Enterprise AI consulting means identifying which of a company’s processes can be improved with artificial intelligence, implementing that by the shortest technically feasible route, and handing the system over to the company’s own team. Intelligence AI is an AI consulting firm in Istanbul, Türkiye. We do not deliver slide decks and reports; we deliver working systems: a running prototype within 2-4 weeks of the first meeting, a production system within 2-3 months.
Two very different services are sold under this heading, and the difference between them decides the fate of your project.
The first is strategy consulting. The organisation’s maturity level is assessed, a roadmap is drawn up, a prioritisation matrix is prepared and a comprehensive report is delivered. That work has value, but at the end you hold a document, not a working system. Implementation is usually left to another vendor, and that is where it turns out the assumptions in the report do not hold in the field.
The second is implementation consulting. It starts with problem identification but ends with a working system. The consultant owns the outcome of its own recommendation. Intelligence AI belongs to this second group: if we recommend something, it means we can build it ourselves.
In practice the distinction means this: we will not recommend anything that is not technically feasible, because we are the ones who would have to build it.
Our consulting scope concentrates on five areas of expertise. Each grew out of the concrete problems companies bring to us most often.
In environments where millions of transactions flow every hour, rule-based systems fall short; a new fraud pattern is always one step ahead of the rule. We build systems that catch suspicious transactions in real time with behavioural analytics and machine learning. Explore our Financial Intelligence solution.
Where should the next store, warehouse or branch open? How should field teams be positioned for maximum efficiency? We ground these decisions in data by combining demographic layers, competitive density and accessibility. Explore our Location Intelligence solution.
Invoices, contracts, delivery notes, application forms: a large part of corporate life is still spent processing documents. We build systems that read a document, classify it, extract the data inside and route it to the right process. Explore our Document Intelligence solution.
Finding out at the end of the day what is being said about your brand on social media means being far too late in a crisis. We build systems that track mentions as they happen, measure their tone and alert you to unusual activity. Explore our Social Reputation Management solution.
Data sits in several systems, reports are prepared by hand and management dashboards are always a week behind. We build infrastructures that bring data together in one place, automate KPI calculation and provide decision support. Explore our Business Intelligence solution.
The process has six steps. Each step ends with a concrete deliverable, and you decide whether to move on to the next one.
We examine your business processes, existing systems and data infrastructure on site. At this stage we talk to your technical team and to the people who actually run the process, not just to management, because the reality on the ground usually differs from the presentation. Deliverable: problem definition, data inventory, feasibility assessment.
We separate the problems worth solving with AI from those that can be handled with simpler automation. Not every corporate problem needs AI; when it does not, we say so. Deliverable: technical approach, success criteria, time and resource plan.
We turn the conceptual design into a working prototype. It runs on your real data; it is not a screenshot or a simulation. The aim is for you to see whether the approach works before making a large investment. Deliverable: prototype running on real data, first measured results.
The prototype goes into the field with real users in a limited scope. Here we measure user behaviour as much as model accuracy: is the system being used, is its output trusted, does it really fit the workflow? Deliverable: field performance report, improvement list.
The system is integrated into your corporate infrastructure, security and access controls are activated, monitoring is set up. The total time from first meeting to production is usually 2-3 months, depending on scope. Deliverable: system running in production, technical documentation.
We hand the system over to your team: how it works, how to monitor it and what needs updating when. We do not aim to keep you dependent on us. Ongoing support is optional. Deliverable: training, operations manual, support plan.
We do not work with everyone. The distinction below protects both your time and ours.
A good fit:
Not a fit:
There are many companies offering AI services in Türkiye: local distributors of the big platforms, general-purpose software houses and newly founded start-ups. Three things set us apart.
We built our own products. We developed three AI platforms, and they run in production today. We know how a system behaves in the field, where it breaks and what needs maintenance not from theory but from operating our own products.
We read the business side and the technology side together. Our co-founder Mehmet Cebeci has more than 20 investments and company foundations behind him; our other co-founder Emre Durgut brings 20+ years in IT and 10 years of hands-on AI development and knows first-hand what works and what looks promising but does not survive contact with the field.
We are a government-certified technology start-up. We hold the Teknogirişim badge from the Republic of Türkiye Ministry of Industry and Technology (Certificate No: 2025-0086). The certificate confirms that our status as a technology- and innovation-driven venture has been formally assessed and approved.
Problem: In global newsrooms, spotting a developing event before rival agencies is the heart of competition. But social media, local sources and official statements flow in many languages and at enormous volume; constant manual scanning is impossible.
Solution: The NewsTracker platform continuously scans multilingual sources, detects unusual activity and presents newsworthy signals to the editor in priority order.
Result: The system used by Anadolu Agency, Türkiye’s official news agency, can detect breaking news hours before it appears in the bulletins of the major agencies. Explore NewsTracker.
Problem: The easiest way to win the buy box on a marketplace is to cut the price; but that means growing revenue while eroding profit. Because sellers cannot track competitor price moves during the day, they lose either position or margin.
Solution: SellBox continuously monitors competitor prices and, for every product, accounts for commission, shipping and cost items to produce price decisions that never drop below the profit threshold.
Result: Sellers hold their buy box position without giving up their margin target; the price decision is made in seconds instead of minutes. Explore SellBox.
Problem: In industrial and wholesale sales, quote requests arrive as Excel files, PDFs or handwritten lists. The sales team matches every line to the catalogue by hand; a request with hundreds of lines can take days and human error is inevitable.
Solution: The Proposal Robot reads the incoming request, matches the items to the company catalogue, prices them and produces a PDF quote in the corporate format.
Result: 95% accuracy in catalogue matching; quote preparation that used to take days now takes minutes. Explore the Proposal Robot.
The discovery phase takes 1-2 weeks and a working prototype is delivered within 2-4 weeks. The total time, including the move from prototype to production, is usually 2-3 months depending on scope. Projects that require integration with many systems or where the data infrastructure has to be built first can take longer; we say so clearly at the feasibility stage.
Yes. The systems we build are designed to work via API with ERP, CRM, accounting software and internal databases. You do not need to replace your existing software. Whether integration is technically possible becomes clear in the discovery phase; if you work with a closed system, we flag it from the start.
Customer data is processed only within the project scope and under the conditions explicitly set out in the contract. Compliance with KVKK (Turkish data protection law) / GDPR, secure API integrations and role-based access controls are standard components of every project. Where data must stay inside the company, the system can be installed on your own infrastructure.
Not during the project; we build the system. After handover, however, it pays in the long run to have a technical owner who monitors the system and updates it when needed. That person does not need to be an AI specialist; our handover training is designed so that your existing IT team can operate the system.
AI projects usually fail not for technical reasons but because of expectation management and data quality. That is why we work in stages: at the prototype stage you see whether the approach works, before any large investment. When we notice something is not going as planned, we say so immediately; continuing a project that will not deliver is in nobody’s interest.
Optionally, yes. At handover we teach your team the system and make it able to run independently. Beyond that, a support agreement can cover maintenance, model updates and new feature development. Keeping you dependent on us is not our goal.
If you are not sure which of your processes is suited to an AI solution, this call is exactly for that. We do not make sales pitches; we do technical assessments. Describe your problem and get an honest answer, including “you do not need AI” when that is the truth.
