AI consulting
AI consulting helps you decide where AI will create value in your business, with which data and at what cost. Corrplus reviews your processes and data, ranks the use cases, chooses models and tools, and plans a first pilot with data protection in mind. You end up with a roadmap you can act on, in an order that makes sense.
Book a free callTiming
Starts with a two-week discovery sprint; the roadmap is delivered at its end
Pricing
The discovery sprint is fixed in scope. Pilots and later work are quoted in writing after the discovery call.
Tools we use
OpenAI GPT models, Anthropic Claude, Google Gemini, Azure OpenAI, Open-source models (Llama, Mistral), pgvector and Qdrant
Who it is for
- Small and mid-sized companies that want to use AI but cannot decide which work to start with
- Leaders whose teams already use ChatGPT or Gemini informally and want to bring order to it
- Teams that ran an AI experiment, got little out of it and want to know why
- Companies that want an independent technical view before briefing a development team
When it is not a fit
- Work where the decision is made and only the build is missing. We go straight to our development services.
- Very early start-ups with no data and no defined processes yet. The digital basics come first.
What you get
- Use case inventoryA list of the work where AI could contribute, gathered from interviews with your teams, with a short description of each.
- Priority matrixEach use case scored on business value, data readiness, technical difficulty and risk.
- Data and privacy pre-assessmentWhich data would be used, whether it is personal data and where it would be processed. Legal interpretation stays with your legal counsel.
- Model and tool recommendationA reasoned choice between an off-the-shelf model, RAG over your own data or an open-source model on your own servers.
- Pilot planScope, success criteria, data and people needed for the first use case, and how the pilot will be judged.
- RoadmapA written plan showing which use cases follow in which order and what they depend on.
How we work
Discover
A free first call to hear your goals, then a two-week discovery sprint where we interview teams and collect use cases.
Design
We rank the use cases, assess data and privacy, and write down the architecture and model choice.
Build
We build the first use case as a small pilot on real data and judge it against the agreed criteria.
Run
We update the roadmap with what the pilot taught us and hand the work that scales to the relevant build service.
What does AI consulting cover?
The job is to turn “we should be using AI” into specific pieces of work. We talk to your teams and look for tasks that repeat, are heavy on text or documents, or hold up decisions. For each one we ask three questions. Would AI actually do this better? Does the data exist? What does it cost when the output is wrong?
Common candidates in the companies we work with: sorting requests that arrive by email or WhatsApp, finding answers in contracts and technical documents, drafting quotes and reports, and forecasting demand from sales history. Each needs different data and a different kind of model.
Which use case should come first?
| Use case type | Data needed | Technical difficulty | Typical first step |
|---|---|---|---|
| Document Q&A (RAG) | An organised document archive | Medium | Pilot with one department’s documents |
| Text classification and summaries | Sample emails or requests | Low | Add one step to an existing workflow |
| Demand or sales forecasting | Consistent historical records | Medium to high | Measure data quality first |
| Multi-step AI agent | Systems with APIs | High | A narrow version with human approval |
We usually recommend starting with a use case whose value is clear, whose data is ready and whose mistakes can be undone. The first pilot exists so your team sees AI working in its own context and so the next decisions rest on real evidence.
How is this different from digital transformation consulting?
Digital transformation consulting looks at your processes, systems, data and people as a whole, and AI is one tool among several. AI consulting is narrower. It focuses only on where and how AI should be used and settles the model and architecture. If your systems are already in reasonable shape, you can start here.
Once decisions are made, the build moves to the relevant service: an enterprise knowledge assistant with RAG for company documents, LLM integration to add a model to software you already run, or AI agent development for multi-step work.
We ask the same questions in our own products. Building the website-aware assistant in Promptor meant deciding which content to use, which model to pick and how to check its answers. We use that same checklist on client projects. For teams in Europe, we also map each use case against GDPR and, where relevant, the EU AI Act.
How we start
In a free first call we listen to your goals and to what you have already tried. When a fuller assessment is needed, the two-week AI discovery sprint collects and ranks the use cases and ends with a delivered roadmap.
How would this work in your business?
Tell us where you are in a free first call; we will say plainly where it makes sense to start.
Related services and use cases
Further reading
Does the EU AI Act apply outside the EU?When the EU AI Act reaches companies outside the EU: scope, roles, risk levels and the current application timeline.
How to choose an LLM for your businessCriteria, access routes, data retention, open models and a test method for choosing a language model for business use.
How to pick your first AI use caseScore candidate AI use cases on six criteria, filter them for data and risk, and define a pilot you can measure.Our own products
We use the tools and checklists from client projects on our own products too.
All products →Key terms
- LLM (Large Language Model)An LLM (large language model) is an AI model trained on very large amounts of text that answers questions, summarises, translates and writes by predicting what text should come next.
- RAG (Retrieval-Augmented Generation)RAG (Retrieval-Augmented Generation) is an AI architecture that lets a language model retrieve relevant passages from a chosen document collection, such as a company's own documents, and use them as context before it writes an answer.
- AI agentAn AI agent is software that uses a language model to decide which steps to take towards a goal and then carries out those steps with tools such as APIs, databases and email.
- AI hallucinationAn AI hallucination is output in which a language model presents information, a source or a figure that does not exist, fluently and confidently, as if it were true.
- Fine-tuningFine-tuning is the process of further training a pre-trained AI model on your own examples so that it performs a specific task, tone or output format better.
Frequently asked questions
How is AI consulting different from AI development?
Consulting decides what to build, in which order and with which tools. Development turns that decision into a working system. We do both, but they can be bought separately, and you are free to have another team implement the roadmap.
Is our data good enough for AI?
It depends on the use case. An assistant that answers from documents may only need a tidy document archive. Forecasting needs consistent historical records. During the discovery sprint we assess data readiness for each use case separately.
Isn't using ChatGPT enough?
For individual tasks it often is. Once AI has to work with company data, write results into another system or handle personal data, you need decisions about access, logging and where data is processed. Consulting makes that line clear.
Which AI model do you recommend?
We are not tied to one provider. We compare OpenAI, Anthropic Claude, Google Gemini and open-source models on your own data. Accuracy, cost, latency and where the data is processed decide the choice.
Can you work with a team outside Türkiye?
Yes. We work in English and Turkish, mostly remotely, with teams in different countries. Workshops can run online, and the written deliverables are produced in the language your team uses.
What do we have at the end of the discovery sprint?
A ranked list of use cases, a written scope and success criteria for the first pilot, notes on data and privacy, and an ordered roadmap. They are yours, and you decide who implements them.
AI consulting: let’s take the first step together
Tell us where you are in one call; we will share a scope and roadmap in writing.
How we start
- A free first callWe pin down the problem and the goal together.
- A 2-week discovery sprintWe look at the data, the process and the tools, and decide where to start.
- A written proposalScope, architecture and timeline, in writing.