AI consulting
Huaris AI is the AI consulting brand of Huaris Technology. We select OpenAI, Google and Anthropic technologies to fit your business needs and integrate them without compromising your data security.
Five core services cover every stage of your AI journey, from strategy to team training.
We review your existing workflows (HR, finance, customer service, operations). We map which processes AI can automate, where it can reduce cost and where it can raise efficiency.
Deliverable: A company-specific AI transformation roadmap and ROI analysis.
We choose the large language model (LLM) and tools that best fit your needs, budget and data policy. We are not tied to a single vendor.
Deliverable: A recommended technology stack that matches your needs and budget.
Learn more - Model Selection and Vendor-Independent Advisory
We design RAG (Retrieval-Augmented Generation) architectures for assistants that know your own data (PDFs, emails, databases).
Deliverable: System designs that ground answers in your data, cite sources and reduce hallucination risk through verification and evaluation tests.
Enterprise API setups, integration of open-weight models running on your own servers (on-premise) where needed, and AI-integrated web and mobile application development when required.
Deliverable: Secure AI infrastructure with a documented data flow, designed with GDPR and KVKK requirements in mind.
Installing the technology is not enough; people need to use it well. We teach teams to use Copilot, ChatGPT or custom assistants and to write effective prompts.
Deliverable: An AI literacy programme and department-specific prompt libraries (marketing, engineering, HR).
We look at model families and use cases rather than version numbers. Models change quickly, so we review this table regularly.
| Model family | Known for | Typical use |
|---|---|---|
| Anthropic Claude | Long context, analytical reasoning, coding | Complex document analysis, software development support |
| Google Gemini | Large context window, Google Workspace integration | Organisations on Workspace, multimodal content |
| OpenAI GPT | Rich API and assistant ecosystem | General-purpose assistants, broad integration needs |
| Meta Llama / Mistral | Open-weight models, full control | On-premise deployment, cases where data must not leave the organisation |
Last updated: September 2026. Logos and product names belong to their respective owners; Huaris AI does not claim to be an official partner of these companies.
We are not tied to one technology. Google, OpenAI or Anthropic, we recommend what fits you best.
We work with Enterprise APIs and, where needed, on-premise architectures so that your company data is not used for model training. We document the data flow.
We do not stop at advice. When needed we write the code, build the product and train your team.
It depends on the provider and the plan. For individual plans, whether content is used to improve the product depends on the settings and the provider's terms. For enterprise and API offerings, providers generally state that data is not used for model training by default.
We verify each provider's current terms with you at the contract stage.
If personal data is processed, topics such as the duty to inform, legal basis, data minimisation, data processing agreements and cross-border transfer come into play. Where possible, architectures that never send personal data to the model are preferred.
This is a general overview and does not replace legal advice.
RAG (Retrieval-Augmented Generation) means the model first retrieves relevant passages from your own documents and grounds its answer in them. Answers can cite sources and the risk of hallucination is reduced, but not eliminated, which is why we run evaluation tests.
Setup time depends on data volume and integration scope; we clarify it in the discovery call.
Data sensitivity, regulatory requirements, budget, expected quality and operating capacity decide. When data must not leave your organisation, an on-premise open-weight model is usually the choice; otherwise an Enterprise API is generally more practical.
Let us listen to your processes and goals, and evaluate together where AI can add value.
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