PRIVATE RAG • ENTERPRISE LLM • SECURE AI SEARCH
Private AI. Your Infrastructure. Nothing Leaves Your Network.
A private search, question-answering, and knowledge-assistance platform for enterprise organizations. Retrieval-augmented generation that runs entirely on infrastructure you own no document, query, or model call ever reaches a third-party API.
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
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.
Use cases are identified during Discovery. The following categories represent confirmed scenarios from organizations that have identified private AI knowledge assistance as a priority.
Engineering & Technical Research
Locate specifications, prior decisions, and architectural notes across wikis, runbooks, and version-controlled documentation.
Contracts & Project Documentation
Search across executed agreements and statements of work. Retrieve specific clauses and obligations with source documents referenced.
Customer Service & Account Knowledge
Enable service teams to query account history and product notes scoped to specific client records.
Operations & Standard Procedures
Answer operational questions from the most current version of your procedures, with citations that make updates auditable.
Quality & Compliance
Retrieve the specific control or requirement relevant to an audit question. Answers include document source for evidence packages.
Internal Policies & Employee Support
HR and IT policies answered from the current handbook. Employees get accurate answers; policy owners see which questions are asked most.
Every deployment keeps organizational data inside the chosen environment. The model and all supporting services run there no data is sent to a third-party AI provider. The deployment model is selected during Discovery based on existing infrastructure, regulatory requirements, and operational preference.
Option 01
Option 02
Option 03
Option 04
Canadian company, Canadian delivery. Nala Networks is Toronto-based. For organizations with Canadian data residency requirements including organizations subject to PIPEDA, provincial privacy legislation, or public-sector data governance obligations we offer deployment inside Canadian cloud regions with Canadian-resident operational support.
Every component in the platform is openly licensed. No proprietary runtime is required, and each layer can be replaced independently without rebuilding the platform. When better components emerge a faster vector database, a more capable embedding model you can upgrade without starting over.
Interface & Identity
Next.js
React
TypeScript
Node.js
Entra IDAI, Search & Retrieval
Dify
Hugging Face
Qdrant
PostgreSQL
Mistral AIInfrastructure & Operations
Docker
NGINX
Ubuntu
Redis
MinIODocument Sources
SharePoint
Microsoft GraphRepresentative component inventory. Final stack is assessed per engagement. All components are openly licensed and independently replaceable. No proprietary runtime is required at any layer.
Each engagement follows a structured delivery sequence. Phases are not collapsed: architecture review precedes infrastructure provisioning; testing against real use-case questions precedes production launch. The sequence produces a system your team can operate independently after handover.
Delivery Sequence — Six Phases
Identify the business problem, intended users, document sources, security restrictions, integrations, and expected outcomes. Collect real questions employees currently answer by searching manually these become Phase 5 test cases.
Evaluate deployment model options, select embedding and generation models appropriate to your content types and language mix, and produce architecture documentation for review before infrastructure is provisioned.
Build a working instance against a constrained document set. This is a real deployed system operating against your infrastructure and data not a demonstration against synthetic content.
Connect document sources, process documents into the vector index, and configure access rules. This phase expands to the full intended document scope.
Test against real questions from Phase 1. Measure retrieval accuracy, answer quality, citation accuracy, and latency. Iterate on chunking, retrieval parameters, and prompt engineering until results meet acceptance criteria established at Discovery.
Move to production. Document every component, every configuration parameter, and every operational procedure. Deliver training to your team. The engagement ends with your team able to operate, extend, and modify the system independently.
Three commitments apply to every private AI engagement with Nala Networks.
Every pilot runs against your actual infrastructure and a real subset of your documents. If the system cannot answer your questions in the pilot, we know before production. We do not demonstrate against synthetic data and consider that a success.
We select openly licensed components that can be operated, reviewed, and replaced without rebuilding the platform. You are not locked into our continued involvement or any single vendor relationship.
Each engagement has a fixed scope: defined use cases, defined document sources, defined acceptance criteria, and a defined handover point. We do not run open-ended engagements that continue indefinitely.
Let's Connect /
Ready to see what private RAG and a private LLM can do with your data?
Private RAG on a private LLM for sensitive technical documents. Your data, your infrastructure, cited answers
