Ferguson BI builds AI knowledge assistants, agents, and automations around your business information and existing tools. Start with the question you need answered or the repetitive task you want to improve.
Discuss an AI or automation project ↗Use RAG when the main job is finding and explaining information, an agent when the work requires several steps and tools, and rule-based automation when the process is predictable. An engagement can combine these approaches.
| Approach | What it does | What to define first |
|---|---|---|
| RAG knowledge assistant | Retrieves relevant documents or data to help an AI assistant answer questions with source references. | Approved sources, access permissions, freshness, and examples of good answers. |
| Agentic AI | Uses tools and context to work through multi-step tasks, such as collecting information and preparing an action. | Allowed tools, permitted actions, review points, and when to hand work back to a person. |
| Workflow automation | Runs a defined sequence when an event or condition occurs, such as routing a document for approval. | Triggers, rules, application connections, and how to handle failures or duplicate events. |
These are illustrative workflows to discuss, rather than completed client projects. Feasibility depends on your systems, data, and access requirements.
A service team asks how to handle a particular request. A RAG assistant retrieves the relevant procedure and drafts an answer that points back to the source document.
Define success: Use representative questions to check relevance, source accuracy, and how the assistant responds when the material does not contain an answer.
An agent gathers an account's recent activity from approved systems, summarizes outstanding work, and prepares a follow-up for a team member to review.
Define success: Check that it uses the right account, stays within its permissions, and requests review before an agreed external action.
When a request arrives, an automation validates required fields, creates a task, and notifies the right team. Incomplete requests go to a review queue.
Define success: Check correct routing, duplicate prevention, and recovery when a connected application is unavailable.
Bring a description of the task, the tools involved, who uses the output, and an example of what a successful result looks like. You do not need to share credentials or raw business data to start the conversation.
Scope and timing depend on system access, source quality, workflow complexity, and support requirements. For dashboards, shared metrics, and ongoing reporting, see how fractional business intelligence works.
RAG retrieves information for a model to use when answering a question. It does not inherently require training a new model. The source collection, retrieval design, permissions, and answer evaluation are important parts of the work.
No. Retrieval can miss relevant material, documents can be outdated, and a model can misinterpret them. Source references, representative evaluation questions, and a clear path for uncertain answers help people review the output.
That depends on the workflow design. An agent can prepare actions for review or carry out agreed actions within defined permissions. We scope review points around the task and the consequences of an error.
No. If the task follows clear rules, a conventional automation may be sufficient. AI can help when the workflow needs to interpret unstructured information; it also introduces additional evaluation and operational requirements.
Hosting and model choices are scoped around your requirements. We discuss your existing environment, permitted providers, data access, and deployment constraints before selecting an architecture.
Contact Caleb at caleb@fergusonbi.com or describe your workflow in our contact form.