We build systems that read documents, classify requests and answer from company knowledge. First we check whether the problem really needs AI, then we measure the value with a small pilot.
Invoice and document reading
Supplier, line items, VAT and totals are extracted from emailed PDF invoices; inconsistent records go to approval.
Anomaly detection
Mismatched amounts between order, delivery note and invoice are flagged before a person has to spot them.
Contract analysis
Clauses are extracted from an uploaded document, risky ones are flagged and fixes are suggested; Hukuk Destek AI works this way.
Search that cites sources
Answers from case-law or internal document search are tied to their source, and the system says "not found" instead of inventing one.
Product image generation
Model photos from a single product shot; we run this every day in our own product, Sanal Studio.
Request and review classification
Customer messages are sorted by topic and urgency, routed to the right team, and recurring questions get draft replies.
Internal knowledge assistant
An assistant that works from procedures, product docs and past correspondence, and answers according to user permissions.
Personalised content
A coach that finds a student’s weak topics and builds tests around them; BilgiKampüs works on this model.
A reliable AI system is less about a good model and more about a good frame: what the model can access, which sources it relies on, what it does when unsure and how each decision is logged.
The flow on the right is the typical structure of an assistant that answers from company documents (RAG). The key is step five: if the system cannot ground an answer in a source, it does not make one up, it hands the question to a person.
We start an AI project by asking how accurate it needs to be to be useful. What share of invoice fields must be right, which classification error rate is acceptable? Without that written down, whether the system succeeded stays open to debate.
On every step where mistakes are costly, the system suggests and a person decides. Approval screens, reason fields and feedback buttons are part of the project; over time the data shows which steps can safely be automated.
Assistants working with company knowledge answer only from retrieved documents and show their source. If there is no basis, they say they don’t know instead of inventing an answer. That is the precondition for users to trust the system.
Models change every few months. We keep model calls in one layer and version prompts and test sets, so when a better or cheaper model appears it can be compared on the same tests and swapped in.
We prioritise AI use cases by value, feasibility and risk, and set a measurable success criterion.
We build a pilot against a test set from your real data and measure accuracy. If the result falls short, we say so plainly.
We turn the pilot into a secure, traceable product connected to your existing systems.
We monitor accuracy and cost in live use and update the system as new models arrive.
AI projects have two cost parts: development and usage. Development scope is set during discovery, after which we offer either a fixed project budget or an ongoing product-team model. We measure model usage cost during the pilot and show you the real figure.
If the pilot does not meet expectations, stopping there without scaling the project is always an option.
No. Most rule-based work is cheaper and more reliable with classic software. We recommend AI for repetitive work that requires reading text, documents or images, and we make that distinction openly during discovery.
We have production experience with Gemini and OpenAI models. We avoid locking the architecture to a single provider, so you can switch when a better or cheaper model appears.
That is why we build systems that rely on sources, hand off to a person when unsure and ask for human approval on critical steps. Every decision is logged.
We prepare a test set from your real data, run the system on it and report the results. After launch, measurement continues with user feedback.
During discovery we review the providers’ data policies for enterprise API use with you, and decide together which approach suits sensitive data.
Yes. The AI step usually sits inside an existing flow such as email, ERP, CRM or an internal panel, and we build the integration as part of the project.
In a free discovery call we listen to what you need and sketch a workable path and a rough timeline.
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