Use Cases

Use cases are not generic AI ideas. They are candidate workflows with a business owner, approved data, a review point, and a proof that shows whether an AI agent should be developed further.

Support workflow

Customer Support Copilot

Turn scattered policies, past replies, and escalation rules into a reviewed response workflow for recurring customer questions.

Agent role: Retrieve approved context, draft replies, show sources, and flag low-confidence cases before sending.
Business signal: Shorter review time, fewer repeated answers, and clearer escalation decisions.
First proof: Test 30 recent tickets with source citations, reviewer edits, and escalation reasons.

Revenue workflow

Sales Knowledge Search

Give sales teams a controlled way to search proposals, CRM notes, service details, and past meeting context before follow-up.

Agent role: Find relevant account history, summarize next actions, and prepare meeting or proposal briefs.
Business signal: Less preparation work, better handoff quality, and more consistent proposal context.
First proof: Run the workflow on 10 active accounts and compare preparation time and manager review comments.

Operations workflow

Back Office Automation

Move recurring reporting, document preparation, and internal requests from manual handoffs into controlled AI-assisted steps.

Agent role: Classify requests, check required fields, prepare drafts, and route exceptions to the right owner.
Business signal: Fewer missing fields, fewer status checks, and a visible audit trail for repeated work.
First proof: Start with one request type and measure rework rate, cycle time, and exception reasons.

Knowledge workbench

Internal AI Tools

Prototype narrow internal tools that help teams review data, draft documents, and coordinate knowledge work inside existing routines.

Agent role: Read approved sources, generate structured drafts, check assumptions, and keep a review log.
Business signal: More reusable work outputs, fewer blank-page starts, and clearer ownership of AI-assisted work.
First proof: Build one lightweight tool around a weekly task and review adoption after two operating cycles.