LLM integration services
LLM integration means connecting a large language model such as GPT, Claude or Gemini to your existing software, ERP or CRM through an API, so your screens gain a new capability: summarising, classifying, extracting fields or drafting text. Corrplus picks the model on evidence, plans cost and data residency up front, and ships the feature in a way you can measure.
Book a free callTiming
The first live version of a single feature usually ships within 3–6 weeks
Pricing
Fixed-scope project quoted after the discovery call; model usage billed to your own account; optional monthly operation.
Tools we use
OpenAI API, Anthropic Claude API, Google Gemini API, Azure OpenAI, Amazon Bedrock, Open models (Llama, Mistral, Qwen) with vLLM
Who it is for
- SaaS and software companies that want AI features in their own product
- Companies whose ERP (SAP, Microsoft Dynamics, or Turkish systems such as Logo, Mikro and Netsis) is full of free-text notes and document fields
- Teams that want HubSpot, Salesforce or Dynamics 365 records summarised, classified and enriched
- Teams that built an OpenAI API prototype but could not go live because of cost, latency or data residency
When it is not a fit
- Companies that have not yet decided where AI should go. We clarify the use case first through AI consulting.
- Teams that only want staff to use ChatGPT or Copilot. That is a licensing and training question, not an integration.
What you get
- Model selection reportSeveral models compared on your own sample data for the same task: quality, latency, cost per transaction and processing region.
- Integration layerA service between your software and the model provider that makes switching models a configuration change, with key management, retries and caching.
- Prompts and output schemaVersioned instructions and structured (JSON) output rules so the model never returns malformed data to your system.
- Evaluation setTest cases built from real examples; quality is measured automatically whenever the model or prompt changes.
- Cost and usage monitoringToken consumption per feature, customer or user, with budget alerts.
How we work
Discover
Together we define which screen the feature lives on, which data it uses, who it serves and how success is measured.
Design
We compare candidate models on your sample data, decide where personal data goes under GDPR and Türkiye's data protection law (KVKK), and write the architecture and quote.
Build
We build the integration layer and the first feature in short cycles and release it to a limited user group first.
Run
We monitor quality and cost, test new model versions against the evaluation set and switch when it pays off.
What problems does LLM integration solve?
Free text is usually the least processed data in your systems: the description on a customer request, the fault note on a service form, a supplier email. A language model can read that text and turn it into structured data, or draft something from it. Typical examples:
- Classifying the fault note on a service ticket and assigning it to the right team
- Pulling a summary and next step out of call notes in your CRM
- Writing the order number, date and amount from a supplier email into ERP fields
- Rewriting product descriptions to each marketplace’s rules
- Adding a “summarise” or “draft” button to your own SaaS product
Which model, running where?
Model choice is not a single decision. It is a balance between quality, cost, speed and where data is processed.
| Option | When it fits | Where data is processed | Watch out for |
|---|---|---|---|
| OpenAI API | Broad range of tasks, mature tooling | Provider’s cloud | Cross-border transfer assessment |
| Anthropic Claude API | Long documents and tool use | Provider’s cloud | Cross-border transfer assessment |
| Google Gemini API | Google Cloud users, image and document input | Google Cloud, with region choice | Free-tier terms differ |
| Azure OpenAI | Organisations on Microsoft infrastructure | Chosen Azure region | Enterprise agreement and quota |
| Open models (Llama, Mistral, Qwen) | Data must not leave your servers, or volume is very high | Your own servers | GPU cost and maintenance |
We build the integration layer to be model-agnostic. Starting with one model today and moving to a cheaper or better one tomorrow does not require changing your code.
How is it different from an AI agent or RAG?
An LLM integration is a single, defined task: known input, known output, running in an existing screen of your software. An AI agent plans several steps toward a goal and executes them with tools; it is more flexible and harder to control. A RAG knowledge assistant is a separate application where employees ask questions of company documents. An integration can use RAG, but its purpose is to speed up an existing step in your process, not to make documents searchable. If you want predictability, start with an integration.
How do we start?
In the first call we look at the screen where you want AI and a few real data samples. We then run candidate models on those samples and show quality and cost per transaction side by side. The decision rests on that comparison, not on guesswork. If the scope is not clear yet, we begin with the two-week AI discovery sprint.
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
Connecting AI agents to systems with MCPIntegration patterns, authorization, prompt injection controls and human approval for connecting AI agents with MCP.
Copilot Studio or a custom AI agent?Platform or custom build: how data sources, MCP, governance, maintenance load and the drivers of cost compare.
Getting Turkish e-invoices into your ERP automaticallyMethods, matching steps and error checks for moving Turkish e-Fatura, e-Arşiv and PDF invoices into an ERP automatically.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.
- Prompt engineeringPrompt engineering is the practice of writing, testing and refining the instructions, examples and context given to a language model so that it produces consistent, accurate results.
- 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.
- 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.
- EmbeddingAn embedding is a fixed-length array of numbers (a vector) that represents the meaning of a word, sentence, document or image, so that similar content ends up close together.
Frequently asked questions
Which model should we choose: OpenAI, Claude or Gemini?
There is no single right answer. We run the same task on several models with your sample data and put quality, speed and cost per transaction side by side. The integration layer makes switching easy, so today's choice does not lock you in.
How do you keep API costs under control?
Simple tasks go to smaller, cheaper models, repeated requests are cached and unnecessary context is trimmed. We track consumption per feature and per user and alert you before a budget limit is reached.
Will our data be used to train the model?
The major providers state that data sent through paid API and enterprise plans is not used for training by default; free tiers can differ. We check current terms for each provider with you. If you need full control, we run an open model on your own servers.
What if data must stay in the EU or in Türkiye?
Options include regional deployments on Azure OpenAI or Google Cloud, or running open models such as Llama, Mistral or Qwen on your own servers or in a data centre in Türkiye. Cross-border transfer is assessed with your legal counsel.
Can you integrate with legacy or local ERPs?
Usually yes. We read data through the database, web services or file exports and write the model's result back to the right field. Adding a small service next to the ERP, without modifying the ERP itself, is our most common route.
Do we need fine-tuning?
In most projects, no. Well-written instructions, examples and, where needed, RAG are enough. Fine-tuning is worth considering when you need a consistent format at very high volume, or a small model specialised for one task.
LLM integration services: 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.