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VergeLabs

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Custom Build

Custom-built for the busywork that doesn't fit a standard product. Only after a scan.

Four fields, one standard.
Education and learning materialsConsulting and strategyPublishers and editorial teamsResearch and think tanksTeachFoundryBookaflowValoraEducation and learning materialsConsulting and strategyPublishers and editorial teamsResearch and think tanksTeachFoundryBookaflowValora

What it is

Custom-built for the busywork that doesn't fit a standard product, on the same foundation: Trail, Retrieval, Approval, Constraints, Expertise, Storage.

How it helps

Sometimes the scan points to a piece that's too specific for a standard pipeline: a valuation engine, assessment preparation, a link between systems. Then we build that, but never without an analysis up front, because building a system and then looking for what it's for is exactly how failed AI projects begin. Hence the fixed rule: first the scan, then the custom build.

The steps

01

Scan

02

A scoped proposal with a fixed price

03

Build with the six layers

04

Trial with measurement

05

Handover and logging in the file

What isn't included

Systems that assess, select, or admit people fall under the heaviest legal regime; there, the work starts with the honest question of whether it's wise at all.

Quote · only after a scanSee the prices

More from the offer

The Office-Memory Assistant

from € 9.500

Ask something, and the answer comes from your own sources. With the location included.

The Material Pipeline

from € 9.500

Speeding up the repetitive work around material, with a human who always signs off.

The Front Office

from € 12.500

Recurring questions handled on their own, decisions always go to a human.

The Supplier Scan

Name a tool and we find out what the supplier publishes about it, with the source alongside.

Free supplier scan

A fit for you?

In half an hour we'll see whether custom build is a fit for you. No sales pitch, an honest picture.

Book an intro call

Here's what that looks like in practice.

Three different worlds, one principle: the AI does the work, the human checks and approves.

Read the case