Enterprise AI Development
We build AI applications that work with your company data.
We develop RAG-based knowledge assistants, natural-language database queries and task-specific AI features. The application, backend integrations, access control, evaluation and deployment model are designed together—using on-premise infrastructure, a private cloud or managed model APIs as the project requires.
Where will the data be processed?
Documents, database records and prompts may follow different security rules. We identify what may be sent to a managed model API and what must remain inside your network.
How will we test the answers?
We prepare representative questions, expected answers and unacceptable failures. Models and retrieval settings can then be compared with the same evaluation set instead of a few hand-picked demo prompts.
Where will people use it?
An AI feature needs a real entry point: an existing web or mobile product, an internal panel or an API. We define who can access it, which actions it may take and where human approval is required.
We begin with the user, data sources, expected output and acceptable failure boundaries. From there, we design the data connectors, access checks, retrieval and indexing, model gateway and application or API layer as one system.
Deployment may be on-premise, private cloud or hybrid. We compare architecture and model options with the same evaluation set, looking at answer quality, latency, infrastructure requirements and monthly operating cost.
AI development services
Application, model and data layers—built together.
On-premise and hybrid LLM systems
We serve suitable open-source models on company infrastructure or in a private cloud. The scope includes the inference API, authentication, logging, rate limits, model updates and—when needed—a managed-model fallback.
RAG applications and company knowledge
We build an updateable index from approved content in documents, databases and APIs while retaining metadata and access rules. Chunking, retrieval, reranking and source references are tested against the questions people will actually ask.
AI application and model integration
We add AI features to web, mobile and internal applications through a backend model gateway. Structured outputs, tool calls, retries, timeouts, fallbacks, evaluation results and model cost are handled outside the user interface.
Example applications
AI features with a defined user and output.
Company Knowledge Assistant
Search policies, technical documents and project archives according to the user’s permissions, with links to the sources used in the answer.
Document Processing
Extract fields from contracts, forms or reports, classify the document and pass structured output to an existing business workflow.
Database and Operations Assistant
Answer natural-language questions using approved database views or APIs, summarize the result and require approval before any operational action.
Engineering approach
An AI feature must remain operable after launch.
The software around the model
The model is only one component. We also build the user interface, backend APIs, data connections, authentication, administration screens and monitoring required for daily use.
Evaluation before and after release
The same evaluation set is run when prompts, retrieval settings or models change. Production feedback is reviewed separately so quality improvements do not depend on anecdotal examples.
Permissions, logs and data retention
Running a model inside the company network is not enough on its own. User permissions, document-level access, prompt and response logs, retention periods and sensitive-data masking are defined separately.
Model gateway and fallback
Where practical, application code talks to a model gateway rather than directly to one provider SDK. This makes it possible to test another model, route specific tasks differently or define a fallback without rebuilding the user-facing product.
Project flow
From use case to production.
AI products we build
Codigma.io
Codigma converts Figma designs into framework-specific code for React, Angular, Vue, React Native, Flutter and HTML/CSS. It is an AI product used in an actual development workflow, where the generated output can be opened, run and reviewed.
- Design context to framework-specific code
- Structured generation and runnable preview
- Output evaluation and production feedback
Operating Codigma requires prompt and context versioning, structured-output validation, retry handling, latency and model-cost tracking, and feedback based on code that developers actually use.
Explore CodigmaAI products we build
ThinkHub AI Studio
ThinkHub is our on-premise AI product for asking natural-language questions over company databases. Database connections, schema context, natural-language-to-SQL and result summaries work in the same application flow.
- On-premise deployment and open-source model options
- Database schema context and natural-language-to-SQL
- Query validation, access boundaries and audit records
ThinkHub has given us direct product experience in selecting the right schema context, validating generated queries, respecting data-access boundaries and preparing evaluations around company-specific questions.
Explore ThinkHubTechnical questions
Enterprise AI and LLM projects
Yes, when the selected model and infrastructure are suitable. The model serving, retrieval and application layers can run on-premise or in a private cloud. A hybrid setup is also possible: sensitive data remains inside the company network while approved requests use a managed model API. We decide this from the data classification, capacity, latency and maintenance requirements.
Not in every project. For company knowledge that changes over time, RAG is usually the first option because documents can be updated without retraining the model and answers can include their sources. Fine-tuning is considered when the required behaviour, format or task performance cannot be achieved reliably with prompting and retrieval alone.
Yes. Documents can be searched through a permission-aware index while approved database views or APIs provide current operational data. Database queries require additional controls such as schema restrictions, read-only access, query validation, timeouts and audit logs.
We test candidate models against the same representative questions and expected outputs. Answer quality is considered together with Turkish and English performance, latency, infrastructure needs, privacy limits and operating cost. The best choice for a prototype is not always the best choice for production.
Planning an AI application or private LLM project?
We can review the users, data sources, deployment boundaries and expected output, then define a first scope that can be tested with real examples.