Find Business Information Using Natural Language

Your business already has the information. The problem is finding it.

Oracast builds AI-powered search that lets employees and customers ask questions naturally across applications, databases, documents, websites and business knowledge.

Instead of searching through screens, folders and filters, users can simply ask what they need to know.

Talk to Us About AI Search

What Is AI Search?

AI Search helps people find information by meaning, context and natural-language questions, rather than relying only on exact keyword matches.

Business records

Application records, databases, customer information and project records.

Documents & knowledge

PDFs, manuals, policies, procedures and knowledge bases.

Published information

Websites, reports and product information.

“Which projects had similar problems last year?”

AI Search can retrieve relevant information, summarize what it finds and help users understand the answer.

Your Business Has Information Everywhere

Most businesses do not have an information problem. They have a finding information problem.

Important information may be scattered across:

Applications

Customer records, jobs, projects, transactions and operational data.

Documents

PDFs, manuals, policies, procedures, contracts and reports.

Websites

Product information, support content and company knowledge.

Databases

Structured operational and historical information.

Knowledge Bases

Internal documentation, support articles and procedures.

Third-Party Systems

CRM, ERP, accounting and industry platforms.

Employees may know the information exists but still spend valuable time figuring out:

  • Where is it?
  • What is it called?
  • Which system contains it?
  • Which filter should I use?
  • Which document should I open?

AI Search can provide a more natural way to access that information.

Traditional Search vs. AI Search

Traditional Search

Search for exact or closely related words.

“invoice rejection error”

Get documents, records and pages containing those terms. Then review the matches to find the answer.

AI Search

Ask a question in natural language.

“Why was this customer's last invoice rejected?”

Retrieve the customer, invoice, submission status, error details and relevant documentation. Summarize the likely reason with supporting sources.

Traditional search finds matching information. AI Search helps users find relevant information and understand it.

Ask Questions the Way You Normally Would

Users should not need to know the database structure or exact terminology. Instead of working through customer screens, job history, date and status filters, exports and reviews, they can ask:

Job history
“Show me incomplete jobs for this customer from the last six months.”
Customer trends
“Which customers reported this problem most often?”
Company policy
“What does our policy say about damaged equipment?”
Project experience
“Find projects similar to this one.”
Accounting status
“Which jobs from yesterday still haven't been exported to accounting?”

Center the search experience around the user's question, not the structure of the software.

Search Across the Information Your Business Already Has

Application Data

Search customers, jobs, projects, orders, inventory, service records and other application information.

Documents

Search PDFs, policies, manuals, contracts, reports and technical documentation.

Databases

Retrieve relevant structured information from application databases.

Knowledge Bases

Search company procedures, support information, internal documentation and FAQs.

Websites

Make website content easier for customers or employees to explore using natural language.

Connected Systems

Search information across CRM, ERP, accounting and other business platforms when appropriate integrations are available.

Make Your Existing Application Easier to Search

Custom applications hold years of valuable information. AI Search can add a natural-language interface without requiring users to navigate screens, filters, reports, exports, dashboards, database fields or archived records.

“Which jobs took more than 20% longer than estimated during the last three months?”

  1. Interpret the request

    Identify the dates, jobs and comparison the operations manager needs.

  2. Retrieve approved data

    Let the application control data retrieval and permissions.

  3. Explain the result

    Present the relevant records and explain the findings.

Explore AI Integration

Turn Your Documents Into Searchable Business Knowledge

Turn information across multiple documents into searchable business knowledge, rather than simply locating pages that contain a phrase.

Policies & procedures

Company policies, procedures, manuals and training materials.

Technical knowledge

Technical documents, product documentation and specifications.

Business documents

Contracts, reports, PDFs and internal documentation.

Ask naturally

“What is our procedure when a customer reports damaged equipment?”

Answer from approved sources

Retrieve relevant sections from company documents, then provide a concise answer with references to the source material.

Give Employees Faster Access to Company Knowledge

Employees often ask the same questions repeatedly:

  • How does this process work?
  • Where is this policy?
  • What should I do in this situation?
  • Which form do I need?
  • How does this product work?
  • What did we do on a similar project?
  • Where is the documentation for this system?
Business planning, analysis and application development

AI Search can provide a conversational interface to approved company knowledge.

This can reduce time spent:

  • searching folders
  • browsing documentation
  • asking coworkers
  • reviewing long PDFs
  • navigating internal websites

The objective is to make organizational knowledge easier to access without losing the connection to the original source.

How Does AI Search Work?

“What were the most common reasons jobs were delayed last quarter?”

  1. Understand

    Interpret what the user is asking.

  2. Retrieve

    Search approved application data, documents and connected systems.

  3. Evaluate

    Identify the context most relevant to the question.

  4. Answer & cite

    Explain the findings and link to supporting records or documents where appropriate.

For business search, responses should be grounded in the organization's authorized information, not just the model's general knowledge.

Ground AI Answers in Your Business Information

Retrieval-Augmented Generation, or RAG, supplies relevant business information as context before AI generates an answer. The question guides retrieval, the retrieved information grounds the response, and supporting sources can be displayed.

Find relevant information
Embeddings, vector search, keyword search or hybrid search.
Select the right context
Metadata filtering, reranking and document retrieval.
Connect structured data
Controlled database queries and APIs.
Present the response
Structured outputs and supporting sources.

Choose the retrieval technology for the information being searched and the business problem being solved.

Search by Meaning, Not Just Matching Words

Keyword search works well when users know the terminology. Semantic search can connect related concepts even when the exact words differ.

The document says

“vehicle maintenance requirements”

The user asks

“What servicing do our trucks require?”

This can be valuable for technical documentation, policies, knowledge bases, product catalogs, support content, project histories and large document collections.

Ask Questions About Your Application Data

Give users a natural-language way to search and analyze structured business data.

Job performance
“How many jobs were completed late last month?”
Customer revenue
“Which customers generated the most revenue this year?”
Overtime
“Which drivers had the most overtime last quarter?”
Project costs
“Show me projects with unusually high material costs.”

The application determines how information is retrieved and enforces business rules and permissions. AI helps translate the user's intent and explain the results.

Natural-language access does not mean unrestricted database access.

Go Beyond Finding Information

Users often need more than a record. They need to understand what the information means.

Understand change

Ask what changed or why something happened.

Recognize patterns

Find common patterns or summarize what happened with a customer.

Focus attention

Identify items that need attention or what is unusual about a project.

  1. Search

    Interpret the question.

  2. Retrieve

    Find relevant information.

  3. Analyze

    Identify what matters.

  4. Explain

    Present the findings clearly.

Advanced analysis may also require application logic, business rules or dedicated analytics, rather than relying solely on an AI model.

Show Users Where the Answer Came From

Users should be able to understand the basis for an AI-generated answer and review the supporting information.

Answer

Employees must submit the completed inspection report before equipment can be returned to service.

Supporting sources

  • Equipment Safety PolicySection 4.2
  • Inspection ProceduresPage 18

Where appropriate, results can include citations, document and application record links, customer or project references, source excerpts, dates, document names and relevant metadata.

AI can provide the answer. The source provides confidence and context.

Search Should Respect Existing Permissions

Finding information should never override a user's right to access it. AI Search should work within the application's existing authorization model.

Identify the user

Apply authentication, roles and permissions.

Limit the scope

Respect departments, customer access and record ownership.

Protect the source

Enforce document restrictions and application security rules.

A manager might search

All department records, if their permissions allow it.

An employee might search

Only the records assigned to them.

If a user cannot access the information normally, AI should not reveal it through search.

Help Customers Find Answers Faster

Let customers describe what they need instead of browsing categories or guessing keywords.

“I need a replacement part for equipment installed before 2022.”

Find a product

Product search and documentation that help identify relevant parts.

Resolve a problem

Support information, troubleshooting, knowledge bases and FAQs.

Use a customer portal

Authorized account information and service details.

The results depend on the available business data and the information the customer is permitted to access.

Give Your Team One Place to Ask Business Questions

A common search interface can bring together information that currently lives in multiple systems.

“What do we know about this customer?”

Customer context

Customer profile, account notes and current status.

Work & service history

Recent projects, service history and outstanding issues.

Supporting information

Relevant documents and recent communications.

Retrieve authorized information across the sources, without asking employees to search each one independently.

This can become the foundation for a broader AI business assistant.

AI Search Can Become the Foundation for an AI Assistant

Search can be a practical first step toward more advanced AI capabilities. Start with useful information access, then expand when the business is ready.

  1. Search & answer

    Find relevant information and explain it naturally.

  2. Analyze & recommend

    Identify patterns, summarize what matters and suggest possible next steps.

  3. Prepare

    Draft reports, communications or actions for review.

  4. Act & automate

    Use approved application tools and coordinate multi-step workflows.

A business does not need to begin with an autonomous AI agent. Search can create the information and integration foundation for later capabilities.

Search Across Multiple Business Systems

With the appropriate integrations, AI Search can retrieve and combine authorized information from custom applications, CRM, accounting, documents and industry platforms.

“Which customers have outstanding invoices and unresolved service issues?”

  1. Find the customer

    Retrieve authorized customer and account information.

  2. Check the accounts

    Identify outstanding invoices in the accounting system.

  3. Connect the service history

    Find unresolved issues and bring the relevant information together.

What Can AI Search Look Like?

Operations

“Which jobs are most likely to be delayed today?”

Search and analyze job status, schedules and operational information.

Customer Service

“Summarize everything that has happened with this customer during the last 90 days.”

Retrieve customer records, service history and relevant notes.

Policies & Procedures

“What is our procedure when equipment fails an inspection?”

Retrieve the appropriate policy and procedure information with source references.

Projects

“Find projects similar to this one and summarize the issues they encountered.”

Search project history, documentation and application records.

Build Search Across Your Business Knowledge

A strong search solution connects the question, retrieval method, permissions and source information. Each source needs the right retrieval strategy.

Search interface
A natural-language question captures what the user needs.
Permission layer
Authentication, authorization, data access and source restrictions control what can be searched.
AI Search layer
Query understanding, semantic or keyword search, filtering, reranking and response generation support retrieval.
Information sources
Application databases, documents, knowledge bases, websites, CRM, ERP, accounting and APIs provide context.
Search result
Present an answer, relevant records, sources and supporting documents.

A document library may need a different retrieval approach than a transactional database. The architecture should reflect those differences.

Add AI Search to the Application You Already Have

You do not necessarily need a new AI platform. Search can often be integrated into the software people already use.

Applications

Web applications, mobile applications, SaaS platforms and legacy software.

Portals

Customer portals and employee portals.

Knowledge & content

Knowledge bases and websites.

Can AI Search Be Added to an Older Application?

Often, yes. Oracast can assess what is ready for AI Search and identify targeted improvements, without assuming the application needs a rebuild.

Application foundation
Assess architecture and infrastructure; address framework upgrades or infrastructure changes where needed.
Data & connections
Review databases, APIs, search functionality, document storage and data quality; consider API development, cleanup or document organization.
Access & protection
Review authentication, permissions and security; improve access controls where required.

Build the foundation required for reliable AI Search, without rebuilding unnecessarily.

Keep Business Search Secure

Security belongs in the architecture from the beginning, especially when search can access valuable or sensitive business information.

Authentication
Verify who is performing the search.
Authorization
Determine which information the user can access.
Source Restrictions
Control which documents and systems can be searched.
Data Isolation
Keep information appropriately separated between customers, departments or users.
Query Controls
Restrict access to sensitive data where required.
Logging
Record important search and system activity.
Secure Integrations
Protect credentials, APIs and database connections.
AI Data Controls
Consider how information is transmitted to AI providers and how AI services handle that information.

Better search should never mean weaker access control.

Good AI Search Requires More Than Connecting an AI Model

A useful search experience depends on the entire retrieval system, not just the model generating the answer.

Prepare good sources

Document and data quality, chunking strategy, metadata, source quality and permissions.

Retrieve the right context

Search indexes, embeddings, keyword search, semantic and hybrid retrieval, filtering, reranking and context selection.

Generate a useful response

Response generation based on relevant, authorized information.

If AI retrieves the wrong information, it can confidently generate the wrong answer.

Test Whether AI Search Actually Finds the Right Information

Evaluate AI Search against representative questions that employees or customers actually ask.

Accuracy & relevance

Retrieval accuracy, answer relevance, source accuracy and response quality.

Difficult questions

Missing information, ambiguous questions, conflicting documents, outdated information and hallucination risk.

Safe access

Permission enforcement across sources and user roles.

Practical performance

Latency and cost under realistic use.

Measure whether the system consistently retrieves the information required to answer real business questions.

AI Search Is Only as Current as Its Information

Documents, policies, customers, projects, products and application records change. Search needs a strategy for staying synchronized with the underlying sources.

  1. Capture changes

    Use scheduled indexing or event-based updates.

  2. Synchronize sources

    Connect document changes, databases and APIs.

  3. Maintain the index

    Update metadata, handle deleted documents and manage versions.

Users should be searching current business information, not an outdated copy.

Our AI Search Development Process

Discover

Understand what users need to find and why.

Identify Sources

Determine where the required information currently lives.

Assess

Review data, documents, permissions, APIs and technical constraints.

Design

Choose the appropriate search and retrieval architecture.

Connect

Integrate the required information sources.

Index

Prepare documents and information for efficient retrieval where required.

Secure

Apply authentication, permissions and source controls.

Build

Create the search interface and AI response experience.

Test

Evaluate search quality using real business questions.

Deploy

Introduce AI Search into the production application.

Monitor

Track search quality, errors, usage, performance and cost.

Improve

Refine retrieval and expand searchable information over time.

You Don't Need to Search Everything on Day One

A practical AI Search project can start with one valuable source of information.

For example:

  1. Phase 1Company Documents
  2. Phase 2Knowledge Base
  3. Phase 3Application Data
  4. Phase 4Customer Records
  5. Phase 5Third-Party Systems
  6. Phase 6AI Assistant & Actions

This allows the business to prove the value of AI Search before expanding its scope.

What Information Is Hard for Your Team to Find?

“What information do employees or customers regularly struggle to find?”

Oracast can review where information lives and how people try to find it.

Your information

Applications, databases, documents, reports, knowledge bases and websites.

Your connections

Third-party systems and existing search functionality.

Your everyday questions

User workflows and customer support questions.

Identify where AI Search could reduce information hunting and make business knowledge easier to access.

Explore an AI Search Project

Keep Your AI Search Accurate and Reliable

AI Search depends on models, APIs, embeddings, vector indexes, documents, application data, integrations, permissions, retrieval configuration, prompts and source systems that change over time.

Availability & connections

Monitor AI API availability, indexing failures, integration failures and model changes.

Search quality

Review relevance, source freshness and response errors.

Access & security

Watch for permission issues as the system evolves.

Usage & performance

Track latency, usage and cost.

Oracast's AI App Care service can help monitor and maintain AI-powered applications and search systems.

Explore AI App Care

Build AI Search With a Team That Understands the Systems Being Searched

AI Search takes an understanding of the systems being searched, not just an AI model.

Application engineering

Applications, databases, APIs, integrations and infrastructure.

Business information

Documents, business logic and search architecture.

Secure AI delivery

Authentication, permissions and AI models.

Oracast combines application development experience with AI integration capabilities to build search around real business systems.

We connect AI to the information, applications and workflows behind the search box.

What Could Your Team Find Faster With AI Search?

Think about the questions employees or customers ask every day. The answers may already exist somewhere in your applications, databases and documents. AI Search can make that information significantly easier to access.

Talk to Us About AI Search

Frequently Asked Questions About AI Search

What is AI Search?

AI Search uses artificial intelligence to help users find information based on meaning, context and natural-language questions rather than relying only on exact keyword matches.

How is AI Search different from traditional search?

Traditional search generally matches keywords, fields or predefined filters. AI Search can interpret natural-language questions and use semantic meaning to retrieve information that may not contain the exact words used in the query.

What can AI Search search?

Depending on the integration, AI Search can retrieve information from applications, databases, PDFs, policies, manuals, websites, knowledge bases, customer records and third-party systems.

Can AI Search search our internal documents?

Yes. Documents such as PDFs, policies, manuals, procedures and technical documentation can be prepared for AI-powered retrieval and search.

Can AI Search access our application database?

Yes. AI Search can be integrated with application databases using controlled queries, APIs or application services. Access should respect the application's authentication and permission model.

Can AI Search search documents and database information together?

Yes. A search system can combine structured application data with unstructured information such as documents when the architecture and permissions support it.

What is semantic search?

Semantic search attempts to retrieve information based on the meaning of a query rather than relying exclusively on matching exact words.

What is RAG?

Retrieval-Augmented Generation, commonly called RAG, is an approach where relevant information is retrieved from approved sources and provided to an AI model as context for generating a response.

What is vector search?

Vector search uses numerical representations of information, often called embeddings, to find content that is semantically similar to a user's query.

Does AI Search provide sources?

It can. Where appropriate, an AI Search solution can provide links or references to the documents, application records or other sources used to generate an answer.

Can AI Search respect our existing user permissions?

Yes. AI Search should be designed so users only retrieve information they are authorized to access.

Can AI Search be added to an existing application?

Yes. AI Search can often be integrated into an existing web application, mobile application, customer portal, employee portal or SaaS platform.

Can AI Search work with a legacy application?

Often, yes. The application may first require an assessment of its database, APIs, authentication, permissions and infrastructure. Targeted modernization may be recommended where necessary.

Can customers use AI Search on our website?

Yes. AI Search can be designed for public websites, customer portals, product information, support documentation and other customer-facing information.

Is AI Search the same as an AI chatbot?

No. A chatbot describes the conversational interface. AI Search describes how relevant information is retrieved. A conversational assistant may use AI Search behind the scenes to retrieve reliable business information before answering.

Can AI Search eventually become an AI assistant?

Yes. Search can provide the information foundation for an AI assistant that later analyzes information, makes recommendations and uses approved tools to perform actions.

How much does AI Search cost?

Cost depends on the number and type of information sources, volume of documents and data, required integrations, permissions, search complexity, user interface and ongoing AI usage.

How long does it take to implement AI Search?

The timeline depends on the number of information sources, data quality, document volume, application architecture, integrations and security requirements. A focused search project using one information source will generally require less effort than organization-wide search across multiple systems.

Does AI Search require ongoing maintenance?

Yes. Documents, data, APIs, AI models and source systems change over time. Search indexes, integrations, permissions and retrieval quality should be monitored and maintained.

Stop Searching Through Systems. Start Asking Questions.

Your business already contains valuable information.

AI Search can make it easier for employees and customers to find, understand and use it.

Talk to Us About AI Search