Solution

AI Assistant for Business | Enterprise RAG

Corporate knowledge is often scattered across intranets, fragmented documents, and legacy systems. Finding the right answer can require manual search or interrupting colleagues. ISZ.AI develops an AI assistant for business: a secure search and conversational interface that retrieves verified answers from proprietary data with source citations.

The Enterprise Knowledge Problem

Every minute an employee spends hunting for a policy document or interrupting a colleague is billable time lost to friction. Our internal AI assistant acts as a unified knowledge base chatbot, connecting to authorized systems and returning answers with cited sources.

Intended Users: Employees, support agents, legal teams, HR, and technical staff who need immediate access to complex documentation.
Knowledge Sources: Connecting to SharePoint, Confluence, Google Drive, Jira, PDFs, and proprietary databases.

Keyword Search vs. a RAG-Grounded Assistant

Enterprise search tools have existed for years, but keyword matching and a conversational assistant grounded in retrieval solve fundamentally different problems:

Capability	Traditional Keyword Search	ISZ.AI Enterprise RAG Assistant
Query type	Exact keyword or filename match	Natural language questions
Result format	List of documents to read yourself	Direct answer with cited source
Cross-document synthesis	None — one document at a time	Combines relevant passages across multiple documents
Access control	Often flattened across the index	Permission-aware, per-user results
Handling “I don’t know”	Returns irrelevant results anyway	Explicitly declines when the answer isn’t in your data
Multilingual queries	Requires matching source language	Query and knowledge base language can differ

Technology: Enterprise RAG Architecture

We build enterprise RAG solutions (Retrieval-Augmented Generation) that ground answers in approved documents while respecting access controls and data-isolation requirements.

Data Ingestion & Indexing: Vectorizing vast amounts of unstructured text into a highly searchable database.
Retrieval Architecture: Semantic search algorithms that find the exact passage needed to answer the query.
Source Citations: Every answer includes a direct link to the source document, ensuring auditability and trust.
Hallucination Management: Constraining the model to only answer using the retrieved context. If the answer isn’t in your data, the assistant admits it doesn’t know.
Answer Evaluation: Automated metrics to measure retrieval accuracy and response quality.
Model Options: Support for commercial foundation models or secure deployment of open-weight models within your VPC. See our RAG vs. fine-tuning comparison if you’re weighing which grounding approach fits your data.

Security, Access, and Governance

A true custom AI assistant respects your existing security boundaries.

Permission Inheritance & Role-Based Access: The assistant only searches documents the specific user has permission to view.
Data Isolation: Your data is never used to train public models.
Audit Logs: Comprehensive logging of user queries and retrieved documents for compliance.
Governance: Controls over what data is indexed, who can access it, and how answers are reviewed.

Deployment and Integrations

Adoption fails when the assistant lives somewhere employees don’t already work. We deploy into the tools your teams use every day:

Multilingual Capabilities: Support for English, Japanese, Chinese, and more — including cases where the knowledge base is written in one language and employees query in another.
Integrations: Deploy into Slack, Microsoft Teams, enterprise portals, or your custom intranet.

Project Considerations

Project Stages: Discovery, data auditing, ingestion pipeline setup, RAG tuning, and deployment.
Timeline Factors: Typical deployments range from 6 to 12 weeks, depending on the complexity of data sources and permission syncing.
Cost Factors: Implementation costs are driven by data volume, integration complexity, and the chosen LLM inference costs.

Read how we deployed a Multilingual AI Assistant for a Global Manufacturer to unify technical documentation across global factories — the deployment cut support workload by 60% by letting frontline staff query approved technical documentation directly instead of routing questions to headquarters. The same retrieval architecture also underpins our AI chatbot for customer service, for teams that need this capability facing customers rather than employees.

For a knowledge platform spanning sales and support rather than internal staff alone, see how an AI Knowledge Platform for an Industrial Manufacturer unified product search, pre-sales configuration, and after-sales fault diagnosis on one knowledge base, cutting configuration lookup time from hours to minutes.

Frequently Asked Questions

How long does it take to deploy an internal AI assistant? Typical deployments range from 6 to 12 weeks. The main variable is how many systems need to be connected and how permission structures are set up across them — a single well-organized SharePoint site moves faster than a mix of legacy databases with inconsistent access rules.

How much work does our IT team need to put in? IT’s main role is granting read access to the source systems and confirming the permission model; ISZ.AI’s team builds the ingestion pipeline, retrieval architecture, and integrations. Data auditing early in the project surfaces any access-control cleanup that needs to happen on your side before go-live.

Can the assistant give employees wrong or outdated information? It is constrained to answer only from the documents it retrieves, and it explicitly says it doesn’t know when the answer isn’t in your indexed data, rather than guessing. Source citations on every answer let employees verify the underlying document themselves, and re-indexing keeps the knowledge current as source documents change.

How is our proprietary data protected? Your data is never used to train public models, access is permission-aware at the individual document level, and every query and retrieval is logged for audit purposes. Deployment can use commercial foundation models or open-weight models hosted inside your own VPC, depending on your data-residency requirements.

What does a pilot look like before we roll this out company-wide? A pilot typically connects one or two knowledge sources — often the ones causing the most repeated questions — for a single department, with retrieval accuracy and answer quality measured against real employee queries before expanding to additional systems, languages, or teams.

Elevate Your Enterprise Knowledge

Contact ISZ.AI to discuss an AI assistant for business, including document sources, permission inheritance, RAG evaluation, and deployment channels.