ResAI is built for environments where every AI-assisted decision has to hold up the moment a question is asked. That changes what an AI platform has to do. Ground every answer in source data. Log every action. Keep sensitive data sensitive by default. ResAI does these things by architecture.
General AI assistants return answers ResAI composes your context, executes the workflow, routes the output to the right reviewer, and writes the audit trail. Every output carries its evidence.
Context, not prompts, configured once and applied everywhere
Outputs delivered inside the operational workflow they belong to
Quarterly review compiled across 12 sources. 8 items cleared, 3 flagged for follow up, 1 escalated to operations lead.
Every output carries its evidence, logged where it can be exported
ResAI is designed for simplicity. Connects directly to operational workflows and data via API or MCP integrations. Interface through a web app, Microsoft 365, or any LLM platform.
Type the question. Get the answer in plain language. Every response is grounded in the records that produced it, with the source available the moment a question is asked. No SQL. No BI ticket queue. No waiting a week for a chart that never quite answers what you needed.
Set up the routine work to run on its own. Pull data from your source systems on a schedule. Validate it against your standards. Send an alert when something needs attention. Route outputs to the right reviewer for sign-off. Publish curated datasets where your team already works. Free up your analysts for the questions that need them.
Describe the view you need. ResAI builds it, refreshed on demand or on a schedule, grounded in source data, ready to share with your team, your board, or your auditor. No ticket. No backlog. The dashboard reflects the question you actually asked.
Outputs that need a human eye route to the right reviewer for sign-off. Approve, return with feedback, or refine. Every decision logged. Every change traceable. Every output defensible the moment it leaves the system.
Every query. Every automation run. Every review action. Every dataset access. Logged, owned by you, available the moment a question is asked. The audit trail is how you'd want to do it anyway, except it's already done.
| Time | Actor | Event | Target | Status |
|---|---|---|---|---|
| 14:21:08 | m.chen | review action | inspection_q1_site_b | requested clarification |
| 14:18:42 | m.chen | dataset access | inspections_log | read |
| 14:02:11 | system | automation run | q1_summary_pipeline | completed |
| 13:47:55 | s.patel | query | cardiac_registry | ok · 3 sources |
| 13:32:02 | r.diaz | dataset access | sim_telemetry_412 | read |
| 13:18:41 | system | automation run | sop_validator | 3 alerts routed |