Customer support chatbots
Grounded in your help centre, policies and order systems. Answers with citations, performs account actions, and escalates to a human with full conversation context attached.
AI chatbot development
Chatbots that resolve, not deflect.
We build chatbots grounded in your own content and connected to your own systems, so they answer with facts, take real actions and hand over cleanly when a person is needed.
The reason most chatbots are disliked is simple: they were built to reduce ticket volume rather than to solve problems. A chatbot that cannot see the customer's order, cannot check a policy and cannot escalate is a wall with a friendly voice. We build the opposite. Our chatbots are retrieval-grounded, connected to the systems that hold the answer, and measured on resolution rate rather than on deflection.
Every chatbot ships with the parts teams discover they need in month three: source citations on every answer, a confidence threshold that triggers handover, conversation analytics that surface what customers actually ask, guardrails against off-topic or unsafe replies, and an evaluation suite so a model or prompt change cannot quietly degrade quality.
The chatbots we build
Internal knowledge assistants
A chatbot over your wikis, SOPs, contracts and tickets, with permissions respected per user so nobody sees a document they could not open directly.
Sales and commerce chatbots
Product discovery, configuration help, quote generation and lead qualification that hands a scored, summarised lead to your sales team.
WhatsApp and messaging bots
Deployed where your customers already are - WhatsApp, web widget, in-app, Slack, Teams - with one shared brain and channel-appropriate behaviour.
Voice chatbots
Speech to speech assistants for phone lines and contact centres, with barge-in, low latency turn-taking and warm transfer to an agent.
Private and on-premise chatbots
Self-hosted models inside your network for regulated data, with no third-party inference and complete conversation retention control.
How we build a chatbot that holds up
- 01
Ground truth first
We inventory the content and systems that hold real answers, clean them, and build the retrieval layer before writing a single prompt.
- 02
Evaluation before launch
A test set built from your real historical conversations. We score accuracy, grounding and refusal behaviour, and we publish the number.
- 03
Actions and escalation
Tool access to your order, ticket and account systems, permissioned per user, with confidence thresholds that route to a human rather than guess.
- 04
Learn from live traffic
Weekly review of unresolved conversations, gaps fed back into content and retrieval, and regression tests so quality moves in one direction.
What teams use these chatbots for
Support deflection with resolution
Tier one questions answered with citations, and the ticket only created when the chatbot cannot finish the job.
Order and account self-service
Status, changes, returns and billing handled conversationally against live system data.
Employee helpdesk
HR, IT and policy questions answered from internal documents with per-user permissions enforced.
Lead qualification
Website visitors qualified, scored and summarised for sales instead of dropped into a generic form.
Field and frontline support
Technicians querying manuals and procedures hands-free from a phone on site.
Onboarding assistants
New customers or staff guided through setup with contextual answers instead of a PDF.
The conversational stack behind them
Reasoning
- Frontier LLMs
- Self-hosted open models
- Fine-tuned small models
- Routing between tiers
Grounding
- Hybrid retrieval
- Vector databases
- Rerankers
- Permission-aware indexes
Channels
- Web widget
- WhatsApp Business API
- Slack and Teams
- Voice and telephony
Quality
- Evaluation suites
- Guardrails
- Conversation analytics
- Human handover
Trusted to ship AI across the world's operational industries
ManufacturingLogisticsEnergyHealthcareFinanceSmart CitiesAgricultureRetailAerospaceTelecomMiningPublic SectorManufacturingLogisticsEnergyHealthcareFinanceSmart CitiesAgricultureRetailAerospaceTelecomMiningPublic Sector
Questions we get asked about ai chatbots
How is an AI chatbot different from the old rule-based kind?
A rule-based chatbot follows a decision tree and fails the moment a user phrases something unexpectedly. An AI chatbot understands intent in natural language, retrieves the relevant facts from your own content and systems, and composes an answer. The important part is the grounding: without it, a language model will sound confident and be wrong.
Will the chatbot make things up?
Not if it is built correctly. We ground every answer in retrieved source content, show citations, set a confidence threshold below which the chatbot escalates instead of answering, and run an evaluation suite that catches regressions before they reach customers.
Can the chatbot do things, not just answer?
Yes. We connect chatbots to your order, ticketing, CRM and billing systems with scoped permissions so they can check status, update records, issue refunds within policy limits and create tickets, with every action logged.
Where does our conversation data go?
Wherever you decide. We deploy into your cloud account, and for regulated data we run self-hosted models so no conversation content leaves your network. Retention periods and redaction rules are yours to set.
How long does a chatbot take to build?
A grounded support chatbot on existing content typically reaches a measured pilot in three to five weeks. Adding system actions, voice or multiple channels extends that depending on the number of integrations.
Related capabilities
Let's build
Have an idea? We'll ship it.
Tell us the problem. We'll come back with an architecture, a timeline and a team ready to build it in production.