RAG & Knowledge AI

Make AI Answer From Your Knowledge — Not the Internet

Private. Accurate. Secure. Enterprise-Grade RAG Systems.

AGIX builds RAG & Knowledge AI systems that allow AI models to retrieve, reason over, and answer questions using your internal data — documents, systems, policies, and knowledge bases — without exposing it to public models.

This is how businesses move from AI demos to AI systems they can trust.

Assess Your Knowledge AI Readiness

The Real Problem With AI in Businesses Today

Most companies experimenting with AI face the same issues:

AI gives confident but wrong answers

Responses change unpredictably

Sensitive data cannot be shared with public LLMs

Knowledge is scattered across tools and documents

Employees don't trust AI outputs

The result? AI becomes a demo tool — not a business system.

Why This Happens

Large Language Models:

Do not know your internal data
Do not understand your policies
Do not have access to live systems

They generate responses based on training data, not your knowledge.

This leads to:
Hallucinations
Outdated answers
Compliance risks
Loss of trust

What RAG & Knowledge AI Actually Solves

RAG (Retrieval-Augmented Generation) is the architecture that allows AI to work with your data.

1

Retrieve

Retrieve the right internal information

2

Ground

Ground responses in verified knowledge

3

Generate

Generate answers based on facts, not guesses

4

Secure

Keep data private and secure

RAG turns AI from a storyteller into a reliable analyst.

What RAG & Knowledge AI Means at AGIX

At AGIX, RAG & Knowledge AI is not just vector databases, embeddings, or document upload + chat.

It is a knowledge system designed as enterprise infrastructure, not experiments.

Data ingestion pipelines
Knowledge structuring
Vector indexing
Access control
Retrieval logic
LLM reasoning
Accuracy validation
The AGIX Knowledge Intelligence Architecture

A 6-Layer System Built for Accuracy & Trust

RAG systems fail not because AI is weak — they fail because knowledge is poorly structured, retrieved incorrectly, or not governed.

1

Knowledge Discovery & Source Mapping

The Foundation Most Teams Skip

Before touching AI models, AGIX maps what knowledge exists, where it lives, which sources are authoritative, which are outdated, and who owns each knowledge domain.

Outputs:

Knowledge source map
Ownership & freshness tagging
Priority-based ingestion plan
2

Knowledge Structuring & Chunking

Where Accuracy Is Won or Lost

We design chunking based on document type, semantic boundaries, question-answer patterns, and hierarchical structure. Each chunk includes source reference, section context, timestamp, and access permissions.

Outputs:

Semantic chunking strategy
Metadata enrichment
Context preservation
3

Vector Indexing & Retrieval Logic

Semantic Search Done Correctly

AGIX optimizes for relevance, precision, recall, and freshness using semantic similarity, hybrid search, metadata constraints, top-k tuning, and confidence thresholds.

Outputs:

Vector database setup
Hybrid search configuration
Retrieval optimization
4

Grounded Generation & Answer Control

Stopping Hallucinations by Design

AI can only answer using retrieved context. If context is insufficient, AI says 'I don't know'. Source citations included. Confidence scoring per answer.

Outputs:

Prompt constraints
Response validation
Fallback logic
5

Access Control, Privacy & Governance

Critical for SMBs & Enterprises

AGIX enforces role-based access control, data isolation, knowledge-level permissions, audit logs for every query, and secure API boundaries.

Outputs:

Role-based access
Audit trails
Compliance readiness
6

Monitoring, Evaluation & Improvement

Why RAG Systems Improve Over Time

We measure retrieval accuracy, answer relevance, user feedback, knowledge gaps, and drift over time. Re-chunking, index refresh, and prompt refinement prevent decay.

Outputs:

Performance dashboards
Knowledge versioning
Continuous tuning

Core Capabilities & High-Impact Use Cases

Each capability represents a deployable system, not a feature.

Simple & Clear Pricing

AGIX pricing for RAG systems is scope-based, not per-document or per-token hype.

Tier 1

Starter RAG

SMBs / Teams

$8K – $15K

Best for:

  • Small teams
  • Internal knowledge assistants
  • Limited document sets

Includes:

  • Knowledge discovery & ingestion
  • Clean chunking & vector indexing
  • Secure RAG pipeline
  • Internal chat UI
  • Source-linked answers
Timeline:4–6 weeks
Tier 2

Business RAG System

Growing / Mid-Market

$15K – $35K

Best for:

  • Multiple departments
  • Enterprise search
  • Internal AI assistants

Includes:

  • Multi-source ingestion (docs + tools)
  • Advanced chunking & metadata
  • Role-based access control
  • Monitoring & tuning
  • API + UI integration
Timeline:6–8 weeks
Tier 3

Enterprise Knowledge AI

Large Organizations

$35K – $75K+

Best for:

  • Large organizations
  • Compliance-driven environments
  • Customer-facing knowledge systems

Includes:

  • Large-scale ingestion & indexing
  • Strict governance & audit logs
  • Private LLM grounding
  • Multi-role access control
  • High availability & observability
Timeline:8–12 weeks
What Actually Drives Cost

Volume of knowledge

Not just number of files, but depth and complexity

Structure quality

Clean vs messy data affects processing

Access control complexity

Role-based permissions add layers

Accuracy requirements

Higher accuracy needs more validation

Compliance & audit needs

Regulated industries require more governance

Internal vs customer-facing

External use requires stricter controls

AGIX prices reliability and trust, not "AI features."

Interactive Assessment Tools

Make a clear, informed decision — without guesswork.

Is Your Data Ready for RAG?

This tool prevents failed implementations.

Do you have structured documents (SOPs, policies, guides)?

Is ownership of knowledge defined?

Are documents updated regularly?

Is access control required?

Is accuracy critical (legal, financial, operational)?

RAG Cost & ROI Estimator

Current Annual Cost

$312,500

6,250 hours lost/year

Estimated Annual Savings

$218,750

4,375 hours saved/year

Estimated Payback Period

2 months

Based on 70% reduction in search time

Buy vs Build vs DIY

DIY RAG

High effort, poor accuracy

Generic tools

Limited control & trust

AGIX RAG

Reliable, secure, scalable

Frequently Asked Questions

RAG is the foundation that transforms AI from a generative tool into a reliable business system.

Ready to Turn Your Knowledge into a Reliable AI System?

Get a Custom RAG & Knowledge AI Plan

Clear scope · realistic timeline · accurate cost · no hype

Request Your Knowledge AI Assessment
Confirmation if RAG is the right approach
Knowledge readiness evaluation
Architecture recommendation
Clear build vs phase-wise roadmap

We'll recommend automation, conversational AI, or RAG — based on what actually fits your business.

Accurate AI starts with knowledge.

RAG is how businesses make AI trustworthy.