Give people faster access to the information your business already knows

The problem this solution solves

• Employees spend too long searching SharePoint, PDFs, SOPs, policies, or internal documentation
• Customer-support teams answer the same questions repeatedly
• Knowledge is spread across files, websites, databases, and business systems
• Staff rely on subject-matter experts for routine information
• Generic AI tools do not know the organisation’s approved content
• Existing chatbots give shallow scripted answers
• Users need fast answers but access permissions still matter
• Teams want AI but do not yet have a controlled production approach

What Datazeb can deliver

How a production assistant works

1. User Question
2. Permissions
3. Retrieve Evidence
4. Generate Answer
5. Escalate / Act

The assistant should retrieve only approved evidence, provide an answer grounded in that evidence, and stop or escalate when confidence, permissions, or workflow rules require it.

Internal knowledge assistants

Reduce the time employees spend searching for answers

• Policies and procedures
• Standard operating procedures
• Product documentation
• Technical manuals
• Internal FAQs
• HR or onboarding information
• Sales enablement knowledge
• Project or service-delivery documentation

The strongest use cases involve information that already exists but is difficult to search consistently.

Customer-support chatbots

Automate common questions without pretending every conversation can be automated

• Product and service questions
• Policy and process guidance
• Order / service information where integration permits
• Troubleshooting steps
• Frequently asked questions
• Pre-sales guidance using approved information
• Escalation to human support

RAG and grounded answers

Connect the model to evidence instead of relying on memory

Retrieval-augmented generation can improve relevance by searching approved sources at question time and providing the model with evidence to answer from.

• Document chunking and indexing
• Metadata and source filtering
• Semantic retrieval
• Hybrid retrieval where useful
• Source citations or references in the interface
• Freshness and document-version handling

RAG is useful when knowledge changes and can be retrieved from trusted sources. It is not automatically the right answer for every AI problem.

Knowledge-source integration

Depending on access and architecture, an assistant may connect to:

• SharePoint or document repositories
• PDF / Word / structured documents
• Websites and help centres
• SQL databases
• APIs
• CRM / support systems
• Approved data warehouses
• Curated Power BI / semantic-model data

Source selection should be deliberate. Giving an assistant access to more data does not automatically make it better.

Permission-aware access

The AI should not become a shortcut around existing access controls

• User identity
• Role-based permissions
• Department or team scope
• Document-level restrictions where supported
• Row / entity restrictions where appropriate
• Restricted sensitive fields

Permissions should be enforced at the retrieval or data-access layer rather than relying only on prompt instructions.

Conversational analytics

Let users ask business questions in natural language

For selected use cases, Datazeb can connect AI interfaces to curated metrics and reporting data.

• Ask for KPI summaries
• Compare periods
• Explain selected variances using approved data
• Retrieve supporting detail
• Generate first-pass management summaries for review

Workflow-connected assistants

Move from answering questions to helping complete a defined task

• Create a support ticket
• Route a request
• Trigger a notification
• Collect structured information
• Start an approved workflow
• Retrieve the status of a process

This is where an assistant begins to overlap with agentic AI. Actions should be limited, permissioned, observable, and reversible where possible.

Human-in-the-loop design

• Escalate uncertain questions
• Require approval for high-impact actions
• Provide source evidence for review
• Allow staff to correct or flag poor answers
• Separate informational answers from transactional actions

Human review is not a failure of AI. It is part of a reliable operating model when consequences matter.

Evaluation before launch

Test the assistant against real questions, not demos

• Known-answer test set
• Unsupported-question testing
• Permission testing
• Adversarial / prompt-injection scenarios
• Source-grounding checks
• Escalation tests
• Latency and usability
• Response consistency

The goal is to understand where the assistant performs well, where it fails, and what should remain outside its scope.

Monitoring after launch

• Unanswered questions
• Low-quality responses
• Escalation rate
• Frequently requested topics
• Source gaps
• Changed or outdated documents
• Action failures
• User feedback

A production assistant should improve through measured review, not through uncontrolled prompt changes.

Data privacy and control

• Approved data sources only
• Data minimisation
• Restricted sensitive fields
• Credential and secret management
• Logging appropriate to the use case
• Retention and access rules
• Environment separation where required

When a chatbot is enough – and when you need an agent

A chatbot or knowledge assistant is often sufficient when the primary goal is to answer questions, retrieve information, or guide a user.

An agentic workflow becomes more relevant when the system must perform multiple steps, call tools, update systems, or coordinate actions under defined controls.

Explore AI, Agentic AI & Chatbots

AI readiness assessment

Start by testing whether the use case has the foundations to work

• Clear user problem
• Defined knowledge sources
• Source quality and freshness
• Access model
• Expected answer types
• Risk level
• Escalation path
• Integration requirements
• Success measures

A small readiness assessment can prevent investment in an assistant whose knowledge base, ownership, or access model is not yet production-ready.

Example use cases

• Internal policy and SOP assistant
• Customer service knowledge chatbot
• IT / internal support assistant
• Sales enablement assistant
• Product knowledge assistant
• Healthcare administrative knowledge assistant
• Finance policy / procedure assistant
• Operations knowledge assistant

Why Datazeb for AI assistants

• AI capability combined with data engineering and system integration
• Practical RAG and knowledge-source architecture
• Focus on permissions, grounding, evaluation, and human oversight
• Ability to connect AI with business data, APIs, analytics, and automation
• Business-first scoping rather than chatbot-for-everything positioning
• Flexible project and managed-support models
• Senior-led global delivery

Engagement options

• AI readiness assessment
• Internal knowledge assistant pilot
• Customer-support chatbot project
• RAG implementation
• Conversational analytics pilot
• Workflow-connected assistant
• Existing AI assistant review
• Managed AI support and improvement

How we work

1. Define
   Clarify users, questions, outcomes, boundaries, risks, knowledge sources, and success measures.
2. Prepare
   Assess content quality, permissions, retrieval structure, integrations, and evaluation data.
3. Build
   Develop the assistant, retrieval layer, prompts, tool connections, access controls, and interface.
4. Evaluate
   Test real questions, unsupported cases, permissions, safety boundaries, escalation, and usability.
5. Deploy & Improve
   Launch in a controlled way, monitor failures and feedback, update sources, and improve the system over time.

FAQ