Conversational AI
Chat, voice and contact centre AI that resolves the issue instead of deflecting it to a form. Eight engagements covering the channel, the conversation and the analytics behind it.
Deflection is not resolution.
Most conversational deployments were scored on how many contacts they kept away from a human, and every design decision followed from that number: broad shallow coverage, buried escalation, and failure language written last by whoever was free. The result is a channel people avoid, and a containment figure that looks respectable in a board pack.
We build for resolution and report containment alongside satisfaction, so a rise in one with a fall in the other counts as a regression rather than a win. Scope comes from your real transcripts, answers are grounded in your own content, and the assistant is designed to hand over quickly rather than to keep somebody talking to it.
What we build
Each is a standalone engagement with its own scope, price and output. Most clients use two or three in sequence.
AI Chatbot Development
A chatbot grounded in your own content, measured on resolution and satisfaction rather than on messages handled.
Read more →Voice Bot and IVR Modernisation
Menu trees replaced with natural speech, intent-based routing and a fast path to a person.
Read more →WhatsApp, Messenger and Slack Bots
Bots on the messaging platforms your customers already use, built around each platform's opt-in and window rules.
Read more →Virtual Assistant Development
One internal assistant across HR, IT and operations that answers from policy and raises the ticket for you.
Read more →Conversational Design and CX Strategy
The design work that decides whether an assistant is used twice: scope, failure recovery, escalation and tone.
Read more →Speech-to-Text and Text-to-Speech
Recognition and synthesis tuned on your own audio, measured on your vocabulary rather than on clean benchmarks.
Read more →Multilingual and Real-Time Translation Bots
Assistants that work in every language you support, with quality measured per language before launch.
Read more →Contact Centre AI and Call Analytics
Every conversation analysed: automated QA, live agent assist, wrap-up and the reasons behind your contact volume.
Read more →How they fit together
You do not need all twelve. Most programmes follow one of these paths depending on where the uncertainty sits.
Customer-facing, text
AI chatbot development on your site or in your product, extended to WhatsApp, Messenger and Slack where your customers already are, and multilingual once the base language works.
Customer-facing, voice
Voice bot and IVR modernisation to replace the menu tree, underpinned by speech engineering tuned on your own calls.
Internal
Virtual assistant development across HR, IT and operations, in the tools your colleagues already have open.
Already live and underperforming
Conversational design and CX strategy to fix scope, failure recovery and escalation without a rebuild, and contact centre AI to find out what is actually being said.
FAQ
Marked up with FAQPage schema so these answers can surface in search results and inside AI assistant responses.
What is the difference between conversational AI and an AI agent?
Conversational AI is about the channel and the exchange: understanding a question, answering it well, escalating properly. An AI agent completes work in your systems, which brings scoped permissions, reversible writes and an audit trail. Many organisations start with a conversational build and add agent capability where resolution genuinely requires it.
Should we build for chat, voice or messaging first?
Wherever your contact volume already is, which is usually visible in your own data rather than a matter of preference. Chat is the fastest to prove, voice carries the highest per-contact cost and therefore the largest saving, and messaging depends on whether your customers already use those platforms with you.
How do you measure whether a conversational deployment worked?
Resolution, escalation reasons, unanswered questions, satisfaction and abandonment, reported together against a baseline captured before launch. Containment on its own is a dangerous target because it improves whenever reaching a person gets harder.
Can you improve an assistant we already have?
Yes, and it is one of the more common engagements. Conversational design works on systems built in-house or by another supplier, and in most cases improves them substantially without a rebuild.
Who owns the code, content and conversation design?
You do, in full, on final payment: source code, prompts, retrieval configuration, evaluation sets, conversation copy and infrastructure as code. Nothing about running the assistant afterwards depends on us remaining involved.
Start with a conversation.
Thirty minutes, no charge, no deck. Tell us what you are trying to build with language models and we will tell you which of these engagements fits, or whether none of them do.