Data & Knowledge

Managing the persistent memory and performance layers of your AI infrastructure.

An AI Agent is only as good as the information it has access to. In Iqra AI, we separate data into distinct categories to optimize for Accuracy, Latency, and Token Usage.

Global vs. Session Data

This section covers Global Data (Facts about your business that rarely change).

If you are looking to store data specific to a single phone call (like the user's name or booking ID), use Script Variables instead.

Data Layers

1. Business Context (Structured)

The Hard Facts. Highly structured entities defined in the dashboard.

Examples: Branch Locations, Opening Hours, Product Catalog.
Strategy: "Summary Injection." The agent gets a list of product names in its prompt (low cost), but uses a tool to fetch the full specs only when asked (high detail).

2. Knowledge Base (Unstructured)

The Library. Documents ingested via a RAG (Retrieval Augmented Generation) pipeline.

Examples: PDF Manuals, Policy Docs, FAQs.
Strategy: "Vector Search." The system chunks your documents and retrieves only the relevant paragraphs based on the user's query.

3. Cache (Performance)

The Short-Term Memory. Storing generated outputs to save time and money.

Examples: TTS Audio files, LLM Responses, Vector Embeddings.
Strategy: "Exact Match." If the system has seen this input before, it skips the AI generation entirely.