SardiChatbot

An AI platform that connects answers to files, APIs, databases, and business logic so the chatbot becomes a practical multi-source response engine.

Integrated AI into real systems and data sources

Built logic on top of the LLM

Moved from a generic chatbot idea to an integrated operating layer

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What this case proves

Integrated AI into real systems and data sources

Built logic on top of the LLM

Moved from a generic chatbot idea to an integrated operating layer

What this case proves

Integrated AI into real systems and data sources

Built logic on top of the LLM

Moved from a generic chatbot idea to an integrated operating layer

The challenge

What made the case demanding?

• Questions were not all the same
• Some needed file-based knowledge
• Others needed APIs or databases
• The flow needed logic that selects the best answer path

What we did

How the solution was shaped

Delivery scope

• Designed logic for classifying inquiry types
• Split response paths across RAG, APIs, databases, and custom logic
• Shaped the orchestration using a LangChain / LangGraph-style approach

Value delivered

• A multi-source response engine
• Reduced hallucination risk
• Turned AI into a practical operational capability

Pages connected to this case

USP-aligned service

Generative AI Integration

We help you introduce AI into current or new systems through real data connections, workflow logic, and deployable implementation.

•AI inside existing systems
•AI inside new platforms
•Connected to data and APIs
•Advisory before implementation

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Applied AI service

AI Agents and Intelligent Assistants

We build AI assistants and agents that understand tasks, choose the right response path, and operate inside real business workflows.

•Task classification
•Multi-source orchestration
•Response and task automation
•Logic layer above the LLM

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Advisory layer

AI Integration Advisory and Discovery

We help you determine where and how AI should enter your systems and workflows before full implementation begins.

•Use-case selection
•Readiness assessment
•Commercially sensible scope
•Clear implementation path

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High-intent solution page

Arabic AI Chatbot for Service Businesses

We build this as a knowledge and response layer connected to files, APIs, data, and workflows instead of a superficial chatbot shell.

•Knowledge-connected responses
•Question classification
•Files and API integration
•Reduced hallucinations

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Publication readiness

Current missing proof assets

These gaps do not block structural implementation, but they still need to be closed before a fully proof-complete public launch.

• Quantified response-quality or efficiency outcomes
• Workflow visuals or diagrams
• Client publication approval if needed
• Deployment details if later needed

Do you have a current system or a new initiative and need the right next step?

We help you define the right scope and connect technology decisions to real workflow value before complexity gets out of hand.

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