Useful automation
Focus AI on repeatable work where speed, consistency and human oversight can be measured.
Generative AI
Conversational AI
Turn scattered support content into a chatbot or voice agent that answers from your own knowledge base, escalates to a human when it should, and keeps improving after launch.
Helpful support and knowledge experiences at scale.
- Human-in-the-loop
- controls
- Evidence-led
- evaluation
- Secure
- integration
01
Quick answer
What is Conversational AI?
Conversational AI combines natural language understanding, retrieval, and dialogue management so a chatbot or voice agent can interpret a request, ground its answer in approved knowledge, and respond in natural language across channels such as web chat, voice, or messaging apps.
- Chatbots, voice agents & live-chat handoff in one build
- Grounded in your own knowledge base via retrieval
- Human handoff & escalation rules built in, not bolted on
- Evaluation, monitoring & cost controls from day one
Business outcomes
Built to answer accurately, not just sound conversational.
Integr8e approaches conversational ai as a business capability—not an isolated technical task. Priorities stay connected to the users, operations and results behind the work.
Grounded answers
Connect models to approved business knowledge so responses are relevant and traceable.
Responsible adoption
Design permissions, evaluation and review controls around real operational risk.
Production readiness
Build for observability, cost control, model change and dependable integration.
What we deliver
Conversational AI services from use-case scoping to live channels.
One connected team covers the decisions and delivery work needed to move from uncertainty to a dependable outcome.
Use-case strategy
Prioritise opportunities by business value, data readiness, risk and implementation effort.
Retrieval & knowledge
Prepare governed information and retrieval flows that keep answers grounded.
Agentic workflows
Connect models, tools and approval steps to complete useful multi-stage work.
AI product engineering
Create intuitive interfaces and robust services around model capabilities.
Evaluation & guardrails
Measure answer quality, safety, latency and cost before and after release.
AI operations
Monitor usage, failures, model behaviour and emerging opportunities over time.
Is this the right fit?
Best suited for support, product, and CX leaders replacing scripted chatbots or overloaded support queues with a conversational AI grounded in real business knowledge.
Not every challenge needs the same team or solution. We start by testing the business case, current constraints and fastest credible route to value—then recommend a scope that fits the evidence.
Delivery architecture
A controlled path from use-case framing to a live conversational agent.
The plan adapts to your context, while short feedback loops and visible milestones keep the engagement controlled.
- 01
Frame
Define the task, user, acceptable output, risk boundaries and measurable value.
- 02
Prepare
Assess data, permissions, integrations and the evaluation set required for confidence.
- 03
Prototype
Test the workflow quickly with representative inputs and real user feedback.
- 04
Productionise
Add guardrails, observability, security and reliable system integrations.
- 05
Evaluate & evolve
Track quality and cost continuously as models, data and needs change.
Technology context
Tools chosen around the problem—not the trend.
We select platforms against security, scale, team fit, integration needs and the full cost of ownership.
- OpenAI
- Python
- AI Agents
- Node.js
- PostgreSQL
- MongoDB
- Redis
- Docker
- AWS
- Azure
Frequently asked questions
Answers about conversational ai.
Need an answer specific to your environment? Share the context and our team will help you identify a practical next step.
What is conversational AI?
Conversational AI combines natural language understanding, retrieval, and dialogue management so a chatbot or voice agent can interpret a request, ground its answer in approved knowledge, and respond in natural language across channels such as web chat, voice, or messaging apps.
What's the difference between a scripted chatbot and conversational AI?
A scripted chatbot follows fixed decision trees and only handles inputs it was explicitly programmed for. Conversational AI uses a language model to interpret intent and context, so it can handle varied phrasing, ground answers in retrieved knowledge, and hand off to a human when a request falls outside its scope.
Can a conversational AI assistant use our own knowledge base and documentation?
Yes. Approved documentation, help centre content, and internal knowledge can be connected through retrieval so answers stay grounded in your material instead of the model's general training data, with source traceability for review.
Which channels can a conversational AI assistant support?
Assistants can be deployed on web chat, in-product help widgets, voice, and messaging platforms depending on where your users already look for support. Channel choice is confirmed during discovery based on volume and user behaviour.
How do you prevent a chatbot from giving wrong or unsafe answers?
We constrain the task, ground responses in approved content, require citations where useful, and design escalation rules so the assistant hands off to a human agent instead of guessing on ambiguous, sensitive, or out-of-scope requests.
How is a conversational AI assistant measured after launch?
We track answer accuracy, containment rate, escalation quality, latency, and cost alongside user feedback. Evaluation cases are extended with real production conversations so quality is monitored continuously, not just tested once before launch.
How much does a conversational AI project cost?
Cost depends on the number of channels, the complexity of retrieval and integrations, and whether voice is included alongside chat. A single-channel assistant grounded in an existing knowledge base costs less than a multi-channel deployment with several system integrations. We scope the use case first and provide a fixed estimate.
How long does it take to launch a conversational AI assistant?
A focused assistant covering one channel and a defined knowledge base typically reaches a production-ready release in six to ten weeks, including evaluation. Multi-channel or voice deployments with several integrations take longer. Timing is confirmed after scoping the use case and available content.
Start with the outcome
Let's make your conversational ai initiative concrete.
Tell us what needs to change, who it affects and where the current approach falls short. We'll help shape a sensible next step.
hello@integr8e.com