FAQ.

The fastest returns come from processes that repeat over and over: preparing quotations, classifying requests, comparing procurement options, extracting information from documents, and interpreting reports are quick examples. Rather than listing these without knowing your organisation, our first engagement surveys your processes and determines — after an analysis — which one is ready today, which one needs its data put in order first, and which one should be left alone. We build fast, accurate, end-to-end integrated solutions shaped around your organisation and its processes.

An orderly process and securely accessible data are enough to begin; a large budget or your own model infrastructure is not necessarily required at the entry stage. In practice the obstacle we meet most often is not technology: it is a process that is undefined in the system, or stages where the decision follows a person rather than a rule. This is why AI work usually starts on the process side.

Yes, and there is significant potential here. Order, production, costing and procurement data is already structured, so the model reads from the system and interprets rather than guessing. The solutions we build use the ERP as a source of record and show which entry an answer is based on. Instead of direct access, we work on a principle where only as much data as the user and the system actually need moves, within authorisation and security boundaries.

Processes whose rules can be written down, whose inputs are defined, and whose errors are reversible are suitable. Unsuitable ones run on one-off exceptions, keep their rules in one person's experience, or push their errors straight to a customer or a regulator. Drawing this line early is the most important decision in the project; automating the wrong process costs more than not automating at all.

We build the system accounting for the possibility of a wrong answer. The layered structure we design includes: standardised access and authorisation to sources; showing the source of an answer so the user can verify it; handing a process over to a human when confidence falls below a threshold; and measuring accuracy through regular evaluation tests. This makes the error margin manageable and scalable, and usage logs feed a structure that keeps improving itself.

Costing, accounting, finance, budgeting, procurement, production, sales, CRM and HR — we provide solutions across every process you run. We treat them as one connected flow rather than separate headings: procurement's order timing affects the production plan, and when production is affected, the delivery dates sales committed to change. A process in one module almost always surfaces as a report in another.

We improve the process that feeds the report, not the report itself. When one question returns two different answers from two reports, we run a root-cause analysis: we trace the difference to its source, list each one with its justification, and leave behind a checklist so the corrected result can be verified. We carry this out with automation practices proven across many projects.

We close proficiency gaps by addressing the process and the system together, not with training alone. When a user avoids a screen the reason is usually not ignorance but that the screen does not match how they actually work — a step too many, a field that serves no purpose, or information they do not have at that moment. We first learn the process from the person doing the work, then simplify its counterpart in the system; for the genuine knowledge gap that remains we provide handover documentation and one-to-one work.

Yes — this is the core of what we do. We learn the current process from the people who know it, produce the gap against the system configuration, then design the target process and build its counterpart in the system. What a standard module can solve is marked separately from what genuinely needs development; instead of unnecessary customisation we offer enterprise-grade, globally applicable solutions.

With a short introductory conversation. We listen to the problem and the current situation, then propose the scope and approach in writing. The output of this first step is not a sales deck but an honest answer: what the scope and the solution are, and why this solution.

Yes, and it is the model we prefer. We work next to your team and transfer knowledge as we go; the goal is that at the end of the engagement the process is handed over to your team completely. Rather than creating dependence, we focus on improvement and development work.

Yes, in two forms: a defined maintenance agreement, or support called upon as needed. The first period after a process change or an AI rollout matters most; we follow up on improvement at defined intervals and stay part of the process.

We shape the architecture around the sensitivity of your data, and we settle in writing at the start of the engagement which data goes where. For scenarios involving personal data we use three approaches: masking before anything reaches the model, hosting on appropriate servers, or running the model entirely on your own infrastructure. Which one is required is determined by the data type and the regulation, not by preference. We conduct our work in compliance with KVKK and GDPR, establish mutual confidentiality terms, and uphold them indefinitely.

Outside terms agreed in advance — masked, or explicitly accepted by you — your data is never used or backed up by us. We connect to your own environments to deliver solutions for your process and service needs. Data security is our company's first concern.

Both are possible; the decision follows data sensitivity, your existing infrastructure and the cost balance. An open model running on your own servers is a realistic option for organisations that want data never to leave; the hardware and operating cost has to be discussed from the start.

Verhex is an İstanbul-based enterprise process consulting and AI engineering company. It designs production, costing, finance, sales, procurement, HR and CRM processes on ERP systems, and builds AI and software solutions that integrate into existing enterprise systems. There are three service lines: consulting, AI, and integration.

İstanbul, Türkiye. We work remotely with organisations across Türkiye and Europe, and on site during process discovery and go-live.

The team is formed around the scope of the engagement and is deliberately mixed: the consultant who knows the process and the software and AI engineers who build the system sit in the same team. This is a deliberate choice — separating the people who design the process from the people who build the system is where most knowledge is lost in handover.

We have no sector restriction. Process names and the regulations they answer to change by industry, but the logic of cost flow, planning discipline and the approval chain does not. We add the most value in organisations running several systems with data moving between them, manufacturers sensitive to costing and planning accuracy, and companies adding AI to their processes.

On the AI side we work with the major large language models and open-source models through a provider-independent layer; on the application side mainly TypeScript, Next.js, Python, PostgreSQL and container-based infrastructure. We choose technology by what your team can sustain after handover, not by what is fashionable.