Generative AI in practice
Practical business use cases
Generative AI is currently deployed across most business functions:
- Customer service - qualified chatbots capable of understanding a complex request and proposing a solution, or intelligently forwarding it to the right contact.
- Marketing & content - generation of product descriptions, creative briefs, A/B variants, large-scale multilingual translation.
- Internal productivity - documentary assistants that summarise, classify and retrieve information from the company's knowledge bases (CRM, drive, intranet).
- Software development - acceleration of prototyping, code review, test generation, automatic documentation.
- R&D and innovation - faster exploration of ideas, synthesis of studies, generation of simulations.
For an SME or mid-sized company, the first gains are often found in the automation of repetitive tasks with high informational value: email processing, document classification, report writing.
How to integrate generative AI into a product?
Three approaches coexist:
- Calling an LLM via API (OpenAI, Anthropic, Mistral, Google) - the fastest to implement, at the cost of dependence on an external provider.
- Self-hosted open-source model (Llama, Mistral) - more sovereignty and cost control, provided that a suitable infrastructure is operated.
- RAG (Retrieval-Augmented Generation) - the LLM is enriched with the company's proprietary data. This is the reference architecture for reliable and up-to-date internal assistants.
Beyond the model, the quality of an AI product primarily relies on the orchestration chain: prompts, guardrails, agents, memory, observability. This is where reliability in production is crucial.
Vigilance points before launching a generative AI project
- Hallucinations - an LLM can produce a false answer confidently formulated. Source traceability, evaluation tests and guardrails are essential in production.
- Confidentiality & GDPR - the choice of provider, data localisation, and prompt anonymisation must be decided very early on.
- Inference cost - at scale, API calls can be expensive. A serious project continuously tracks its cost per request.
- Adoption - an AI assistant only has value if it is used. User experience and integration into existing tools weigh as much as model quality.
Why seek support?
There is a considerable gap today between a prototype that impresses in a demo and a reliable AI product in production. Prompt architecture, RAG, observability, security, evaluation tests, cost control: these are new subjects for most internal teams.
At Galadrim, we design and develop products integrating Generative AI for SMEs, ETIs and large corporations - from business chatbots to autonomous agents, including document automation. If you wish to explore an AI use case for your company, we can help you frame it, prototype it and deploy it.
