Why AIP Assist Exists
If AIP Logic is a tool for designing a specific workflow, AIP Assist is an operating copilot that sits beside the user and handles query writing, code generation, and data analysis on request, in natural language. What sets it apart from a generic chatbot is that AIP Assist answers with an understanding of the company’s own ontology, data schema, and permission structure already in place — so a request like “visualize the last three months of revenue data” can produce something that actually runs.
How It Works
Ontology-grounded RAG
- Internal company data and schema are vectorized, searched in real time, and injected into the LLM’s context.
- It goes beyond plain text search — it also draws on the relationships between objects defined in the ontology to produce answers that fit the actual business context.
Agentic execution (tool use)
- A natural-language request is carried through multi-step work — writing and running a SQL query, calling a visualization library, and so on.
- It includes a feedback loop: if code execution throws an error, it analyzes the error log itself and attempts a fix.
Governance
- It follows the user’s existing permissions (RBAC) as-is — questions about data the user isn’t authorized to see get refused or filtered.
- It keeps an audit trail of the code the AI ran and the data sources it referenced.
How Teams Use It in Practice
AIP Assist tends to be more useful in practice when you treat it as “a tool that runs repetitive work for you” rather than “a chatbot that answers questions.”
- Delegate repetitive queries and aggregation first: recurring data-pull tasks are the highest-leverage place to start.
- Sort out access scope in multi-user environments: if several people with different roles share the environment, it’s safer to clarify beforehand which data each role can access, before rolling out AIP Assist.
Common Mistakes
- Treating it like a generic chatbot with only open-ended questions: AIP Assist’s strength shows up on concrete data-work requests. Asking specifically — “take this property of this object and transform it this way” — produces better results than a vague open question.
- Pushing generated queries straight to production without review: an auto-generated SQL query or piece of code can look logically sound and still be wrong for the business context. For anything that matters, it’s safer not to skip a review step.
- Postponing permission design until later: even if the AI respects permissions, that’s meaningless if the underlying permission structure itself is weak. Sort out per-data access rights before introducing AIP Assist, not after.
Next Steps
If you want to offer a conversational interface as its own separate channel, look at Chatbot Studio next; if you need an agent specialized purely in analysis, look at Analyst.