Intelligent Knowledge
Retrieval at Scale

In order to overcome the challenge of having to search through large amounts of data to answer questions, Altrosyn proposes a highly performant and scalable AI system using Retrieval Augmented Generation (RAG) with no vendor lock-ins. This system will be built for enterprise needs with selectable LLMs for controllable, transparent pricing.

In the pilot phase we will develop a small scale version of the final AI system, using the same building blocks that will be scaled for the final version. This pilot will be limited to ingesting simple documents (i.e. PDF) to demonstrate proof-of-concept. Upon acceptance of this, the full enterprise AI system can be developed with the capability to ingest various forms of content including documents, video transcripts, website content, etc.

Simple Explanation

How It Works

Four connected components work together to create intelligent responses from your content. Think of it as a smart librarian that knows exactly where to find information.

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Content Ingestion

Your PDFs and website content are processed and organized

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Smart Storage

Information stored in a way that enables intelligent searching

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Question Processing

User questions are understood and matched to relevant content

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Intelligent Response

AI generates natural answers with source links

System Design

RAG Pipeline Architecture

A sophisticated data flow that transforms user queries into intelligent, contextual responses through advanced vector search and retrieval-augmented generation.

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User Query

Natural language input processed and analyzed for intent

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Embedding

Gemini models convert text to high-dimensional vectors

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Vector Search

Semantic similarity matching for relevant content

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Context Retrieval

Relevant documents fetched from Supabase knowledge base

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LLM Generation

Switchable AI models generate contextual responses

Response

Intelligent, accurate answer delivered to user

Technical Foundation

Scalable Technology Stack

Enterprise-grade components chosen for reliability and growth potential. Each piece can be upgraded independently as your needs evolve.

Supabase

Primary Database

  • PostgreSQL-based reliability
  • Real-time subscriptions
  • Row-level security
  • Auto-scaling infrastructure

Cloudflare

Edge Computing

  • Global edge network
  • Zero cold starts
  • DDoS protection
  • Workers AI integration

Gemini

Embedding Models

  • State-of-the-art embeddings
  • Multi-language support
  • Competitive pricing
  • Google infrastructure

Switchable LLMs

Language Models

  • Provider flexibility
  • Model comparison
  • Cost optimization
  • Future-proof design
Pricing

Cost Structure

Flexible pricing options tailored to your organization's needs.

Interactive LLM Cost Calculator

Adjust parameters to see real-time cost estimates for LLM usage and infrastructure components

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Supabase Database, Cloudflare Pro, and Cloudflare Workers

Active Users
5,250
Monthly Queries
21,000
LLM Monthly Cost
$756
Infrastructure Cost
$50
Total Monthly Cost
$806
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Project Costs

Pilot development and Implementation

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Pilot Development (One-Time)

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Your Technical Partner

Let us become your AI technical team, enabling you to confidently present sophisticated solutions to clients while we handle the complex implementation.

Deep AI expertise on-demand
Proven custom development track record
Strategic guidance for future AI projects
Client-facing presentation support