AI assistants grounded in approved business knowledge
Plan website or internal knowledge assistants with approved sources, access boundaries, evaluation examples and human escalation. Begin with a controlled question set and a defined pilot.
Who this service is for
Teams answering repeated questions from current support material, product information or internal documents. Suitability depends on data sensitivity and the consequence of an incorrect answer.
What the engagement can include
- Knowledge inventory and document ownership
- Retrieval with source references and access rules
- Unanswered-question and human support paths
- Evaluation cases for missing or conflicting information
- Knowledge updates, monitoring and handover
A practical workflow example
An assistant retrieves an approved support passage, answers the question and points to the source. It offers staff help when the material is incomplete or a consequential action needs approval.
When something goes wrong
Unsupported questions, conflicting sources and uncertain answers are declined or escalated. Approved-source retrieval can still produce errors and must be evaluated before wider use.
Inspect the synthetic workflow ↗Client responsibilities and integrations
Provide current material, publishing rights, document owners, evaluation questions and reviewers. Specify which information each role may access and which decisions the assistant must never make.
Implementation and running costs
Source cleanup, retrieval, interface integration and evaluation are distinct from hosting, storage, model usage and ongoing review. Document volume and access controls affect the cost.
Read the cost and ownership guide ↗Ownership, handover and support
Agree source updates, provider data settings, prompts/configuration handover, exports, deletion responsibilities and escalation coverage before launch. Private hosting needs a separate hardware and operations assessment.
Evidence and limitations
The public founder portfolio describes local multimodal AI work, and the existing hospital page describes its website assistant. Neither alone establishes enterprise-scale private-AI readiness or a universal accuracy figure.
Common questions
Can it answer only from approved documents?
Design retrieval and refusal behavior around approved material, then test unsupported questions, conflicting passages and access boundaries. No absolute accuracy guarantee is appropriate.
Can data stay on our server?
Assess hardware, model licensing, performance, access, backups and a maintenance owner in a feasibility review before promising a local deployment.
When should we avoid an AI agent?
Prefer a form, search interface or deterministic rule where the task is predictable, errors are costly, sources are unreliable or no one can review failures.
Evaluate the next step
Use observed volumes and a realistic support allowance before commissioning a workflow. A qualification discussion can establish fit and the boundaries of any diagnostic work.