Production AISystems
Conversations

Conversational AI Solutions

Conversational AI systems that answer questions, hold useful conversations across chat, voice and messaging, connect to your business systems and hand off to people when the situation needs judgment. Deploy in the cloud, a private cloud or on your own infrastructure.

Plan a Conversational AI System Explore the Solutions
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What is a conversational AI solution?

A conversational AI solution is a system that understands natural language across chat, voice and messaging channels, retrieves knowledge from your approved content, takes actions through your business APIs and escalates to a human when confidence is low or the stakes are high. Unlike a standalone chatbot, it shares context across channels so a conversation started on web chat can continue on WhatsApp without repeating information. The system is engineered for production—measured on resolution rate, handoff accuracy and response latency, not just on whether it can generate a plausible-sounding reply.

Customers repeat themselves across channels

Context is lost every time a conversation moves from chat to email to phone. Shared memory fixes this.

Agents handle routine questions that AI could resolve

Password resets, order status and policy lookups consume agent time. AI handles these and frees people for complex cases.

Knowledge is scattered across systems

Answers live in SharePoint, CRM, ticketing and internal wikis. Conversational AI retrieves from all of them with citations.

Handoffs lose context

When AI escalates to a human, the agent gets the full conversation, intent, actions attempted and retrieved evidence—not a blank screen.

How This Solution Can Be Built

Different situations call for different implementations. These are the most common variants.

Internal assistant

Employees ask about policies, tools and processes. Grounded in internal knowledge with permission-aware retrieval.

Customer-facing agent

Customers get answers about products, orders and support. Integrated with CRM and order systems for real actions.

Omnichannel deployment

One system across web chat, WhatsApp, voice and email. Shared context, consistent answers, unified audit trail.

How the System Works

From input to output, each step is engineered for a specific purpose.

1

User message

Customer or employee sends a message via chat, voice, WhatsApp or email

input
2

Intent detection

System identifies what the user wants and which knowledge domain applies

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3

Knowledge retrieval

Relevant content fetched from approved sources with permission filtering

data
4

Response generation

Answer composed from retrieved sources with citations

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5

Action execution

If the request needs a business action, the system calls the right API under controlled permissions

output
6

Confidence check

If confidence is low or stakes are high, the conversation is routed to a human

control
7

Human handoff

Agent receives full context: conversation, intent, actions attempted, retrieved evidence

human
8

Feedback loop

Outcomes and corrections feed back into retrieval, prompts and evaluation

feedback

Production and Enterprise Readiness

What makes this solution work in a real operating environment—not just in a demo.

Permissions

Retrieval is filtered by user role and department. No document the user cannot see is returned.

Guardrails

Content and action controls prevent unsafe responses and unauthorised system actions.

Human escalation

Configurable confidence thresholds route low-confidence or high-stakes conversations to a person.

Evaluation

Conversation quality is measured on resolution, containment, handoff accuracy and user satisfaction.

Deployment

Cloud, private cloud or on-premises. Sensitive conversations never leave your environment if policy requires.

How Success Is Measured

The right metrics depend on the solution. These are the measures that matter for this system.

Illustrative system view — metrics shown in the dashboard above are labelled examples, not client results.

Resolution rate

Percentage of conversations resolved without human escalation

Handoff rate

Percentage of conversations escalated to a human, and whether the handoff was correct

Response latency

Time from user message to first useful response, measured at P50 and P95

Conversation completion

Percentage of conversations that reach a natural conclusion rather than abandonment

Channel usage

Distribution across chat, voice, WhatsApp and email, with cross-channel continuation rate

View related case studies

Plan a Conversational AI System

Tell us the workflow, problem or system you want to improve. We respond with how we would approach it.

Plan a Conversational AI System

No finished technical specification required.

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Frequently Asked Questions

Can the system share context across chat, voice and WhatsApp?
Yes. A conversation started on one channel can continue on another. The system stores a shared session with the user's identity, conversation history, detected intent and retrieved context. When the user switches channels, the new channel picks up the same session rather than starting over.
How does the system know when to hand off to a human?
Confidence thresholds are configured per intent and per action. If the model's confidence is below the threshold, or the request involves a high-stakes action like a payment or account change, the system routes to a human agent with the full conversation context, actions attempted and retrieved evidence.
Can it take real actions in our CRM or ticketing system?
Yes. The system connects to your APIs through a controlled tool layer. Each tool has a defined purpose, input schema and permission scope. Actions that modify data require either automatic validation against business rules or explicit human approval, depending on the stakes.
Does it work with our existing knowledge base?
Yes. The system retrieves from SharePoint, Confluence, Google Drive, internal wikis, CRM knowledge bases and custom APIs. Retrieval is permission-aware—users only see content they are authorised to access.
Can we deploy it on-premises for sensitive conversations?
Yes. The conversational AI system can run entirely inside your infrastructure with open-source models, or in a hybrid configuration where sensitive data stays on-premises and non-sensitive processing uses cloud models.
How do you measure conversation quality?
Resolution rate, containment, handoff accuracy, response latency and user satisfaction are tracked. Evaluation sets test known conversations against expected outcomes. Drift detection flags when quality changes over time, and feedback from agents and users feeds back into retrieval and prompts.