Janfolio
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Solutions · Workflow & AI automation

Let the repetitive work run itself. Your team keeps the decisions.

We put language models where they save real hours — documents, mail, matching, summaries — and design every feature so a person can see where a suggestion came from and approve it before it counts.

A scanned judgment read into fields, each next to the sentence it came from

Where it helps

Work that is repetitive, but not mindless

  • Reading documents

    Invoices, judgments, contracts and forms read into fields, each next to the line it came from.

  • Sorting the mailbox

    Every message classified with the reason, so the right person sees it first.

  • Drafting replies

    Answers built from the case file, with any missing fact left as a visible gap.

  • Matching and reconciling

    Bank lines to invoices, claims to cases, with the uncertain ones set aside for a person.

  • Summaries and next steps

    Who is waiting for whom on a case, in two lines, from the record itself.

  • Assistants that classify

    The model picks a request from a closed list; the query itself is code we wrote, inside your permissions.

Accountable automation

The rules every AI feature we build follows

  • The model proposes, a person approves

    Nothing is saved, sent or posted from model output without an approval that records who and when.

  • Every field shows its source

    The sentence or line it came from, and how confident the model is.

  • Gaps stay visible

    What the model was not given stays in [brackets] for a person to fill — it never guesses a number.

  • Rules decide what rules should

    Legal deadlines, amounts and permissions come from written rules, never from a model.

  • Every call on record

    Each request is logged, counted against a monthly ceiling, and can be switched off per client.

  • Works without it

    Each feature has a non-AI path, so a provider outage slows you down instead of stopping you.

Try the approval flow

The model prepares. A person approves.

We automate the conversations and paperwork that eat your team’s day: WhatsApp and customer service, inboxes, replies, payments and documents. Routine answers go out on their own, inside rules you set; money, promises and exceptions wait for a person, with the source of every suggestion on screen. Try it:

Shared mailbox · 5 newNames and figures are illustrative

  1. claims@northsea-insure.exampleNew case: Mr R. Olsen, Phuket — policy NS-55120
  2. billing@cityhospital.exampleInvoice INV-20417 (PDF attached)
  3. ops@alpine-assist.exampleAny update on MFA-002380?
  4. news@medtravel-weekly.exampleThis week in travel health
  5. j.weber@lakeside-clinic.exampleIntroduction

Sorted, with the reason for eachWaiting for a person

  • New case: Mr R. Olsen, Phuket — policy NS-55120New case request95%↳ Names a patient, a place and a policy numberCase proposal prepared
  • Invoice INV-20417 (PDF attached)Provider invoice97%↳ Known provider, PDF with an invoice numberQueued for invoice reading
  • Any update on MFA-002380?Status question93%↳ Quotes an open case referenceReply drafted — see “Draft a reply”
  • This week in travel healthNewsletter99%↳ Bulk sender with an unsubscribe headerArchived
  • Introduction52%↳ No case, no invoice, sender not on fileLeft for a person
Fill in the gaps to continue

Automate the work inside your company

What happens on its own, the moment something arrives.

Pick what starts it and press play. Each step is something a person used to do by hand.

When this happens

  1. WhatsAppCustomer sends the order
  2. Your systemOrder recorded with the customer’s details
  3. ManagerApproves orders above AED 5,000
  4. InvoiceInvoice created as PDF
  5. StockQuantities updated
  6. WhatsAppConfirmation and invoice sent to the customer

Where we built it

Questions we are often asked

Which AI models do you use?

Each feature sits behind its own interface, so the provider can be chosen per client — including ones that keep data in a particular region. The system also runs with AI switched off.

Is our data used to train models?

We use providers and settings that do not train on your data, send only what a task needs, and log every call.

What happens when the model is wrong?

A person sees the suggestion with its source and confidence before it counts. Low-confidence fields are flagged and need an explicit check.

Can costs run away?

No. Every call goes through one gate with a monthly ceiling per client.

Have a system like this in mind?

Tell us how your team works today. We will tell you honestly what we would build, or whether an existing product is enough.

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