Toptal delivers dual AI solutions for tech firm, enhancing access and language processing.
Facing limited Arabic training data and rising demand for smarter document access, a leading AI technology firm turned to Toptal to scope and develop scalable solutions for natural language processing and retrieval-based chat interaction.
Client
A Saudi-based AI technology firm building Arabic NLP and document-retrieval chatbot solutions.
Employees
Revenue
Industry
TechnologyDelivered Services
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The client lacked annotated Arabic named entity recognition (NER) data for model development and needed an intuitive way for users to access dense legal and training documents without relying on human advisors.
Solution
Strategic LLM Scoping
Toptal conducted a detailed scoping initiative for fine-tuning a large language model (LLM), delivering a technical report with time and cost estimates, risk assessment, and alternative solutions to support Arabic NER development.
Agentic RAG Chatbot
Toptal designed a retrieval-augmented generation (RAG) solution using OpenSearch, Docker, FastAPI, LangChain, and Haystack to implement a chatbot capable of accessing indexed documents asynchronously.
Outcome
Informed Client Decisions
The analysis helped the client confidently evaluate multiple NER pathways, making informed choices on timeline, cost, and implementation feasibility for Arabic language model deployment.
Reduced Manual Support
The solution allowed users to engage in multiturn conversations with a document-aware chatbot, reducing reliance on human advisors and streamlining access to regulatory and educational content.
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