RAG and an internal AI assistant for your team
We connect LLMs to knowledge and internal workflows so employees get answers faster — without drowning in manual search.
Why an internal AI assistant
Teams spend hours searching documents, Confluence, and drives. RAG connects an LLM to your knowledge base and returns answers with citations.
How it works
Index documents → build a vector store → connect an LLM with retrieval → set up quality and access controls.
Limitations we state upfront
RAG doesn’t replace experts. Quality depends on data. Hallucinations can’t be removed 100%. We design with these constraints in mind.
Process & artifacts
We run projects in six stages: discovery, product logic, UX and scope, AI-assisted delivery, QA and handoff, support and evolution. At each stage you get clear artifacts and demos — no black box.
Full YappiX processRelevant cases
Projects with a similar context — methodology and artifacts on the case page.
realLaw
realLaw AI — Legal Tech SaaS для ОАЭ
Legal-tech SaaS для бизнеса и юристов ОАЭ. Полный цикл: исследование, бренд, дизайн-система, фронтенд на Next.js/Framer.
Food Delivery
Ассистент заявок — голосовой заказ еды
Голосовой AI-ассистент для заказа еды без касания экрана. Идеально для водителей и людей с ограниченными возможностями.
Related focus areas & services
Go to pillar pages for methodology, or to services for scope and formats.
Services
FAQ
What data can be connected?
Documents, wikis, Confluence, Google Drive, Notion, internal databases — PDF, DOCX, HTML, Markdown.
How do you control answer quality?
Metrics: accuracy, relevance, coverage. Logging for all requests. Data contour boundaries.
Ready to discuss your project?
We’ll unpack regional context, the product, and a collaboration format — without a forced scope.