Practical guides

These guides are task-shaped: each one takes a real goal and walks the decisions and trade-offs, not just the clicks. They assume you've done the basic setup in the Get Started orientation — channels connected, knowledge ingested, an agent configured.

Callario is in supervised alpha, so every guide leans the same way: start narrow, keep a human in the loop, verify with evidence, and widen scope as trust builds. Pick the guide that matches what you're trying to do next.

Move without losing your history or your momentum. Bring contacts and organizations into the shared CRM first so the AI and the inbox have context from day one, then connect your channels so live traffic lands in Callario.

Run Callario alongside your current tool during the cutover rather than flipping everything at once — route a slice of traffic, watch the resolution receipts, and expand. The honest difference to plan around: you're moving to outcome-based, auditable billing, so map your old per-seat or per-resolution assumptions to verified resolutions before you forecast cost.

The single highest-leverage thing you can do. Ingest accurate, current articles into the knowledge base so the RAG layer gives the agent real sources to stand on, and prune anything stale — confident answers from outdated content are worse than no answer.

Scope each agent to the right slice of knowledge, then test with sandbox dry runs and read the citations on the answers. If an answer isn't grounded, the guardrails should hold it back — and an ungrounded answer is never billed, so grounding well protects both your customers and your bill.

Decide your AI budget before you go live, not after the first invoice. Set a monthly cap as a hard ceiling and an alert threshold that warns you with room to react — both ship on every plan.

Reason about it in the platform's terms: you're charged about $0.99 only on a verified resolution, and $0 for handoffs, silence, and low-confidence answers. Start conservative, watch real volume for a few weeks, then raise the ceiling deliberately as you trust the meter.

A good first automation because it's high-volume and rule-bound. Use the no-code workflow builder to define the trigger (a refund request), the conditions (eligibility against your policy), and the action (process, escalate, or ask for more).

Keep the human in the loop where money moves: let the AI gather and validate, but route anything outside policy to a person via routing and SLAs. Dry-run the workflow before publishing, and lean on the agent trace to see exactly what it did on each request.

Resist vanity metrics. "Messages sent" or a generous "resolution" count tells you nothing about whether customers were helped — and Callario deliberately doesn't bill on those.

Use the reporting and CSAT views together with the resolution receipts: read what the AI actually resolved, check the grounding behind it, and watch CSAT on AI-handled conversations. The efficacy view is built to keep you honest rather than flatter the bot. Treat handoff rate as a signal to improve, not a number to suppress.

Bring agents in gradually. Use role-aware access and user lifecycle controls to invite the right people with the right permissions, and start the AI in a supervised mode where humans review its work.

Make the copilot the on-ramp: let agents see AI suggestions with citations before you trust the autonomous agent on a channel end to end. Expand the AI's autonomy as the team builds confidence in the receipts — a steady, evidence-led rollout beats a big-bang launch every time.