Custom AI solutions solve a business problem that no off-the-shelf product covers, with a system built on your own data and your own processes. Intelligence AI is an AI software development company based in Istanbul. We build custom large language model (LLM) applications, RAG-based knowledge systems, AI agents, forecasting and anomaly detection models for businesses in Türkiye and abroad. We are not a general software agency; AI is our only business. A working prototype within 2-4 weeks of the first meeting, a production system within 2-3 months.
Not every problem needs custom software. For a need that has become an industry standard and that your competitors share, an off-the-shelf product is usually faster and cheaper. A custom AI solution makes sense when at least one of three conditions holds:
If none of the three applies, we recommend a ready product first and a hybrid route if needed. We cover that comparison in detail on the AI solutions for business page. Our own products, SellBox, NewsTracker and the Proposal Robot, exist for that very reason: for some problems a ready system is the better answer than custom development.
Systems that answer questions by citing your company’s documents, contracts, procedures and past correspondence. With RAG (retrieval-augmented generation) the model speaks from your knowledge, not its general training; the hallucination risk is reduced by making a cited source mandatory. This is the architecture we build most often for legal, procurement, HR and technical support teams.
Systems that do not just answer but do the work: agents that prepare a quote, process an order, match an invoice or update a price. See the AI agents page for agent architecture, security and the development process.
Systems that use OCR and LLMs to extract structured data from unstructured documents such as invoices, contracts, insurance forms and customs papers, classify them and feed them into a workflow. Custom versions of our Document Intelligence solution fall into this category.
Demand forecasting, inventory optimisation, dynamic pricing, campaign impact measurement. Machine learning models trained on your own sales, stock and market data. SellBox’s pricing engine is a production example of this class.
Systems that catch deviations from the norm in real time across financial transactions, insurance claims, supply chain and operational data. For environments where rule-based approaches fall short because the pattern keeps changing. Details: Financial Intelligence.
Analysis and decision systems that combine branch, field team, logistics and customer data with location. Details: Location Intelligence.
Sentiment, topic and risk analysis across news, social media, customer reviews and call centre records; custom classification models for your organisation. Details: Social Reputation Management.
| Decision | Our approach | Why |
|---|---|---|
| Model choice | Cloud LLM, open source or a mix of both; the model layer is swappable | Less vendor lock-in and pricing risk |
| Data location | Cloud, hosting in Türkiye or fully on-premise | Chosen according to KVKK (Turkish data protection law) / GDPR and company policy |
| Fine-tuning vs RAG | RAG first; fine-tuning only when there is a measurable gain | RAG is cheaper, easier to update and cites sources |
| Integration | API, database or file based; MCP standard | Your existing ERP/CRM stays as it is |
| Delivery | Handover with code, documentation, monitoring dashboard and training | You do not stay dependent on us |
General-purpose models perform worse in Turkish than in English; the agglutinative structure, industry terminology and formal business language all cause problems. In every project we build a separate evaluation set for Turkish and choose the model and prompt strategy against it. Where needed we use open-source models adapted to Turkish. For international teams operating in Türkiye this means the system works equally well in both languages.
Problem definition, data inventory, success criteria. The most important output of this phase is sometimes the sentence “this problem does not need AI”; we are not afraid to say it.
A measurable prototype running on your real data. You see whether the approach works before making a large investment; if the result does not meet expectations, you can stop there.
The system takes over a slice of real work. Accuracy, speed and user acceptance are measured; the model and workflow are tuned accordingly.
Scaling, monitoring, a model update procedure, documentation and training for your team. For a step-by-step walkthrough see the guide on how to start an AI project; for the consulting scope see enterprise AI consulting.
A good fit: you have digital data, the problem you want to solve is measurable (time, cost, error rate, revenue) and ready products either do not meet your need or you do not want to send your data outside the company.
Not yet: if your data is on paper or scattered, the problem is not yet defined, or the expected gain does not cover the development cost. In that case we recommend data collection or a ready product first.
A ready product solves a problem common across an industry with a standard workflow; it is quick to set up and inexpensive. A custom solution is built on your own data and processes; you need it when your data is unique, your process does not fit the standard or your competitive advantage sits at that point. In most projects a hybrid of the two is the right choice.
In most cases, no. An enterprise LLM application connects an existing model to your knowledge base with RAG and steers it according to your workflow. Training a model from scratch is rarely necessary; fine-tuning is done only when it delivers a measurable gain.
Yes. We build systems that run fully on-premise with open-source models or on infrastructure hosted in Türkiye. Which option fits is decided in the discovery phase based on data sensitivity, cost and performance expectations.
Discovery 1-2 weeks, working prototype 2-4 weeks, pilot 3-6 weeks; 2-3 months in total including production rollout and handover. The biggest factors are how ready the data is and how many systems need to be integrated.
The prototype phase is fixed-scope and low-budget. The pilot and production budget is defined based on what the prototype shows. Running costs, LLM usage fees or on-premise infrastructure, are calculated and shown separately in the proposal; there are no hidden costs.
No. We hand over the code, documentation, monitoring dashboards and a model update procedure, and we train your team. If you wish, we continue under a maintenance and development agreement; if not, the system can be operated by your own team.
In a thirty-minute call we will tell you plainly whether your problem can be solved with AI, whether it needs a ready product or custom development, and what the prototype requires. No commitment needed.
Book a free assessment → For connecting to your existing systems, see our AI integration services page.
