Chatbots that actually convert. We build assistants that answer from your own data, handle support and sales around the clock, and plug into WhatsApp, your website and your CRM.
Trusted by teams worldwide
From a simple FAQ bot to a multi-channel AI agent. Six service lines cover every part of building a chatbot that ships and performs. Need one that takes action rather than just answering? See our AI agent development work.
Most projects start as one of these eight. Each solves a different business problem, and the right one is usually obvious once the workflow is on the table. Assistants that take action rather than answer belong with AI agent development.
Six engineering disciplines behind every AI assistant we ship, and the tools we use for each.
We pick the model per workload rather than defaulting to one vendor, then tune tone, refusal behaviour and formatting until the assistant sounds like your team instead of a generic bot. Prompt and model choice are versioned like code, so a change is reviewable and reversible. Where a task needs to act rather than answer, we hand it to an AI agent.
Your documents, help centre, product data and database records are chunked, embedded and indexed, so the assistant answers from approved content and shows the source. Updating a policy updates the answer, with no retraining. This is the single biggest factor in whether a chatbot earns trust internally, and our RAG implementation guide covers the architecture.
One assistant, one knowledge base, many front doors: a web widget, WhatsApp, Slack, Microsoft Teams, SMS and in-app. Conversation history follows the customer across channels, so nobody repeats themselves after switching. Each channel keeps its own rules, which matters most on WhatsApp, where templates and opt-in are enforced by the platform. Multi-language handling is covered in our multilingual chatbot guide.
Voice assistants that handle inbound calls, qualify, book and deflect the routine ones. We tune speech-to-text and text-to-speech for interruption and turn-taking, because a caller talking over the bot is normal rather than an edge case, and we set escalation rules so a frustrated caller reaches a person quickly. Latency targets are agreed per deployment against your telephony setup rather than promised up front. See AI voice agent development for the full service.
A chatbot is only as useful as what it can see. We connect it to your CRM, helpdesk, booking system and order data behind your own authentication, so it can discuss a specific account rather than recite generic help text, and write back so your team is not retyping. Where the CRM itself is the gap, we also do CRM development.
We build an evaluation set from real questions before launch, so quality is a number you can track rather than an impression. Guardrails constrain topics, block unsafe output and force escalation when confidence is low. Analytics show what people actually ask, what went unanswered and where handover happened, which is what drives the tuning after go-live.
Retrieval-Augmented Generation is what separates a chatbot that guesses from one your team trusts. The model answers from your approved content and shows where the answer came from, which is what makes hallucination a manageable engineering problem rather than a gamble. Our guide to implementing RAG covers the architecture in depth.
For a lot of markets WhatsApp is where customers actually message a business, long before they open email. We build custom assistants on the WhatsApp Business API and connect them to the systems behind your service desk. Our WhatsApp AI assistant case study reports a 75% reduction in human-handled ticket volume and 92% CSAT.
A short call turns your goals into a clear plan: use cases, channels, the right model and a fixed budget. So your first dollar goes toward something that ships and performs.
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A proven process that removes the science-project risk from AI adoption.
We map use cases, channels and success metrics, and agree a fixed quote.
We connect and prepare your content so the bot answers accurately.
We build the assistant, integrations and guardrails, then tune on real questions.
We run evaluation sets and red-teaming to make sure answers are safe and correct.
We go live, monitor conversations and keep improving from real usage.
Any figure quoted before the workflow is on the table is a guess. Cost tracks scope, and these are the things that actually move it. For worked ranges and a fuller breakdown, see our AI chatbot development cost guide.
Figures below come from named, published deployments rather than blended averages.
Instant answers for customers, in every time zone.
Measured on the same deployment, alongside up to 90% of FAQs resolved.
Across AI, mobile, web and enterprise work, including chatbots and voice.
Real products, real users, real numbers, across health, mobility, delivery and education.
Mood tracking, guided sessions and personalised wellness plans in a warm, calming experience.
Live driver tracking, surge-aware fare estimation and multi-payment support in a fast, reliable app.
Live order tracking, smart dispatch and route optimisation that cut delivery times across 200+ restaurants.
Coach matching, personalised study plans and adaptive progress tracking for 10K+ students.
Chosen per project. We are not tied to a single model vendor, and we will say so when a simpler tool is the right answer.
The same assistant behaves very differently by sector. These are the chatbot use cases each one usually starts with.
Patient-facing assistants that book, remind and answer, while staying clear of clinical advice.
Account and payment support that works inside strict authentication and audit requirements.
Product discovery and order support running on live catalogue and order data.
The where-is-my-order question, answered without a human, plus support for the people delivering.
Assistants for learners and admissions teams that answer from your own course material.
Lead qualification and viewing booking that keeps working outside office hours.
“Appther built our student competition app exactly the way we envisioned it: easy registration, smooth judging, and real-time results that keep participants engaged.”
“Building a mental health app means every detail has to be handled with care, and Appther got that from day one. The product feels warm, simple, and genuinely helpful.”
“Appther exceeded every expectation, on time and on budget, and the final product has been instrumental in scaling our business. A rare engineering partner.”
“Implementing an ERP across our operations felt daunting at first, but Appther made the transition seamless. Every department now works off a single source of truth.”
Field notes from real builds. No hype, just what worked and what didn't.
How we choose the right approach to ground your bot in your data.
Guardrails, evaluation and the failure modes nobody shows you.
Design and safety patterns for high-volume messaging bots.
A clear breakdown of what drives chatbot cost, and how to scope it.
Walk away from one call with a shortlist of use cases, the right model and channels, a realistic budget range and a 2 to 4 week pilot plan. No pitch deck, no obligation.
Tell us what you want your chatbot to do. No sales pitch, just a straight answer on scope, timeline and cost.
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