Intelligent Chatbots & Assistants
Conversational AI assistants trained on your own documentation and data, deployed entirely within your infrastructure. Accurate, cited, and domain-aware without any data leaving your environment.
RAG Architecture
Responses are grounded in your actual documentation retrieved at query time, verified, and cited. No hallucinations from stale training data. The knowledge base updates as your documents update.
Domain-Specific Training
The assistant is trained and prompted on your terminology, workflows, and internal knowledge. It understands your products, policies, and processes, not generic internet knowledge.
Multi-Language Support
Native NLP for local markets. Whether your staff communicates in English, German, French, Arabic, or other languages, the assistant handles it with domain accuracy, not just translation.
Escalation & Handoff
When the assistant reaches the boundary of its knowledge or detects escalation signals, it hands off to a human agent with full conversation context. No lost threads, no repeated questions.
Why Not Just Use ChatGPT or Copilot?
General-purpose AI assistants are trained on broad internet data. They can answer general questions competently, but they do not know your internal policies, your product specifications, your compliance requirements, or your organisational structure. When asked about something specific to your business, they either hallucinate a plausible-sounding but incorrect answer or admit they don't know.
More critically, using cloud AI services means sending your internal documents, customer queries, and proprietary knowledge to a third-party provider. For organisations handling patient data, financial records, legal documents, or any information subject to HIPAA, GDPR, or internal data governance policies, this is a compliance risk. An on-premise chatbot trained on your data eliminates both problems: it knows your domain deeply and your data never leaves your network.
How It Works
Our chatbots are built on Retrieval-Augmented Generation (RAG) architecture. Instead of relying solely on what the model learned during pre-training, RAG retrieves relevant passages from your document library at query time and uses them to generate an accurate, grounded response. This approach offers several critical advantages:
- —Always up to date: When your policies change or new documentation is published, you update the document library and the chatbot immediately reflects the new information without any model retraining.
- —Source attribution: Every answer includes citations pointing to the specific document and section it was drawn from. Users can verify the source directly, which is essential for regulated environments where traceability is required.
- —Reduced hallucination: By constraining the model to answer based on retrieved documents rather than generating from memory, the rate of incorrect or fabricated responses drops significantly compared to a standalone LLM.
- —Access-controlled responses: Document-level permissions mean that different users see different answers based on what they are authorised to access. An HR manager querying benefits policies gets different depth than a general employee.
Common Deployment Scenarios
Deployment Timeline
A typical chatbot deployment takes 4 to 8 weeks. The first two weeks focus on document ingestion, parsing your documentation library, chunking content for optimal retrieval, and embedding into a vector database. Weeks three and four cover model deployment, prompt engineering, and tuning the retrieval pipeline for your domain vocabulary. The remaining time is spent on integration with your existing platforms (intranet, ticketing system, Slack, Teams), user acceptance testing, and staff training. More complex deployments with multiple knowledge bases, strict RBAC requirements, or multi-language support may extend to 10 to 12 weeks.
Technology Stack
Core NLP and deployment tools