We design and develop secure Enterprise AI Knowledge Assistants that allow employees, customers, and management teams to search company information and receive accurate, contextual answers from private business data.
The solution can connect with documents, PDFs, Word files, spreadsheets, websites, databases, policies, manuals, product catalogues, support tickets, FAQs, and internal knowledge bases. Using Retrieval-Augmented Generation (RAG), semantic search, and Large Language Models, the assistant retrieves relevant information before generating an answer with source references.
Key features can include:
• Generative AI-powered question answering
• Retrieval-Augmented Generation (RAG)
• Private business knowledge base
• PDF, Word, Excel and document processing
• Website and database content ingestion
• Semantic search and vector search
• Source citations and document references
• AI chatbot and conversational interface
• Conversation history and contextual memory
• Document summarisation and comparison
• Information extraction and classification
• Multilingual questions and responses
• Department-specific knowledge assistants
• Admin dashboard and document management
• Knowledge-base synchronization and updates
• User feedback and answer-quality monitoring
• REST API and third-party integrations
• Single Sign-On and enterprise authentication
• Cloud or private-server deployment
• Multi-tenant SaaS architecture
• Data encryption, audit logs and security controls
• Human-agent escalation when required
We can develop knowledge assistants for customer support, employee onboarding, HR policies, legal documents, sales enablement, technical documentation, healthcare information, financial services, education, manufacturing, e-commerce, operations, compliance, and enterprise search.
Our AI capabilities include Generative AI, Large Language Models, Natural Language Processing, conversational AI, prompt engineering, embeddings, semantic search, vector databases, intelligent document processing, AI agents, function calling, and RAG architecture.
Depending on project requirements, the technology stack can include React.js, Next.js, Node.js, Express.js, NestJS, TypeScript, JavaScript, Python, FastAPI, Django, PostgreSQL, MongoDB, Redis, Elasticsearch, Pinecone, Weaviate, Qdrant, FAISS, ChromaDB, and suitable vector databases.
AI integrations can include OpenAI API, Azure OpenAI, Anthropic Claude, Google Gemini, LangChain, LlamaIndex, Hugging Face, Amazon Bedrock, and suitable open-source LLMs.
Deployment options include AWS, Microsoft Azure, Google Cloud Platform, Docker, Kubernetes, CI/CD pipelines, serverless infrastructure, private cloud, and on-premise environments.
With 25 years of software development experience, we combine proven engineering practices with modern AI technologies to build reliable, scalable, secure, and business-focused solutions.