AI-Assisted Business Applications

We treat AI not as a separate product, but as a component that sits inside an organization's existing work. A model becomes genuinely useful once it's tied to the organization's own data and its own process rules.

When AI comes up, the discussion is usually about which model to use. On the enterprise side, what actually determines the outcome usually isn't the model choice; it's what data the system can access, what rules constrain it, and who verifies the answer it gives. When making a task AI-assisted, most of the effort goes into answering these three questions.

That's why we start development with a single process. Parsing incoming invoices, extracting information from a set of contracts, classifying field records, or letting a user ask natural-language questions about their own data. Keeping scope narrow makes it possible to measure whether the output is correct, and to fix the system before it's rolled out more broadly.

The application itself is still the enterprise software work we know: data model, authorization, audit trail, integration with existing systems. AI sits inside that structure as a component with a defined responsibility. What the user ends up facing isn't a new screen — it's a version of the system they already use that asks for less manual work.

Yapay zekâ bileşeninin kurumsal sistem içindeki yeri Kurumun verisi ve iş kuralları uygulama katmanında tanımlı sınırlarla yapay zekâ bileşenine bağlanır. Bileşenin çıktısı ya doğrudan işe döner ya da emin olunmayan kayıtlar insan kuyruğuna düşer. Her çıktı denetim izine yazılır ve doğrulama setiyle ölçülür. UYGULAMA KATMANI Kurumun verisi kayıtlar, belgeler, geçmiş İş kuralları yetki, onay akışı, mevzuat Yapay zekâ bileşeni belge okuma · alan çıkarma soru-cevap · sınıflandırma İşin kendisi daha az elle iş İnsan kuyruğu şüpheli kayıtlar ERİŞİM SINIRI YETKİ VE ONAY DOĞRULANAN ÇIKTI EMİN DEĞİLSE Denetim izi hangi soru, hangi veriyle, hangi sürümle cevaplandı Doğrulama seti kurumun kendi örnekleri; sürüm değişince aynı test

Are we the right fit?

For work with a clear-cut rule, AI is an unnecessary layer. If a rule can be written, writing it stays both cheaper and verifiable. This technology earns its place where the rule can't be written, but an example exists.

Yes, let's talk

  • There's manual, repetitive work whose output can be checked
  • The decision rests on a document, free text, or unstructured data
  • The organization has enough examples showing what the correct answer looks like

No, someone else does this better

  • The work is already defined by rules — conventional software is cheaper and more exact
  • The decision in question is one where a wrong output produces an irreversible outcome
  • The organization's data is scattered; compiling it is a project in its own right

What we do

  • Process assessment — determining which work fits this technology
  • Q&A infrastructure over the organization's own documents and records
  • Document reading, field extraction, and classification
  • Natural-language reporting and data-query interfaces
  • Decision support and recommendation generation tied to the workflow
  • Integration with existing enterprise systems and data sources
  • Model selection; assessment of cost, speed, and data privacy
  • The decision and setup for running on-premises or in the cloud
  • Measuring output accuracy, human approval, and audit trail
  • Capability interfaces AI clients can call

How we proceed

01

Process selection

Not every task in an organization fits this technology. We choose a process that's manual, repetitive, and has output that can be checked; we build the first application on top of that.

02

Extracting data and rules

We gather the documents, records, and internal definitions the process rests on. Where the data is scattered or the definitions were never written down, this is where the real work begins.

03

Accuracy criterion

We define what the correct answer looks like using the organization's own examples. Without a criterion in place, whether an output is good stays a matter of opinion.

04

Application development

We embed the model inside the enterprise application. We build authorization, record-keeping, and handoff to a human in uncertain cases at this stage.

05

Go-live and monitoring

In the early period we route outputs through human approval. As measurement results accumulate, we determine which steps can run without approval.

Our technical choices

  • The AI component is positioned so it can't step outside the application's own rules. Permission limits, accessible data, and the approval step are all kept in the system's own layer; the text the model generates can't change these rules. Otherwise, who can access what becomes just as uncertain as whether the output is correct.
  • Every output is logged. What question was answered with what data, which version was running, and whether the user approved the result — all of it is kept on record. Without that record, when the system produces a wrong result, there's no way to go back and find out why.
  • Accuracy is measured against a set of examples, not by guesswork. A validation set built from the organization's own documents lets the same test be repeated whenever a version changes. That's the only way to see what broke when the model or the prompt changes.
  • Where the data gets processed isn't a technical preference — it's the organization's decision. For work with high privacy requirements, on-premises models are considered; in other cases, cloud services are. The choice is made between cost, speed, and privacy, and it's written down along with its rationale.
  • The system is built to hand off records it isn't confident about to a human. A setup that queues up doubtful records is more useful than one that decides everything on its own; you can see where errors accumulate, and the fix goes exactly there.
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