PRIVATE RAG • Enterprise AI & RAG • SECURE AI SEARCH

Turn Your Business Knowledge Into Secure, Useful AI

Give your team a secure way to search company knowledge, ask questions in plain language, receive answers with source citations, and build AI agents around the information and systems your business already uses.

Nala Networks designs and implements enterprise AI solutions using private infrastructure, Google Cloud, or AWS based on your security requirements, existing cloud environment, data, use case, and budget.

Self-Hosted · Google Cloud · AWS

 Discuss Your AI Project

System Overview

Nala Private AI is a private search, question-answering, and knowledge-assistance platform for enterprise organizations. It combines enterprise search with retrieval-augmented generation (RAG): when an employee asks a question, the platform retrieves relevant passages from approved internal sources and returns an answer with citations to the source documents.

The fundamental principle: the model is never trained on your organizational data. It receives only what is retrieved at the moment each question is asked. This makes answers current, verifiable, and scoped to what each employee is permitted to access.

Fig. 01 — Retrieval-Augmented Generation Pipeline

Retrieval-Augmented Generation Pipeline

The AI model receives only the passages retrieved at query time for the requesting employee. It does not store organizational knowledge between sessions and cannot leak what it was never given.

What the organization receives

  • A private knowledge assistant that answers questions about your organization specifically, not general public knowledge.
  • Access-scoped search that returns only documents the asking employee has permission to see — permission rules from your existing identity system apply.
  • Answers with source citations that employees can verify and auditors can trace back to original documents.
  • A self-hosted AI model running on infrastructure you own. No organizational data reaches an external AI provider.

What We Can Build

Solutions are identified during Discovery. The following represent the types of enterprise AI systems we design and implement across private, Google Cloud, and AWS environments.

Enterprise Knowledge Assistant

Give employees a secure interface for company knowledge. Ask questions and receive answers grounded in approved internal sources with citations.

Technical Document Search

Search manuals, drawings, specifications and engineering documentation using semantic understanding and exact identifiers. Part numbers and drawing numbers treated as precise matches.

RAG & LLM Applications

Add retrieval and generative AI capabilities to existing applications without rebuilding your entire software environment.

AI Research Assistants

Research across multiple internal sources, summarize findings, compare documents and return supporting references.

AI Agents

Move beyond question-and-answer. Agents can retrieve company knowledge and interact with approved tools, APIs and business systems to complete multi-step tasks.

Document Intelligence

Extract and understand information from PDFs, forms, specifications, reports, tables and other business documents.

One AI Solution. Three Deployment Options.

There is no single AI architecture that makes sense for every organization. Some require complete isolation. Others already operate on AWS or Google Cloud. We help determine which approach fits your infrastructure, security requirements, and operational preferences.

Option 01

Private / Self-Hosted AI

Data residency
Your infrastructure
Infrastructure ownership
You
Best for
Sensitive data, strict controls, on-premise or private cloud
Air-gap capable
Yes

Option 02

Google Cloud Enterprise AI

Data residency
Your GCP account and region
Infrastructure ownership
You
Best for
Organizations on GCP, Gemini models, managed enterprise AI
Air-gap capable
No

Option 03

AWS Enterprise AI

Data residency
Your AWS account and region
Infrastructure ownership
You
Best for
Organizations on AWS, Amazon Bedrock, managed AI infrastructure
Air-gap capable
No

Option 04

Hybrid

Data residency
Configured per workload
Infrastructure ownership
You
Best for
Mixed security requirements across environments
Air-gap capable
Per workload

Canadian company, Canadian delivery. Nala Networks is Toronto-based. For organizations with Canadian data residency requirements including those subject to PIPEDA, provincial privacy legislation, or public-sector data governance obligations we offer deployment inside Canadian cloud regions with Canadian-resident operational support.

Discuss your deployment requirements

CHOOSE YOUR PLATFORM

Choose the Right Platform for Your Enterprise AI

Enterprise AI does not require every organization to use the same platform. Nala Networks can implement cloud-native or flexible AI architectures depending on your existing infrastructure, data requirements and deployment preferences.

DF
Dify

Dify Implementation

Build private knowledge assistants, RAG applications and AI workflows with a flexible AI application platform that can connect to multiple model providers.

Explore Dify Implementation
AWS
AWS Bedrock

AWS Bedrock

Build enterprise generative AI, Knowledge Bases, RAG and AI agents directly within your AWS environment using Amazon Bedrock.

Explore AWS Bedrock Consulting
VA
Google Vertex AI

Google Vertex AI

Build Gemini-powered enterprise AI, RAG and search applications using Google Cloud and Vertex AI.

Explore Google Vertex AI Consulting

Flexible Architecture Across Private, Google Cloud, and AWS

We work across private infrastructure, Google Cloud, and AWS. That means we are not limited to one AI platform. Architecture is determined by your security requirements, existing infrastructure, and operational preferences — not by what we prefer to sell.

Private AI — RAG, Search & Models

Dify
Hugging Face
Qdrant
PostgreSQL
Mistral AI

Private AI — Infrastructure

Docker
NGINX
Ubuntu
Redis
MinIO

Google Cloud AI

Gemini
Google Cloud
BigQuery

AWS AI

Amazon Bedrock
AWS
OpenSearch

Interface, Identity & Data Sources

Next.js
React
TypeScript
Node.js
Entra ID

Data Sources

SharePoint
Microsoft Graph

Representative component inventory. Final stack is assessed per engagement. Private AI components are openly licensed and independently replaceable. Google Cloud and AWS implementations use managed services within your own accounts.

How We Implement It

Each engagement follows a structured delivery sequence. Architecture is designed before tooling is selected. A proof of concept validates the approach before production commitment. The sequence ends with your team able to operate the system independently.

Delivery Sequence — Eight Phases

  1. 01

    Discovery

    Identify the business problem, users, data sources, security requirements, and expected outcomes. We also determine whether Private AI, Google Cloud, AWS, or a hybrid architecture is the best fit.

  2. 02

    Architecture

    Design the system before committing to models or tools. This includes: Data Sources, Processing, Retrieval, Models, Agents, Security, and User Experience. Architecture is reviewed before infrastructure is provisioned.

  3. 03

    Proof of Concept

    Test the approach using representative documents and real questions from your organization. The objective is to verify the system can retrieve your information accurately enough to solve the intended business problem.

  4. 04

    Data & RAG Pipeline

    Connect approved data sources and configure parsing, chunking, metadata, embeddings, search, reranking, permissions, and retrieval logic.

  5. 05

    AI & Agent Development

    Configure the selected models, prompts, RAG workflows, tools, and agents for the approved deployment environment.

  6. 06

    Testing

    Create an evaluation set based on real business questions and measure the system against agreed acceptance criteria.

  7. 07

    Production Deployment

    Deploy the solution into the approved environment with identity, security, monitoring, and operational controls in place.

  8. 08

    Handover & Support

    Your team receives documentation and training. Ongoing support is available for monitoring, maintenance, model evaluation, and incremental improvements.

Start with a discovery conversation

How We Work

Three principles that apply to every enterprise AI engagement with Nala Networks.

  1. We start with the business problem, not the model

    We identify what problem you need to solve, where information lives, who can access it, and where the system must run. The answers determine the architecture — not the other way around.

  2. Architecture before tooling

    We design the system before committing to models or infrastructure. Private AI, Google Cloud, and AWS each have different trade-offs. We evaluate which fits your security, accuracy, integration, and operational requirements.

  3. Tested against real questions before production

    The proof of concept verifies the system can retrieve your information accurately enough to solve the intended business problem — not simply demonstrate that an LLM can produce convincing text.

Have an AI Project?

Your Data. Your Cloud. Your AI Architecture.

Private infrastructure, Google Cloud or AWS. Nala Networks helps you turn organizational knowledge into secure enterprise AI systems your team can actually use.

Book an AI Discovery Call