AI integration services add intelligence to the software you already run: the data your systems produce is processed with AI and the result is written back into those same systems, without replacing anything. You do not have to give up your ERP, CRM or accounting software; an intelligence layer is added on top. A connection test usually takes 3-5 days, a working prototype 2-4 weeks, and the move to production 2-3 months in total. Intelligence AI delivers this for companies in Türkiye and abroad; integration is the technical leg of a wider enterprise AI consulting engagement.
When companies start talking about AI, the concern we hear most often is: “Will we have to replace our existing system?” The answer is no, and that distinction changes the entire cost and risk calculation of the project.
Replacing the system means abandoning the ERP or CRM you use and moving to another platform that has AI features. That means months of data migration, retraining staff and rebuilding every workflow. Going down that road for an AI need is like demolishing the house because the roof leaks.
Integration leaves the existing system where it is. We read the data the system already produces, process it with AI and write the output back in a form the system understands. Your users keep working on the same screens; the only thing they notice is that decisions are now suggested automatically.
In practice this means: your sales rep still uses the CRM, but next to the customer record it now says which product to recommend. Your accounting team still works in the same software, but the suspicious invoice arrives already flagged.
We work at API or database level with SAP, Logo, Netsis, Mikro and similar enterprise resource planning systems. The data accumulated in the ERP, such as stock movements, purchasing data and production planning, is the most valuable source for demand forecasting and supply optimisation. Most ERPs have a standard integration interface; where there is none, a read-only database connection is set up. For management reporting built on this data, see our Business Intelligence solution.
Customer history, call records and pipeline data are used to predict which customer is at risk of churning and which quote has a high probability of closing. The result appears inside the CRM again, as a field or a tag. For external signals on the customer side, our Social Reputation Management solution can step in.
We build systems that read and classify the incoming invoice flow and look for inconsistencies at amount and line-item level. Because Türkiye’s e-Fatura and e-Arşiv infrastructures work in a standard format, these integrations are usually the fastest to go live. For the document reading and classification side, see our Document Intelligence solution.
Product, price, stock and order data is read through the seller APIs of Trendyol, Hepsiburada and similar marketplaces; price and stock updates are written back the same way. Because our SellBox product is built on this integration, we know the API constraints, rate limits and edge cases in this area first-hand.
For applications you developed yourself, or data held directly in a database, a read-only connection is enough. This is usually the most flexible scenario, because the company knows its own data model and the mapping phase moves quickly.
Systems with no API, lost documentation or a vendor that no longer provides support are a fact of corporate life. In these cases we build a file-based bridge: the system periodically produces a report or export, and we read it. It is not elegant, but it works, and because it does not touch the existing system it carries no risk.
Our preference is always the official API, because it is the route least likely to break on version updates. If there is no API, we set up read-only database access; if that is not possible either, file-based transfer. In no scenario do we write directly to the existing system’s database; that approach can void vendor support and put data integrity at risk.
Not every integration has to be two-way. If the goal is only analysis and reporting, reading the data is enough, and that is the lowest-risk setup. If the system needs to make decisions and write them back into the existing software, it is built two-way, but then permission limits and a rollback mechanism are designed from the start.
Not every scenario needs to be real-time, and unnecessary real-time processing drives up cost. Where seconds matter, as in fraud detection, we run instantly. For demand forecasting or monthly reporting, a nightly batch job is both sufficient and more economical. We make this decision together at the feasibility stage.
The most critical design decision in an integration is what happens on failure. In the systems we build, your existing system keeps running when the AI layer goes down; the intelligence layer is an add-on, not a dependency. When the connection drops, transactions are queued, retried, and after a defined threshold the responsible person is alerted.
We verify that we can technically connect to your system. At this stage we ask you for access to a test environment or a user account with limited rights. The aim is to prevent surprises later: is there an API limit, which fields can be read, how often is the data updated?
Together we clarify what the fields in the system mean. The step looks technical but actually requires business knowledge: questions like “which of the 7 values in the status field means a cancelled order” can only be answered by someone who knows the process. That is why we need a few hours from someone in the business unit at this stage.
We build a prototype that runs on your real data. The prototype never writes to the production system; it only reads and produces results. So you see whether the approach works without taking any risk.
After approval the system moves into production: authorisation, monitoring, error notification and, where needed, the write-back mechanism are activated. The total time from the first meeting to this point is usually 2-3 months, depending on scope.
We include this section because these problems come up in almost every project, and knowing them in advance beats being surprised halfway through.
Missing or inconsistent data. The same customer recorded under three different spellings, optional fields left empty, mixed date formats. This exists in almost every company and it is a solvable problem; but it takes time and must be accounted for at the feasibility stage.
API limits. Many systems accept only a certain number of requests per minute or per hour. When working with high-volume data, that limit directly determines how the architecture is built. We measure it during the connection test.
No documentation for the legacy system. The relationship with the firm that built the system may have ended, and nobody who knows the data model may be left. In that case we have to reverse-engineer the data; the process takes longer but does not stall.
Authorisation and approval processes. The technical work takes a few days, but granting access rights can take weeks. This is usually the slowest step of the project. We recommend starting it early.
Vendor restrictions. Some software vendors restrict third-party access by contract. This has to be checked from the start; if there is a restriction, we design an alternative route.
Integration: Product, price, stock and competitor data is read through the Trendyol seller API; the calculated new price is written back through the same API. A two-way setup running close to real time.
Challenge: API rate limits and the need to track thousands of products continuously. The solution is a prioritised queue: products with intense competition are checked more often, static products less often.
Result: The price decision is made in seconds and never drops below the profit threshold. Explore SellBox.
Integration: The quote request arriving from the customer as Excel, PDF or handwriting is read; the items are matched to the company’s own product catalogue and a PDF quote is produced in the corporate format.
Challenge: The product name the customer writes rarely matches the official name in the catalogue exactly. There are abbreviations, typos and local naming. The solution is a matching layer that evaluates text similarity together with product attributes.
Result: 95% accuracy in catalogue matching; a process that took days now takes minutes. Explore the Proposal Robot.
Integration: A large number of news sources publishing in different languages and formats are scanned continuously; the signals are merged into a single stream and prioritised.
Challenge: None of the sources follow a common standard; some offer RSS, some an API, some only a web page. On top of that, the same event is expressed differently in different languages.
Result: The system used by Anadolu Agency can catch breaking news hours before it appears in the major agencies’ bulletins. Explore NewsTracker.
No. By definition, integration leaves your existing system where it is. The AI layer reads the data the system produces and returns the result. Your users keep working on the same screens. If we ever recommend replacing a system, we explain exactly why, and that situation is rare.
Usually, yes. If there is no API we set up read-only database access; if that is not possible either, file-based transfer: the system produces a periodic report and we read it. This method is slower, but because it never touches the existing system it is the lowest-risk option. During the connection test we establish within 3-5 days which route is possible.
That is entirely your choice. The system can be installed on your own servers or in a cloud environment under your control; in that case data never leaves the company boundary. If you prefer it to run on our infrastructure, data is processed only within the scope set out in the contract and the requirements of KVKK (Turkish data protection law) / GDPR are applied. Which data goes where is put in writing at the feasibility stage.
Not when designed correctly. Heavy queries run in batches outside working hours, and real-time needs are served through a separate read channel. During the connection test we measure the system’s load capacity and build the architecture accordingly. We will not propose a setup that puts the performance of your production system at risk.
Most enterprise software offers an official API for integration; this use is a supported scenario. Some contracts, however, contain restrictions on third-party access. We check this together at the start of the project. If there is a restriction, we design an alternative route through the methods the vendor permits.
When the AI layer goes down, your existing system continues to operate normally; that is the core principle of the design. When the connection drops, transactions are queued and processed once the connection returns. Outages that exceed a defined threshold trigger an automatic alert to the responsible person. Monitoring infrastructure is a standard part of the installation.
Tell us which systems you use and what you want to automate; we will tell you whether the integration is technically possible, which route it would take and how long it would need. If it is not possible, we say that clearly too.
