Solutions
AI Chatbot Development
We build AI chatbots and assistants that answer accurately from your own knowledge, take real actions in your systems and hand over to people when they should.
Chatbots That Resolve Questions, Not Just Respond
Large language models have transformed what chatbots can do. Instead of rigid decision trees, modern assistants understand free-form questions, retrieve relevant content from documents and databases, and complete tasks such as checking an order, booking an appointment or updating a record. The difference between a helpful assistant and an embarrassing one lies in grounding, integration, guardrails and continuous evaluation, not in the model alone.
Hanoi Technology designs and builds AI chatbots for customer service, sales, internal knowledge and operations. We use retrieval-augmented generation to ground answers in your approved content, connect assistants to business systems through secure tools, and deploy them on websites, mobile apps, WhatsApp, Zalo, Microsoft Teams or Slack. Every chatbot ships with an evaluation set, monitoring and a clear path to a human.
The Challenge
Why Chatbots Disappoint Users
Poor chatbot experiences usually come from a few predictable gaps.
Confident but Wrong Answers
Models without access to your content fill gaps with plausible fabrications. One wrong answer about pricing, policy or eligibility can damage trust and create liability.
Chatbots That Cannot Act
An assistant that only links to help articles frustrates users who want something done. Without integration, it cannot check status, change bookings or create tickets.
Dead Ends and Loops
Users who cannot reach a human get stuck repeating themselves. Escalation without context forces them to explain everything again.
No Measurement of Quality
Without evaluation sets and monitoring, teams cannot tell whether a prompt change helped or hurt. Quality drifts silently as content and products change.
Business Value
What a Well-Built AI Chatbot Delivers
Accurate, Sourced Answers
Retrieval grounds responses in your documents and data, with citations where useful. Answers stay consistent with official policy.
Real Task Completion
Secure tool calls let the assistant look up orders, schedule appointments or open tickets. Users get outcomes, not instructions.
Around-the-Clock Availability
Customers and employees get help at any hour and in many languages. Peaks no longer mean long queues.
Smooth Human Handover
When a case needs a person, the conversation and collected details pass to an agent. Customers never start over.
Lower Cost per Interaction
Routine questions are resolved automatically, freeing staff for complex and valuable work. Costs scale with usage rather than headcount.
Insight Into What Users Need
Conversation analytics reveal common questions, content gaps and product issues. Teams improve documentation and services based on evidence.
Evaluation Is the Product
A chatbot is only as good as the process that measures it. We build a test set of real questions with expected answers before launch, run it on every change and review live conversations weekly. This turns quality from a matter of opinion into a number that improves release by release.
Our Services
Hanoi Technology’s End-to-End AI Chatbots Services
We cover the full lifecycle of AI chatbots and assistants.
Use Case Discovery and Design
We analyze conversations, tickets and content to identify the questions and tasks worth automating, then design conversation flows, tone and escalation rules.
- Use case prioritization
- Conversation design
- Escalation policy
Knowledge Retrieval and RAG
We ingest documents, websites and databases, chunk and index them in a vector or hybrid search engine and keep the index synchronized with source changes.
- Content ingestion pipeline
- Hybrid search index
- Source synchronization
System Integration and Tools
We connect the assistant to CRM, order, booking, ticketing and internal systems through secure APIs with permission checks and audit logging.
- Tool and API integrations
- Permission enforcement
- Action audit logs
Multichannel Deployment
We deploy chatbots on websites, mobile apps, WhatsApp, Zalo, Facebook Messenger, Microsoft Teams and Slack, with consistent behavior across channels.
- Web and app chat widgets
- Messaging channel connectors
- Channel analytics
Guardrails, Safety and Compliance
We implement input and output filtering, topic restrictions, personal data protection and AI disclosure to users, aligned with regulations such as the EU AI Act transparency rules.
- Guardrail configuration
- Data protection controls
- AI disclosure messaging
Evaluation, Monitoring and Optimization
We create evaluation sets, monitor accuracy, latency, cost and satisfaction, and run regular reviews of conversations to improve content and prompts.
- Evaluation suite
- Quality and cost dashboards
- Monthly improvement reports
Our Approach
How We Build AI Chatbots
We launch narrow, measure carefully and expand based on results.
- 01
Discover and Prioritize
We review your conversation history and content to choose a focused first scope with clear success measures, such as resolution rate or handling time.
Deliverables: Scoped use case · Success metrics
- 02
Prepare Knowledge and Integrations
Content is cleaned and indexed, and the APIs needed for actions are connected and secured.
Deliverables: Knowledge index · Integrated tools
- 03
Build and Evaluate
We build the assistant, create an evaluation set with your experts and iterate until quality targets are met.
Deliverables: Working assistant · Evaluation results
- 04
Pilot With Real Users
The chatbot goes live for a subset of users or channels, with close monitoring and quick fixes.
Deliverables: Pilot report · Improvement backlog
- 05
Scale and Improve
Coverage expands to more topics, channels and languages, with ongoing monitoring and regular quality reviews.
Deliverables: Expansion roadmap · Ongoing quality reports
Use Cases
AI Chatbots We Build
Examples of assistants delivering value across customer and employee journeys.
Customer Service Assistant
A SaaS company’s chatbot answers product questions from documentation, checks account status and creates tickets with diagnostic details when needed.
Ecommerce Shopping Assistant
Shoppers ask about products, sizes and delivery in natural language. The assistant recommends items from the live catalog and tracks existing orders.
Internal Knowledge Assistant
Employees ask about policies, procedures and technical documentation in Microsoft Teams. Answers include links to the source and respect document permissions.
Appointment and Booking Bot
Clinics, salons or service businesses let customers book, reschedule and cancel appointments through chat. The calendar system updates instantly.
Lead Qualification Assistant
A real estate or B2B website assistant answers questions, captures requirements and books meetings with sales. Qualified leads arrive in the CRM with context.
IT Helpdesk Assistant
Employees reset passwords, request software and troubleshoot common issues through chat. Complex problems become tickets with steps already tried.
Technology
Technologies We Use for AI Chatbots
We select models and tools based on quality, cost, latency and data requirements.
- OpenAI GPT models
- Anthropic Claude
- Google Gemini
- Azure OpenAI
- Amazon Bedrock
- Llama
- LangGraph
- LlamaIndex
- Model Context Protocol
- pgvector
- Elasticsearch
- Qdrant
- Langfuse
- Python
- Node.js
- WhatsApp Business Platform
Platforms
Deliver AI Chatbots on Your Preferred Platform
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · SaaS
AI support assistant and smart escalation for a global SaaS company
A project-management SaaS provider receiving thousands of support requests a day needed faster answers without growing the support team. We built an AI assistant grounded in its knowledge base, with escalation to human agents.
Read the Full StoryMore Success Stories
E-commerce and retail
+32%conversion ratePersonalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Real estate
+38%lead conversion rateProperty discovery and lead conversion for a US real estate marketplace
A US marketplace connecting buyers, sellers and agents had plenty of traffic but few enquiries. We built personalized property discovery, AI valuation and a one-tap route to contact an agent.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Launch a Chatbot Your Customers Will Trust
Book a free consultation to discuss your use case, content and systems. We reply within 24 hours and can help scope a focused pilot with clear success measures.
AI Chatbot FAQ
How do you stop the chatbot from making things up?
We ground answers in your approved content using retrieval, instruct the model to answer only from that content and to say when it does not know, and add output checks for sensitive topics. An evaluation set of real questions tests accuracy before every release. Live conversations are monitored, and answers flagged as wrong are used to improve content and configuration.
Which AI model should we use?
There is no single best model. We compare candidate models from providers such as OpenAI, Anthropic, Google and open-weight families on your own evaluation set, considering answer quality, latency, cost, language support and data processing requirements. The architecture lets you switch models later as prices and capabilities change, so you are not locked into one vendor.
Is our data safe when using AI models?
We use enterprise model offerings or cloud platforms such as Azure OpenAI or Amazon Bedrock that do not use your data to train models, choose processing regions to match residency needs and encrypt data in transit and at rest. Personal data can be masked before reaching the model, and access to tools follows each user’s permissions.
Do we need to tell users they are talking to AI?
In many cases, yes, and it is good practice regardless. The EU AI Act’s transparency obligations, applicable from August 2026, require that people are informed when they interact with an AI system unless it is obvious from context, and other jurisdictions have similar rules. We design clear disclosure into the chat experience and recommend confirming specific obligations with your legal advisers.
How long does it take to launch an AI chatbot?
A focused chatbot answering questions from an existing knowledge base can often be piloted within a few weeks. Assistants that perform actions in several systems, support multiple channels or languages, or require extensive content cleanup take longer. We start with a narrow scope, measure results and expand, which delivers value early and limits risk.
What does it cost to run an AI chatbot?
Running costs include model usage, which depends on conversation volume and length, plus hosting, search infrastructure and monitoring. We estimate costs from your expected traffic, and reduce them with caching, smaller models for simple tasks and efficient retrieval. Development costs depend on integrations, channels and content preparation, and are estimated after discovery.