Software & AI
AI chatbot development services that answer with your data
Qovex Studio builds AI chatbots and assistants that answer from your own content and systems: support bots, sales assistants, internal knowledge bots and AI agents on your website, WhatsApp, Messenger and Slack. Every bot cites its sources and hands off to a human when it should.
Chatbots and AI assistants we build
Each chatbot is built for one clear job, with your content, your systems and a human hand-off.
Customer support chatbots
Answers to policy, product and order questions around the clock.
Sales and lead qualification bots
Questions that qualify visitors and book meetings in your calendar.
Ecommerce shopping assistants
Product finders, order tracking and returns for online stores.
Internal knowledge assistants
HR, IT and policy answers for your team from internal documents.
AI agents with tools
Assistants that update orders, create tickets and book appointments.
WhatsApp and Messenger bots
The same assistant on the messaging apps your customers use.
Voice assistants
Phone and voice bots that answer calls and route them.
Booking and appointment bots
Availability checks, bookings and reminders in one conversation.
Chatbot rescue
Fixes for existing bots that give wrong answers or frustrate users.
Answers grounded in your data with RAG
Retrieval-augmented generation (RAG) makes a large language model answer from your documents instead of its general training data.
A RAG chatbot works in 3 steps:
Index: your help articles, policies, product data and PDFs are split into passages, converted into embeddings and stored in a vector database.
Retrieve: each question runs a hybrid search, semantic plus keyword, and the best passages are re-ranked.
Generate: the language model writes the answer using only those passages and cites them.
When no passage answers the question, the bot says so and offers a human instead of guessing.
- Answers with cited sources
- Hybrid search and re-ranking
- Permission-aware retrieval
- “I don’t know” fallback
- Automatic re-indexing of new content
Types of chatbots, compared
There are 5 main types of chatbot. The table compares setup speed, how well each handles open questions, whether it can take actions and the stack each one uses.
Most businesses get the best results from an LLM chatbot with RAG, extended into an AI agent for the tasks it can complete safely. Rule-based flows still suit fixed processes such as form filling.
AI agents that take action, safely
An AI agent is a chatbot that can use tools: it checks an order, updates an address or books an appointment instead of only describing how.
Agents call your systems through defined functions or the Model Context Protocol (MCP), an open standard for connecting AI models to tools and data. Every tool has a narrow permission scope, sensitive actions need the user’s confirmation, and each action is written to an audit log.
Accuracy, safety and privacy guardrails
A chatbot speaks for your brand. These guardrails ship with every build.
Accuracy
- Answers only from approved sources
- Source citations in replies
- Fallback when unsure
- Evaluation set tested before launch
Safety
- Prompt-injection defences
- Topic boundaries and content filters
- Rate limits and abuse detection
- Clear disclosure that it is a bot
Privacy
- Personal data redaction
- Encryption in transit and at rest
- API providers that do not train on your data
- GDPR-aware data retention
Hand-off
- Human escalation with full transcript
- Business-hours routing
- Satisfaction survey after chats
- Conversation analytics
Our chatbot and AI tech stack
Model-agnostic by design: we choose the model and tools per use case, cost and privacy need.
31 tools
Language models · Frameworks · Vector databases · Channels · Integrations · Voice · Engineering
Language models
- OpenAI GPTGeneral-purpose models for chat and tool use.
- Anthropic ClaudeLong-context models with strong instruction following.
- Google GeminiMultimodal models on Google Cloud.
- Meta LlamaOpen-weight models for private hosting.
- MistralEfficient open and hosted models.
Frameworks
- LangChainRetrieval pipelines and agent orchestration.
- LlamaIndexDocument indexing and retrieval.
- Hugging FaceEmbedding and open models.
- OllamaLocal models for private deployments.
- RasaOpen-source conversational AI.
- DialogflowIntent-based bots on Google Cloud.
Vector databases
- PineconeManaged vector search at scale.
- QdrantOpen-source vector search engine.
- PostgreSQL + pgvectorVectors stored next to your app data.
- ElasticsearchHybrid keyword and vector search.
Channels
- WhatsAppWhatsApp Business messaging.
- MessengerFacebook and Instagram messaging.
- TelegramBots for Telegram users and groups.
- SlackInternal assistants inside Slack.
- Microsoft TeamsInternal assistants inside Teams.
- IntercomWebsite messenger and support inbox.
Integrations
- ZendeskTicket creation and human hand-off.
- HubSpotLead capture and CRM updates.
- SalesforceCRM records and case management.
- ShopifyProducts, orders and returns.
- ZapierNo-code links to 7,000+ apps.
- n8nSelf-hosted workflow automation.
Voice
- ElevenLabsNatural text-to-speech voices.
- TwilioPhone numbers, calls and SMS.
Engineering
- PythonRetrieval, evaluation and back-end code.
- FastAPIFast, typed chatbot APIs.
How we build your chatbot
Seven phases from use case to a measured, improving assistant.
- 01
Discovery and use cases
Top questions, conversation volume, channels and the goal: deflection, leads or speed.
Use-case brief and success metrics
- 02
Knowledge audit
Content cleaned, structured, tagged and checked for gaps and permissions.
Prepared knowledge base
- 03
Conversation design
Persona, tone, flows, fallbacks and hand-off rules.
Conversation design document
- 04
Prototype
A working RAG pipeline on your data, tested by your team.
Working prototype
- 05
Evaluation
A test set of real questions scored for accuracy, grounding and tone.
Evaluation report
- 06
Integration and launch
Channels, CRM, helpdesk and analytics connected, then a staged rollout.
Live chatbot
- 07
Monitor and improve
Transcripts reviewed, unanswered questions turned into new content, models updated.
Monthly chatbot report
Measured by resolutions, not conversations
A chatbot is successful when it solves problems, not when it chats a lot.
We agree the success metrics in discovery and report them every month: how many conversations end resolved without a human, how accurate the answers are against approved sources, how satisfied users feel, and what each conversation costs compared with a human agent.
Ways to work with us
Start small, prove the value, then scale.
Pilot chatbot
One channel and one use case, live quickly to prove the value.
Custom RAG chatbot
A production assistant on your content and channels.
AI agent build
An assistant that completes tasks across your systems.
Website chatbot
A chatbot for your WordPress or Shopify site.
Chatbot rescue
Audit and rebuild of a bot that underperforms.
Managed chatbot
Monthly monitoring, content updates and improvements.
What we never do
The shortcuts that make chatbots unreliable and damage trust.
Risks we refuse
- Letting the bot invent answers
- Hiding that users talk to a bot
- Trapping users without a human option
- Giving the bot actions without permissions
- Launching without an evaluation set
Data rules we keep
- No training of public models on your data
- No personal data in logs without need
- No access beyond each user’s permissions
- No lock-in to one model provider
- No unreviewed content in the knowledge base
Plan your AI chatbot
Tell us what your chatbot should handle. We will reply with the right approach, a pilot plan and an estimate.
Frequently asked questions about Chatbot Development
What is a RAG chatbot?
A RAG chatbot uses retrieval-augmented generation: it searches your documents for relevant passages and has a language model answer using only those passages, with citations.
How much does a custom chatbot cost?
Chatbot cost depends on the number of use cases, channels, integrations, data preparation and compliance needs. A pilot on one channel costs far less than a multi-channel AI agent; we quote after discovery.
How long does it take to build a chatbot?
A pilot chatbot on one channel typically takes 3 to 6 weeks. Multi-channel assistants and AI agents with integrations take 2 to 4 months.
Which AI model do you use?
We choose per use case among OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama and Mistral, based on accuracy, cost, speed and privacy requirements.
How do you stop the chatbot from making things up?
The bot answers only from retrieved passages, cites them, and says it does not know when no source matches. Every bot is tested against an evaluation set of real questions before launch.
Is our data used to train AI models?
No. We use model providers’ business APIs, which do not train on your data by default, and can host open models privately for sensitive use cases.
What is the difference between a chatbot and an AI agent?
A chatbot answers questions. An AI agent also takes actions through tools, such as updating an order or booking an appointment, within strict permissions.
Can the chatbot hand off to a human?
Yes. The bot passes the conversation with its full transcript to your support team in tools such as Zendesk, Intercom or HubSpot.
Can the chatbot work on WhatsApp?
Yes. The same assistant runs on your website, WhatsApp, Messenger, Telegram, Slack and Microsoft Teams.
Does the chatbot support multiple languages?
Yes. Modern language models understand and answer in dozens of languages, and we test the languages your customers use most.
Can you improve our existing chatbot?
Yes. We audit transcripts and answers, fix retrieval and content gaps, add guardrails and hand-off, and measure the improvement.
How do you measure chatbot success?
We track resolution rate, answer accuracy, customer satisfaction, hand-off rate, response time, cost per conversation and leads or bookings captured.