Scoring 100% of sales calls at an agricultural machinery distributor

About this case study. The customer’s name is withheld by agreement — the deployment is real and running today. Every number below comes straight from Aelo’s production ledger (the same billing-grade records we invoice from), not from a survey or an estimate. We publish no customer quotes until the customer signs them off.

The company

A wholesale distributor of agricultural machinery and spare parts. Two call-heavy desks: sales (new equipment, trade-ins, seasonal campaigns) and parts & service (orders, availability, delivery status). The team lives in Bitrix24 — deals, calls, and tasks all happen there.

Call volume is the defining constraint: tens of thousands of conversations a month. No management team can listen to that. Before Aelo, nobody did — beyond occasional spot-checks.

Before: spot-checks and gut feel

The QA method was the one most sales teams still use:

• A manager opens a few recordings a week — usually after something already went wrong.
• Coaching is based on the handful of calls the manager happened to hear.
• Disputes (“the client was told X”) get settled by memory, or by digging through recordings for an hour.
• What the other 95%+ of conversations contained — nobody knew.

Manual listening covered a small fraction of the stream, selected mostly by accident. Not because the team didn’t care — because the volume made anything else physically impossible.

The rollout

• Aelo connected to Bitrix24 — the native app, installed against the company’s own portal. Calls flow in automatically; verdicts come back to the same CRM cards the team already works in.
• A scoring profile in the company’s own language — greeting, needs discovery, product knowledge, objection handling, next-step agreement and more: 30+ parameters in 8 groups, tuned to how a distributor actually sells, not a generic call-center template.
• Every score is auditable — each parameter points to the exact line in the transcript (with a timestamp) that earned or lost the points. A manager can open the quote and re-listen to that second of the call.
• Managers changed nothing in how they dial. Aelo runs in the background.

Numbers from the production ledger

Figures below are read directly from Aelo’s production database for this customer (launch — March 2026; snapshot — July 5, 2026):

The honest caveat we’d want to see in anyone else’s case study: this data confirms scale, coverage, and cost — it can’t by itself prove revenue causality (“Aelo added X% to sales”). We don’t claim what we can’t measure.

What changed for the team

Coverage stopped being a sample. “Which calls did we check this week?” is no longer a question — the stream is scored continuously, on identical criteria for every rep.

Coaching switched from anecdotes to patterns. Instead of “I listened to two of your calls,” managers see where each rep systematically loses points — and coach on that, with transcript quotes as evidence.

Disputes resolve in seconds. “What exactly was the client told?” is answered by opening the call card: transcript, score, and the flagged line — not by an hour of re-listening.

The economics stayed boring — by design. Aelo charges per analyzed minute, and this deployment runs at wholesale-scale volume on a predictable monthly budget with spend caps in the product. Cost per call is a known number, visible in the ledger, every day.

Why this matters if you run a call-heavy team

This deployment is the core Aelo claim, tested at scale: if your sales live on calls, quality control can be a property of the system, not a heroic effort of one manager. A team doing 200 calls a month and a team doing 20,000 get the same thing — every call scored, every score traceable to a quote.

• See how scoring works: Product
• What it costs at your volume: Pricing — starts free, 30 minutes a month, no card
• Already on Bitrix24? Aelo vs built-in Bitrix24 AI