AI-powered kiosk that fully replaces in-store sales associates

Human staff take breaks, call in sick, resign, and typically lack full knowledge of the product catalog. Sonotiv operates 24/7, has full awareness of your SKU database and technical documentation, and is designed to replace frontline retail personnel while automating routine consultation workflows. The result: reduced payroll expenses and increased average order value through systematic cross-selling.

It depends on foot traffic, but the economics are straightforward: one kiosk replaces at least 1.5 FTEs (accounting for shift rotation). In addition, you eliminate lost revenue from customers who leave without assistance. In most deployments, ROI turns positive in year two.

Low-cost solutions are typically thin wrappers around public LLM APIs. They suffer from high latency (up to 30 seconds per response) and unpredictable token-based costs. We deliver an engineered system: an optimized multi-model orchestration stack, custom hardware configuration, and sub-5-second response times — effectively setting a new industrial benchmark for AI retail assistants.

We implement Retrieval-Augmented Generation (RAG). Sonotiv responds strictly based on your knowledge base, technical sheets, and official documentation. It does not invent answers — it retrieves verified information from your internal data sources. If your documentation states “Product X is compatible with Product Y,” that is exactly what it communicates. All unsupported claims are programmatically restricted.

Cross-selling and upselling are core functionalities. Conversion logic and aggressive cross-sell strategies can be embedded directly into the recommendation pipeline.

The system is trained to understand context and industry-specific slang across verticals (beauty, construction, electronics, etc.). If a customer asks “How do I fix a hole in concrete?”, Sonotiv interprets the intent correctly (e.g., filler rather than tape), asks clarifying questions, and surfaces the appropriate product.

No. Our strict SLA guarantees a relevant response within 4–5 seconds. We do not route every query to heavyweight models. Simple queries are processed instantly; complex ones are handled by high-performance inference servers. Customers will not stand waiting at the shelf for 30 seconds, as often happens with low-grade chatbot implementations.

Yes — with any ERP or CRM system. Integration and ongoing technical support are included in our scope of work.

The default deployment model is our secure cloud infrastructure, which enables faster and more cost-efficient onboarding. If your security policy requires on-premise deployment, we can install the system within your infrastructure. However, this requires dedicated GPU hardware investment, as LLM inference infrastructure is capital-intensive.

Your data: product catalogs, internal guidelines, PDF documentation, website content. Manual dataset annotation is not required — we automatically parse and structure raw documentation.

No. It is a multi-model orchestration layer. We do not send raw user prompts directly to a single LLM. The architecture includes intent classifiers, query normalizers, ranking models, and LLM components operating in coordination.

We use optimized chunking strategies and retrieve only contextually relevant documents along with structured metadata (price, availability, SKU data). This keeps the context window efficient and reduces inference costs significantly compared to naïve RAG implementations.

We optimize for Time to First Token (TTFT). This includes pre-caching frequent queries (Redis), response streaming (the user sees the text as it is generated), and prompt optimization (fewer input tokens result in faster generation). Our SLA guarantees a full response within 5 seconds. If the system detects that generation may take longer (e.g., a complex calculation), it returns an intermediate status message (“Calculating…” ) to maintain user engagement.

It depends on RPS (Requests Per Second). A single A100/H100-class GPU cannot handle high concurrency alone — requests will queue, and latency can spike to unacceptable levels. For stable on-premise performance, you will need a GPU inference cluster (minimum 2× A6000 or equivalent) along with a load orchestration layer (such as vLLM or TGI). If you do not have existing ML infrastructure, we can provide deployment within our cloud environment.

No. We do not fine-tune the model for every new product — that approach is costly and time-consuming. Instead, we update the vector index. When pricing changes in your ERP or new products are added, our parser refreshes embeddings in the vector database. The assistant becomes aware of new items within 5–10 minutes, with zero downtime.

REST API and gRPC for data exchange. WebSockets for streaming responses. We ingest data from XML/YML feeds, SQL databases, or via your PIM system’s API. On output, we provide structured JSON for POS systems, including cart generation.