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Modern AI applications demand more than isolated components — they require a cohesive infrastructure that connects the dots between data, reasoning, memory, and action.
Today, teams spend months stitching together fragmented solutions: ingestion pipelines, retrieval databases, inference endpoints, and front-end interfaces. The result is complexity, fragility, and inefficiency.
Frost AI Fabric changes that.
It’s a composable intelligence layer that gives enterprises all the building blocks they need — modular, interoperable, and built to integrate seamlessly into existing environments. Whether you’re powering a knowledge assistant, deploying autonomous agents, or modernizing decision-making workflows, Frost AI Fabric is the foundation you can rely on.
Frost AI Fabric is composed of five interoperable modules. Each can be deployed independently or combined into a fully integrated system. Together, they create a powerful, end-to-end platform for building intelligent applications.
🧠 FrostBridge – Inference & Orchestration Layer
Purpose: Power intelligent decision-making by connecting and managing multiple LLMs, APIs, and reasoning services at scale.
Capabilities:
Route inference tasks intelligently between cloud-based and on-premise models.
Chain together multiple reasoning steps for complex workflows.
Automate decisions using event triggers and orchestration logic.
Example Use Cases:
Build retrieval-augmented generation (RAG) pipelines that respond to changing data.
Automate financial risk scoring or compliance checks using LLM agents.
Scale inference across models from OpenAI, Groq, or Hugging Face with a single API.
Business Impact: aster time-to-market for AI applications, reduced operational complexity, and better model utilization.
🔍 FrostSearch – Knowledge Fabric & Retrieval Engine
Purpose: Transform unstructured data into a dynamic, queryable knowledge layer for AI systems.
Capabilities:
Ingest and embed documents, media transcripts, and structured data.
Provide semantic, context-aware retrieval with metadata-based filtering.
Power retrieval-augmented generation (RAG) with domain-specific knowledge.
Example Use Cases:
Enable enterprise copilots with contextually relevant answers from internal documents.
Build compliance or legal intelligence assistants that reason over policy data..
Support dynamic memory for autonomous agents.
Business Impact: Improved accuracy, deeper context, and faster time-to-insight for all LLM-driven applications.
🗄️ FrostBlock – Native Object Intelligence Storage
Purpose: Manage, govern, and retrieve unstructured intelligence natively — without relying on external object stores.
Capabilities:
Store embeddings, documents, audio, video, and structured metadata directly inside Snowflake internal stages.
Integrate storage and retrieval seamlessly into AI workflows.
Automate data lifecycle management with governance and tagging.
Example Use Cases:
Replace fragmented S3 buckets with governed, queryable storage.
Centralize embeddings for multi-model retrieval.
Enable compliant long-term retention of sensitive intelligence.
Business Impact: Lower storage overhead, stronger governance, and faster AI data pipelines.
🧭 FrostCrawler – Autonomous Ingestion & Intelligence Extraction:
Purpose: Automate the continuous ingestion, parsing, and enrichment of external data sources — from video and APIs to websites and enterprise tools.
Capabilities:
Stream, crawl, or schedule ingestion from any source.
Automatically transcribe audio/video, summarize documents, and extract metadata.
Link incoming data into your knowledge graph in real time.
Example Use Cases:
Build continuously updated intelligence databases from external content.
Monitor regulatory changes, news, or competitor activity.
Automate the enrichment of customer data for downstream workflows.
Business Impact: Real-time awareness, reduced manual work, and faster decision cycles.
👁️ FrostLens – Unified Intelligence Experience
Purpose: Deliver a single interface to explore, interact with, and control your AI infrastructure.
Capabilities:
Search and query your knowledge fabric interactively.
Visualize retrieval, context, and inference pipelines in one place.
Provide business users with conversational and analytical interfaces.
Example Use Cases:
Build internal copilots and chat interfaces for enterprise data.
Monitor regulatory changes, news, or competitor activity.
Automate the enrichment of customer data for downstream workflows.
Business Impact: Real-time awareness, reduced manual work, and faster decision cycles.
Most AI platforms are rigid — forcing you to adopt the full stack even if you only need one piece. Frost AI Fabric takes a different approach: modularity is the core principle.
This flexibility ensures faster adoption, lower costs, and a smoother path from prototype to production.
Start with one module to solve an immediate challenge.
Add others as your needs grow — ingestion, retrieval, inference, and beyond.
Compose your own workflows and connect Frost modules to existing tools.
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