AI development with company data
We build applications around company documents, databases and internal systems. Depending on the project, we use RAG, managed model APIs, a private cloud or a local/on-premise LLM that runs inside your infrastructure.
Examples
The system searches policies, contracts, manuals and project files, then shows the sources used in the answer. Example: “What risks appear across these contracts?”
Users ask approved ERP, CRM or reporting data without writing SQL. Example: “Which expense categories exceeded budget last month?”
The system groups support tickets, survey comments and reviews to find recurring issues. Example: “Which complaints repeated most often in the last six months?”
The system summarizes long reports, extracts fields from forms or contracts and sends the result to an existing workflow for review.
Our products
ThinkHub lets employees ask questions in plain language over approved company databases. It generates SQL, checks the query against access and security rules, runs it with read-only permissions and summarizes the result.
Codigma generates frontend code from Figma designs for the selected technology. The generated code opens in a runnable preview and can be edited by a developer.
We can use cloud model APIs for a fast proof of concept without setting up model infrastructure. We do not send data that is not allowed to leave the company.
A private cloud keeps the network and data within defined boundaries while the internal IT team does not have to operate every part of the model infrastructure.
We can run the model locally when sensitive data must stay inside the company network. Before installation, we check hardware capacity, model fit and who will maintain the system.
Local is not always safer, cheaper or better. We look at the data, permissions, expected traffic, latency, model quality, operating cost and maintenance together.
We do not have to choose only one option. Sensitive searches can stay inside the company network while approved tasks use a cloud model API.
What we build
An internal assistant searches approved documents and company knowledge. Users see the sources and only access content they are allowed to view.
Users ask questions over approved ERP, CRM or reporting data. Queries run through approved APIs or read-only database views and are checked before execution.
We extract fields from contracts, forms, invoices and reports, classify or summarize the document, and send the result to the existing workflow for review or approval.
We run the model, backend, authentication and logs on-premise or in a private cloud, then connect them to existing systems through APIs, internal panels, web or mobile applications.
The model will not answer every question correctly. We test it with real examples before launch and use source references, permission checks, validation or human approval where the risk requires it. The same tests run again when the model or retrieval method changes.
How we start
Common questions
Yes. If the model fits the available hardware, the application, RAG layer and model service can all run on-premise or in a private cloud. A hybrid setup is also possible. The choice depends on data sensitivity, expected load, latency, cost and maintenance responsibility.
Usually not. If company information changes often, RAG is easier to update and can show sources in the answer. We consider fine-tuning only when prompts and retrieval cannot produce the required behaviour or output reliably.
Yes. Documents are searched according to user permissions, while current figures come from approved APIs or read-only database views. We limit the available tables and queries, set timeouts and keep audit logs.
We give each candidate model the same questions and compare the results. We look at answer quality, Turkish and English performance, latency, data restrictions, infrastructure needs, operating cost and maintenance.
We can build a small proof of concept with one problem and a limited set of real data. If it is useful, we take it into production; if it is not, we do not expand the scope.