We measure it on 500 of your own incoming documents in 14 days: what got matched, what got misfiled, and what a human still has to see. Before you commit to the build.
Fixed pilot fee. Below the matching threshold we agree together, you pay nothing. No per-document pricing, no annual licence.
What sorting the post really costs
A broker with fifteen insurer relationships receives fifteen document conventions. Somebody opens each one, works out whose it is, and drags it to a folder. Count how many times that happened last month.
per document to open it, identify the client and policy, rename it and file it. Multiply by everything the insurers sent you.
of documents filed against the wrong client or the wrong policy year. You find them when someone goes looking during a claim.
typical lag between an endorsement arriving and it being visible on the client record, which is a day your account handler answers from memory.
who recognises each insurer's layout on sight, and whose absence backs the post room up within a week.
Three fields. This is what filing insurer post costs you per year.
Excludes the cost of a misfiled document surfacing during a claim, which is the expensive kind and does not show up in a time study.
The mechanism
Filing is a matching problem, not a reading problem. The hard part is not pulling a policy number off a page. It is deciding which of your four Smith Ltd records the page belongs to, and admitting when it cannot tell.
Schedule, endorsement, renewal invitation, cover note, premium advice, claims correspondence. Each type carries different identifiers and different urgency, so the type is decided before anything is extracted.
Policy number, insurer reference, insured name and risk address are all read, then scored together against your client and policy records. Agreement across several identifiers is what produces confidence, not any single field.
A confident match files itself with the document type and date recorded. An ambiguous one goes to your team with the candidate records side by side and the reason for the doubt named.
An insurer endorsement on the left. What the layer extracts and decides on the right. Illustrative example with fictional data.
| Item | Change | Was | Now |
|---|---|---|---|
| 1 | Buildings sum insured, Unit 4 | 1,850,000 | 2,140,000 |
| 2 | Business interruption indemnity | 12 months | 24 months |
| 3 | Additional premium, pro rata | - | 1,284.60 |
Endorsement · NG-CP-2025-77310 · eff. 01.08.2026 · AP 1,438.75 · awaiting client confirmation
The offer
One fixed fee. Fourteen days. Your real inbound post. A matching accuracy number at the end that tells you, and us, whether the full build is worth doing.
Not a demonstration on tidy sample documents. The pilot ends with your own month of post processed and a per-insurer breakdown you can argue with.
Against a problem costing most clients €40,000–70,000 a year.
Credited in full against the build if you proceed within 90 days.
No software to install, no workshop series, no project team on your side. Your people keep working; we work on copies.
Before we start, we agree the accuracy threshold that makes this worth deploying. Together, in the engagement letter. If the measured result on your documents comes in below it, the pilot is free. We can offer this because we scope honestly: if we don't think your document mix can hit the number, we decline the pilot and tell you why.
Pilots run one at a time per engineer, so they start in the order requests arrive. Current lead time: about two weeks
Your actual alternatives
| Do nothing | Hire another handler | Vertical SaaS product | Braynex pilot → build | |
|---|---|---|---|---|
| Matches to your own client book | ✕ manual every time | ✓ if they know the book | ✕ matches on policy number alone | ✓ scored across several identifiers |
| Cost over 3 years | rising with your book | €110–160k+ | per-document pricing, grows with volume | known, fixed, quoted |
| Proof before commitment | - | ✕ | ✕ demo on their documents | ✓ measured on your post |
| Admits when it is unsure | - | ✓ usually asks | ✕ files its best guess | ✓ escalates with the reason |
| Survives an insurer changing its layout | ✕ | ✓ after retraining the person | ✕ template breaks | ✓ trained on your mix, retrained on ours |
Why believe us
AI that converts medical records into legally defensible timelines. Accuracy-critical, regulated, every extraction traceable to its source page.
Client meeting recordings become compliant advisory documents automatically, adopted across the practice for every client meeting.
Production vision-AI moderation for marketplace listings: real ML in production at volume, not an API wrapper.
Swiss entity (Zug) · EU-region hosting · GDPR & revised FADP compliant · named senior engineers you meet before signing
Fair questions
It stores documents well. It does not decide which client a PDF belongs to, which is the part your team actually spends time on. We sit in front of it: by the time a document reaches your document management, it already knows what it is and whose it is.
That is the case the pilot is designed to measure, not to hide. Where several identifiers agree, the document files itself. Where the name matches but the policy number history does not, it stops and shows your handler both candidates. The report tells you how often that happened and why, so you can decide whether the residual manual rate is acceptable.
That is the part we prove first, in a test environment, during the pilot. Some systems expose a clean interface and some need a supported integration path; if yours turns out to be the hard kind, you learn that in week one for a fixed fee rather than in month four of a build.
Sometimes you should, and the pilot report will say so plainly. If your post is dominated by a handful of insurers who all send structured data, an off-the-shelf product may cover you and we will tell you that rather than sell you a build. You will have paid a fixed fee for a clear answer either way.
EU-region hosting, a GDPR-compliant processing agreement, and a contract with our Swiss entity in Zug. Nothing trains a public model. Your own compliance lead can review the setup before the pilot starts, and the pilot sample can be narrowed to a subset you are comfortable sharing.
Because we are structured for exactly this engagement size. The large consultancies staff €60k projects with juniors or decline them; we deliver them with the people you actually meet. That is the trade we have built the company on.
Start here
Tell us roughly how much post you receive and which systems it has to land in. You get a reply from an engineer within one business day, and a straight answer about whether this is worth piloting on your book.
Lead Engineer · runs your pilot
The people on this page are the people on your project. No handover to a delivery team you never met: the engineer who scopes your pilot is the engineer who builds it.
Braynex
Prishtina, Kosovo · Zug, Switzerland
Contact: contact@braynex.ai
Responsible for the content of this page: Braynex. Further statutory details (register entries, VAT identification) are available on request and via the main website at braynex.ai.
The contact form on this page sends your entries directly to contact@braynex.ai by email. We use the data you provide solely to respond to your enquiry; it is not shared with third parties, used for advertising, or added to any mailing list.
This page is hosted in the EU region and sets no marketing cookies. For questions about your data or to request deletion of correspondence, write to contact@braynex.ai.