, then fuzzy on name plus address
normalize phone numbers and country codes to one format
obvious duplicates — same VAT, same address — propose a merge
anything fuzzy goes on the review list, never auto-merged
write every proposed change to the changes sheet, before and after
send Greta the list and pause — nothing runs until she approves
after approval, apply the merges and archive the log
Connected with single-click passkeys — no copied tokens, no OAuth hell.
Every tool can be limited to exactly what this chore needs. For this one, you might set:
Not on its own. Exact matches — same VAT number, same address — are proposed; fuzzy matches go on a review list and are never merged automatically. And nothing at all changes until Greta approves the batch.
It never ships. The matching, the normalizing and the reasoning all run on the box, inside your network. The customer table is read in place and updated in place — no export, no third-party processor to add to your records.
The slow rot — duplicate companies, five phone formats, country codes that disagree with addresses, email domains that stopped resolving. It proposes; the change log shows each fix as a before-and-after pair.
Yes. Every applied change is in the append-only log with its previous value, so reversing one is a documented step, not an archaeology project.
Your Aivell compares spreadsheets with the database, explains every mismatch in one line, and mails the list — financial data checked behind your firewall.
A webhook fires in one system, your Aivell writes the record correctly into the other — on-premise glue between your tools, with guardrails on every insert.
Order status, returns and price checks handled by a private AI colleague — onboard inference, so customer data never leaves your shop.
An on-premise AI colleague for insurance brokers: sorts claims documents as they arrive, works the renewal book, chases clients for papers. Client data stays in your office.
An Aivell is an on-premise AI colleague: a small box that plugs into your network, runs its own AI onboard and takes the repetitive work off your desk. Prompts, documents, data — nothing ever leaves your office. Unplug the internet: it keeps working.
You describe each task as a chore, in plain language. Your Aivell turns it into a solid, guarded procedure and runs it in the background — with guardrails on every tool, a complete audit trail, and approvals in your hand when you want them. One-time setup, fixed monthly fee. No tokens, no overages.
onboard inference · air-gapped · fixed monthly fee