AI assistants grounded in your data, not generic LLM output.
A useful AI assistant is grounded in your actual business data and constrained to what it should say. I build assistants that retrieve relevant information, follow defined behaviour and hand off to humans at the right point.
AI assistant development covers customer-facing chatbots, internal knowledge assistants, support deflection systems and document Q&A tools. Each is built with a retrieval layer from your actual data, not raw LLM prompting.
What this engineering discipline covers
- Retrieval-augmented assistant grounded in your content
- Context management and conversation history
- Defined scope and refusal behaviour
- Human handoff at appropriate points
- Integration with your existing knowledge base or CRM
- Logging for quality monitoring and improvement
How we engineer and deliver
Define the scope
What the assistant answers, what it refuses and when it escalates.
Ground in your data
Build the retrieval layer from your actual documents, FAQs or product data.
Evaluate and deploy
Test against adversarial and edge-case inputs before going live.
Have something worth building?
Let's turn the idea into software. Scoped in writing, engineered to specification, and delivered with precision.