Multilingual conversational agents

West African contact centres face a unique linguistic diversity: French, Modern Standard Arabic, dialectal Arabic (Hassaniya), Wolof, often blended within a single conversation. Generic chatbots, trained on English, fail on these languages and frustrate customers with off-topic answers.

On top of this sits the risk of large language models left to their own devices: they "hallucinate" plausible but false answers, unacceptable when it comes to pricing terms, regulatory procedures or financial advice. The challenge is to deliver natural, multilingual, reliable service anchored in the company's real knowledge.

Shift designs conversational assistants built on a RAG (retrieval-augmented generation) architecture. Rather than letting the model make things up, we connect the large language model to your document base: FAQs, contracts, procedures, catalogue. Every answer is generated from the genuinely relevant passages, with source citation.

The language foundation is tailored to the local context: language detection, understanding of French, Arabic (standard and dialectal) and Wolof, including code-switching and phonetic transcriptions. Intents are recognised even in colloquial or approximate language.

The assistant handles common tasks end to end — balance enquiry, order tracking, complaint, appointment booking — and intelligently hands off to a human agent with full context when the situation requires. Guardrails frame sensitive responses, and every conversation feeds continuous improvement.