AIAppliance

Wissly-in-a-Box — the enterprise AI appliance you run right inside your corporate network

Wissly-in-a-Box is an on-premise AI workspace that runs a local LLM, embeddings, and a vector store entirely within your network. Connect your internal documents without a long SI build, and get answers with source verification in a single flow.

AIAppliance

Wissly-in-a-Box — the enterprise AI appliance you run right inside your corporate network

AIAppliance

Wissly-in-a-Box — the enterprise AI appliance you run right inside your corporate network

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Day 1

Start validating AI on your internal documents soon after install

Local Stack

Run the LLM, embeddings, and vector store on your internal network

Incremental

Re-index only changed files to stay current

#AIAppliance

#OnNetworkAI

#OnPremise

#DocumentRAG

#SourceBackedAnswers

#Day1AI

#LocalLLM

#IncrementalIndexing

Why Appliance

Start AI adoption as a product, not a project.

Wissly-in-a-Box isn't just a server — it's a complete RAG stack that runs inside your corporate network. It connects even the documents you can't upload — on local PCs, NAS, and internal servers — turning them into working knowledge.

Typical SI build

~3-month build lead time

Requires project management and development resources across requirements, development, integration, and QA.

Typical SI build

~3-month build lead time

Requires project management and development resources across requirements, development, integration, and QA.

Typical SI build

~3-month build lead time

Requires project management and development resources across requirements, development, integration, and QA.

Typical cloud AI

The burden of uploading sensitive documents

A constraint for organizations that can't send contracts, research, technical documents, or customer data to external services.

Typical cloud AI

The burden of uploading sensitive documents

A constraint for organizations that can't send contracts, research, technical documents, or customer data to external services.

Typical cloud AI

The burden of uploading sensitive documents

A constraint for organizations that can't send contracts, research, technical documents, or customer data to external services.

Wissly in a Box

Validate from day one

Ships a local LLM, embeddings, vector store, and indexing pipeline as one package, so you can quickly start using AI on your internal documents.

Wissly in a Box

Validate from day one

Ships a local LLM, embeddings, vector store, and indexing pipeline as one package, so you can quickly start using AI on your internal documents.

Wissly in a Box

Validate from day one

Ships a local LLM, embeddings, vector store, and indexing pipeline as one package, so you can quickly start using AI on your internal documents.

Key Points

What makes Wissly-in-a-Box different

From fast adoption to network security, local data connectivity, a complete RAG stack, incremental indexing, and source verification — see the six points that make Wissly-in-a-Box different where the work actually happens.

POINT 01

Day 1 adoption

No long build process like an SI project — start validating AI on your internal documents right after install.

POINT 01

Day 1 adoption

No long build process like an SI project — start validating AI on your internal documents right after install.

POINT 01

Day 1 adoption

No long build process like an SI project — start validating AI on your internal documents right after install.

POINT 02

Corporate-network security

Puts a network-first setup front and center for organizations that can't send sensitive business documents to an external cloud.

POINT 02

Corporate-network security

Puts a network-first setup front and center for organizations that can't send sensitive business documents to an external cloud.

POINT 02

Corporate-network security

Puts a network-first setup front and center for organizations that can't send sensitive business documents to an external cloud.

POINT 03

Direct local data connection

Connects even the data that cloud-only tools can't reach — local PCs, NAS, internal servers, and on-premise folders.

POINT 03

Direct local data connection

Connects even the data that cloud-only tools can't reach — local PCs, NAS, internal servers, and on-premise folders.

POINT 03

Direct local data connection

Connects even the data that cloud-only tools can't reach — local PCs, NAS, internal servers, and on-premise folders.

POINT 04

A complete RAG stack

A local LLM, embedding model, vector store, and document pipeline all work together inside your network.

POINT 04

A complete RAG stack

A local LLM, embedding model, vector store, and document pipeline all work together inside your network.

POINT 04

A complete RAG stack

A local LLM, embedding model, vector store, and document pipeline all work together inside your network.

POINT 05

Large-scale, incremental indexing

Instead of reprocessing every document each time, it re-indexes only changed files to keep large-scale internal knowledge continuously up to date.

POINT 05

Large-scale, incremental indexing

Instead of reprocessing every document each time, it re-indexes only changed files to keep large-scale internal knowledge continuously up to date.

POINT 05

Large-scale, incremental indexing

Instead of reprocessing every document each time, it re-indexes only changed files to keep large-scale internal knowledge continuously up to date.

POINT 06

Source verification

Not an AI that only gives answers — a work-grade AI experience where you can verify which document and page each answer came from.

POINT 06

Source verification

Not an AI that only gives answers — a work-grade AI experience where you can verify which document and page each answer came from.

POINT 06

Source verification

Not an AI that only gives answers — a work-grade AI experience where you can verify which document and page each answer came from.

Inside the Box

A RAG stack that runs entirely within your network

The heart of Wissly-in-a-Box isn't the hardware itself — it's an architecture where AI search and agent workflows are completed entirely within your network.

Local LLM

Answer generation on the internal network

Puts front and center a setup that generates document-based answers within your network, without relying on external cloud model calls.

Local LLM

Answer generation on the internal network

Puts front and center a setup that generates document-based answers within your network, without relying on external cloud model calls.

Local LLM

Answer generation on the internal network

Puts front and center a setup that generates document-based answers within your network, without relying on external cloud model calls.

Embedding

Turn internal documents into searchable vectors

Embeds document content so you can quickly find evidence relevant to a question across large-scale internal knowledge.

Embedding

Turn internal documents into searchable vectors

Embeds document content so you can quickly find evidence relevant to a question across large-scale internal knowledge.

Embedding

Turn internal documents into searchable vectors

Embeds document content so you can quickly find evidence relevant to a question across large-scale internal knowledge.

Vector Store

An internal store for evidence retrieval

The vector store operates within your control, keeping search and answer evidence managed on internal infrastructure.

Vector Store

An internal store for evidence retrieval

The vector store operates within your control, keeping search and answer evidence managed on internal infrastructure.

Vector Store

An internal store for evidence retrieval

The vector store operates within your control, keeping search and answer evidence managed on internal infrastructure.

Parser

Handle diverse enterprise documents

Parses business documents such as PDF, DOCX, XLSX, PPTX, and HWP while preserving document- and page-level evidence.

Parser

Handle diverse enterprise documents

Parses business documents such as PDF, DOCX, XLSX, PPTX, and HWP while preserving document- and page-level evidence.

Parser

Handle diverse enterprise documents

Parses business documents such as PDF, DOCX, XLSX, PPTX, and HWP while preserving document- and page-level evidence.

Incremental Indexer

Reprocess only changed files

As documents grow, it processes changes rather than rebuilding the entire corpus — designed for 1TB+ scale operation.

Incremental Indexer

Reprocess only changed files

As documents grow, it processes changes rather than rebuilding the entire corpus — designed for 1TB+ scale operation.

Incremental Indexer

Reprocess only changed files

As documents grow, it processes changes rather than rebuilding the entire corpus — designed for 1TB+ scale operation.

Admin / API

Built for operations and integration

Includes an operations layer to manage document connections, indexing status, user permissions, and internal system integration.

Admin / API

Built for operations and integration

Includes an operations layer to manage document connections, indexing status, user permissions, and internal system integration.

Admin / API

Built for operations and integration

Includes an operations layer to manage document connections, indexing status, user permissions, and internal system integration.

Workflow

How internal documents become answerable knowledge assets

Not just a chatbot intro — it shows the process of connecting internal documents and processing only changes to keep them searchable.

01 Connect

Connect local PCs, NAS, internal servers, and on-premise folders to fit your environment.

02 Change detection

A desktop agent detects file changes and identifies documents that need reprocessing.

03 Incremental indexing

Converts, parses, and indexes only changed files — not the whole corpus — to stay current.

04 Query

Users ask in natural language, and the AI answers based on the connected internal documents.

05 Verify

Check the original sources behind an answer so you can use it for business decisions.

Security

Sensitive documents stay on your network; AI stays close to where the work happens.

Security-conscious organizations look first at data location, access control, operational logs, and network policy — not external uploads. Wissly-in-a-Box's network-centric operating model supports both sensitive-document use and the shift to AI-driven work.

Enterprise Network Boundary

On-premise

Local PC / NAS / Internal server

Contracts, technical docs, research, and customer-support documents that are hard to upload to the cloud

Users

Business users, administrators, and IT operators

Wissly-in-a-Box

Local LLM, embeddings, vector store, document indexing, RAG Q&A

Incremental indexing pipeline

Converts, parses, and indexes only changed files to stay current

Admin tools / Internal integration

Permissions, logs, system status, API, and business portal integration

Network-centric operation

Built around a setup where the local LLM, embeddings, and vector store all run inside your network.

Permissions and logs

The scope of user access, admin operations, and audit logs is finalized to match the actual supported spec.

Network separation & air-gap review

Air-gapped networks, offline setups, and update policies are confirmed per deployment environment.

Direct local data connection

Connects internal knowledge that cloud-only tools can't reach, such as local PCs, NAS, and internal servers.

Features

Key features

The product's capabilities, organized into scannable units for buyers. The key point: the RAG stack is self-contained within your network, and local data stays continuously indexed and current.

LOCAL LLM

Answer generation on the internal network

Supports a setup where a local LLM generates document-based answers within your network.

LOCAL LLM

Answer generation on the internal network

Supports a setup where a local LLM generates document-based answers within your network.

LOCAL DATA

Direct local data connection

Turns documents on local PCs, NAS, internal servers, and on-premise folders into searchable knowledge assets.

LOCAL DATA

Direct local data connection

Turns documents on local PCs, NAS, internal servers, and on-premise folders into searchable knowledge assets.

INCREMENTAL

Re-index only changes

Indexes only changed files instead of reprocessing every document, reducing the load of large-scale operation.

INCREMENTAL

Re-index only changes

Indexes only changed files instead of reprocessing every document, reducing the load of large-scale operation.

SOURCE

Source-based verification

Provides a flow to check the file, document location, and original text behind an answer.

SOURCE

Source-based verification

Provides a flow to check the file, document location, and original text behind an answer.

ADMIN

Web-based admin tools

Lets operators monitor document connections, indexing status, user permissions, and system health.

ADMIN

Web-based admin tools

Lets operators monitor document connections, indexing status, user permissions, and system health.

SCALE

Built for 1TB+ scale

We tailor the hardware configuration to your GPU, memory, storage, user count, and document volume.

SCALE

Built for 1TB+ scale

We tailor the hardware configuration to your GPU, memory, storage, user count, and document volume.

Connecting internal documents or folders

Indexing progress and completion status

Document-based answers generated from a question

Clicking an answer's source to view the original

Hardware Information

Hardware specification table

Organized around the finalized hardware configuration so buyers can compare at a glance. See the appliance's performance and operating scope in one table.

CPU

Xeon 24C / 48T

RAM

512 GB

GPU

RTX PRO 6000 × 2

Storage

4.5 TB

Product type

On-premise AI appliance

Operating environment

Corporate network / On-premise environment

CPU

Intel(R) Xeon(R) Platinum 8559C (24C / 48T)

GPU

NVIDIA RTX PRO 6000 Blackwell Server Edition × 2

RAM

512 GB RAM

Storage

EBS 1TB · NVMe Instance Store 3.5TB

Network

eth0 10GbE

Key features

Document search, RAG Q&A, source verification, admin tools, incremental indexing

Built-in AI stack

Local LLM, embedding model, vector store

Connected data locations

Local PC, NAS, internal server, on-premise folder

Indexing method

Incremental indexing focused on changed files

Use Cases

Start with the business documents where security matters most.

Quickly begin search, summarization, Q&A, and source verification for internal documents and security-sensitive work.

Legal / Contracts

Search contracts, terms, and side agreements, and verify the basis for each clause.

Legal / Contracts

Search contracts, terms, and side agreements, and verify the basis for each clause.

Manufacturing / Quality

Quickly find specs and issues across test reports, quality documents, and manuals.

Manufacturing / Quality

Quickly find specs and issues across test reports, quality documents, and manuals.

Research / R&D

Connect papers, lab notes, and technical materials to cut time spent on repeated searches and summaries.

Research / R&D

Connect papers, lab notes, and technical materials to cut time spent on repeated searches and summaries.

Customer support

Draft responses based on manuals, FAQs, and incident reports.

Customer support

Draft responses based on manuals, FAQs, and incident reports.

Corporate / Admin support

Turn internal policies, meeting minutes, and reports into searchable knowledge assets.

Corporate / Admin support

Turn internal policies, meeting minutes, and reports into searchable knowledge assets.

Deployment

Deployment process

After reviewing your network environment and document storage structure, we proceed step by step through hardware configuration and initial document connection.

Environment consultation

We review your network, security policy, document storage locations, and user scale.

Environment consultation

We review your network, security policy, document storage locations, and user scale.

Spec finalization

We set the hardware configuration based on GPU, memory, storage, and recommended document volume.

Spec finalization

We set the hardware configuration based on GPU, memory, storage, and recommended document volume.

Installation & setup

We handle device installation, basic network setup, and admin account configuration.

Installation & setup

We handle device installation, basic network setup, and admin account configuration.

Document connection

After the initial document connection and indexing, business users validate with real questions.

Document connection

After the initial document connection and indexing, business users validate with real questions.