AI/ML
Production-grade conversational AI systems — LLM-powered or intent-based — that handle real customer queries, integrate with your backend, and escalate intelligently.
0h
Response time
0+
Projects delivered
0+
Years in production
What it is
Conversational AI systems are software interfaces that use natural language understanding and generation to engage users in goal-directed dialogue, handling queries, collecting information, and completing tasks — either via large language models or intent-based dialogue engines.
What you get
The gap between a demo chatbot and one that resolves real customer queries is substantial. Demo chatbots answer FAQ questions from a static knowledge base. Production chatbots understand context across multiple turns, recognise when a query falls outside their scope, retrieve current data from live systems, and escalate to human agents with full conversation history when needed.
We build LLM-powered chatbots using RAG to ground responses in your documentation, product data, and knowledge base — preventing hallucination while keeping responses current without retraining. For high-volume, latency-sensitive applications, we offer hybrid architectures where intent classification handles common queries efficiently and the LLM handles complex or novel ones.
Integration depth determines business value. A chatbot connected only to a FAQ database provides limited ROI. A chatbot connected to your CRM, order management system, and ticketing platform can check order status, update account details, create tickets, and route to the right team — measurably reducing agent handle time.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Live system integration (CRM, OMS, ticketing, databases)
Intelligent escalation to human agents with context handover
Multi-channel deployment (web, WhatsApp, Slack, email)
Conversation analytics and intent coverage reporting
Automated testing and regression suite for dialogue flows
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
Platforms are the right choice if your use case fits their boundaries — standard FAQ, basic routing, simple form collection. Custom is warranted when you need deep integration with proprietary systems, specialised domain knowledge, or conversation flows that platforms cannot support. We assess this during discovery and recommend accordingly.
RAG grounds every response in retrieved source documents, so the model answers from your content rather than from training data. We also implement confidence thresholds that route low-confidence responses to human agents rather than generating an answer, and build automated evaluation pipelines that test response accuracy on a sample of real queries.
GPT-4o and Claude support 80+ languages natively. For a knowledge base in English, responses in other languages will be translated automatically. For the highest quality in a non-English primary language, we recommend building the knowledge base in that language from the start.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
